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NeurIPS 2024 WildfireGPT: Tailored Large Language Model for Wildfire Analysis (Papers Track)
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Abstract: Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-specific information, particularly in areas requiring specialized knowledge, such as wildfire details within the broader context of climate change. For decision-makers focused on wildfire resilience and adaptation, it is crucial to obtain responses that are not only precise but also domain-specific. To that end, we developed WildfireGPT, a prototype LLM agent designed to transform user queries into actionable insights on wildfire risks. We enrich WildfireGPT by providing additional context, such as climate projections and scientific literature, to ensure its information is current, relevant, and scientifically accurate. This enables WildfireGPT to be an effective tool for delivering detailed, user-specific insights on wildfire risks to support a diverse set of end users, including but not limited to researchers and engineers, for making positive impact and decision making.

Authors: Yangxinyu Xie (University of Pennsylvania); Bowen Jiang (University of Pennsylvania); Tanwi Mallick (Argonne National Laboratory); Joshua Bergerson (Argonne National Laboratory); John Hutchison (Argonne National Laboratory); Duane Verner (Argonne National Laboratory); Jordan Branham (Argonne National Laboratory); M. Ross Alexander (Argonne National Laboratory); Robert Ross (Argonne National Laboratory); Yan Feng (Argonne National Laboratory); Leslie-Anne Levy (Argonne National Laboratory); Weijie Su (University of Pennsylvania); Camillo Jose Taylor (University of Pennsylvania)
NeurIPS 2024 Clustering-Based Framework for Assessing Transportation Resilience to Flood Events (Papers Track)
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Abstract: Flooding presents a significant threat to critical infrastructures (CIs), particularly in Spain, which is frequently cited as the most flood-affected country in Europe. The transportation sector, a crucial CI, is particularly susceptible to such events. It is imperative to monitor the corresponding resilience to improve disaster management efforts. With the advancements in data science and machine learning, various approaches have been developed in this area; however, researchers encounter challenges such as data inconsistency and reliability. This paper presents a resilience assessment framework that utilises machine learning and open data to evaluate the impact of floods on Spain's transportation network. The analysis aims to facilitate well-informed decision-making by stakeholders and government entities, thereby enhancing disaster preparedness and response.

Authors: Matheus Pedra (TECNUN); Leire Labaka (TECNUN); Josune Hernantes (TECNUN)
NeurIPS 2024 Flood Prediction in Kenya - Leveraging Pre-Trained Models to Generate More Validation Data in Sparse Observation Settings (Proposals Track)
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Abstract: Kenya has lacked a national flood risk management framework and also has sparse flood observation data, which makes developing deep learning flood prediction models on a national scale challenging. Flood prediction models are critical to operationalize Early Warning Systems (EWS). We propose two different models to feed into an EWS. The first model will leverage statistical machine learning approaches to predict flood or no flood events on a 0.25 x 0.25 degree scale (approximately 30 km x 30 km in Kenya) using ERA5 features as well as land cover and Digital Terrain Model (DTM) data. This first model will also be used to create a baseline prediction benchmark across the entire country of Kenya. The second model will leverage pre-trained remote sensing based models to generate segmented flood or no flood data on a fine spatial scale. This will increase the number of validation points by a factor of 1000, which opens the door to deep learning approaches to predict flood or no-flood events on a 30 meter x 30 meter spatial scale. We hope that this approach of leveraging pre-trained models to generate fine scale validation data can eventually be used widely in other extreme climate event forecasting scenarios given the scarcity of historical extreme climate events compared to normal weather events.

Authors: Alim Karimi (Self / Purdue University); David Quispe (University of Toronto); Hammed Akande (Concordia University); Nicole Mong'are (Athi Water Works Development Agency); Valerie Brosnan (Mitga Solutions); Asbina Baral (Ministry of Education, Science and Technology)
ICLR 2024 Semi-Supervised Domain Adaptation for Wildfire Detection (Papers Track)
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Abstract: Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompasses 30 times more diverse labeled scenes than the current largest benchmark wildfire dataset, HPWREN, and introduces a new labeling policy for wildfire detection. Inspired by coordconv., we propose a robust baseline, Location-Aware Object Detection for Semi-Supervised Domain Adaptation (LADA), utilizing a teacher-student based self-supervised Domain Adaptation framework capable of extracting translational variance features characteristic of wildfires. Our framework significantly outperforms the existing baseline by a notable margin of 3.8\%p in mean Average Precision on the HPWREN wildfire dataset.

Authors: Joo Young Jang (Alchera); Youngseo Cha (Alchera); Jisu Kim (Alchera); SooHyung Lee (Alchera); Geonu Lee (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera); Nojun Kwak (Seoul National University)
NeurIPS 2023 Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean (Papers Track)
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Abstract: Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprint and roof classification maps. By enhancing local capacity in government agencies, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.

Authors: Isabelle Tingzon (The World Bank); Nuala Margaret Cowan (The World Bank); Pierre Chrzanowski (The World Bank)
NeurIPS 2023 Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments (Papers Track)
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Abstract: Climate change has resulted in a year over year increase in adverse weather and weather conditions which contribute to increasingly severe fire seasons. Without effective mitigation, these fires pose a threat to life, property, ecology, cultural heritage, and critical infrastructure. To better prepare for and react to the increasing threat of wildfires, more accurate fire modelers and mitigation responses are necessary. In this paper, we introduce SimFire, a versatile wildland fire projection simulator designed to generate realistic wildfire scenarios, and SimHarness, a modular agent-based machine learning wrapper capable of automatically generating land management strategies within SimFire to reduce the overall damage to the area. Together, this publicly available system allows researchers and practitioners the ability to emulate and assess the effectiveness of firefighter interventions and formulate strategic plans that prioritize value preservation and resource allocation optimization. The repositories are available for download at https://github.com/mitrefireline.

Authors: Alexander Tapley (The MITRE Corporation); savanna o smith (MITRE); Tim Welsh (The MITRE Corporation); Aidan Fennelly (The MITRE Corporation); Dhanuj M Gandikota (The MITRE Corporation); Marissa Dotter (MITRE Corporation); Michael Doyle (The MITRE Corporation); Michael Threet (MITRE)
NeurIPS 2023 Prototype-oriented Unsupervised Change Detection for Disaster Management (Papers Track)
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Abstract: Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the remote sensing community has explored change detection methods. These methods are primarily categorized into supervised techniques, which yield precise results but come with high labeling costs, and unsupervised techniques, which eliminate the need for labeling but involve intricate hyperparameter tuning. To address these challenges, we propose a novel unsupervised change detection method named Prototype-oriented Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures changes by comparing features from pre-event, post-event, and prototype-oriented change synthesis images via a foundational model, and refines results using the Segment Anything Model (SAM). Although PUCD is an unsupervised change detection, it does not require complex hyperparameter tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the disaster dataset and it achieves state-of-the-art performance compared to other methods on the LEVIR-Extension dataset.

Authors: YoungTack Oh (SI Analytics); Minseok Seo (si-analytics); Doyi Kim (SI Analytics); Junghoon Seo (SI Analytics)
NeurIPS 2023 FireSight: Short-Term Fire Hazard Prediction Based on Active Fire Remote Sensing Data (Papers Track)
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Abstract: Wildfires are becoming unpredictable natural hazards in many regions due to climate change. However, existing state-of-the-art wildfire forecasting tools, such as the Fire Weather Index (FWI), rely solely on meteorological input parameters and have limited ability to model the increasingly dynamic nature of wildfires. In response to the escalating threat, our work addresses this shortcoming in short-term fire hazard prediction. First, we present a comprehensive and high fidelity remotely sensed active fire dataset fused from over 20 satellites. Second, we develop region-specific ML-based 3-7 day wildfire hazard prediction models for hazard South America, Australia, and Southern Europe. The different models cover pixel-wise, spatial and spatio-temporal architectures, and utilize weather, fuel and location data. We evaluate the models using time-based cross-validation and can show superior performance with a PR-AUC score up to 44 times higher compared to the baseline FWI model. Using explainable AI methods, we show that these data-driven models are also capable of learning meaningful physical patterns and inferring region-specific wildfire drivers.

Authors: Julia Gottfriedsen (OroraTech GmbH); Johanna Strebl (OroraTech GmbH); Max Berrendorf (Ludwig-Maximilians-Universität München); Martin Langer (OroraTech GmbH); Volker Tresp (Ludwig-Maximilians-Universität München)
NeurIPS 2023 The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation (Papers Track)
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Abstract: Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By pre-releasing water via these structures in advance of extreme weather events, water levels are sufficiently lowered to prevent floods. In this work, we propose FIDLAR, a Forecast Informed Deep Learning Architecture, achieving flood management in watersheds with hydraulic structures in an optimal manner by balancing out flood mitigation and unnecessary wastage of water via pre-releases. We perform experiments with FIDLAR using data from the South Florida Water Management District, which manages a coastal area that is highly prone to frequent storms and floods. Results show that FIDLAR performs better than the current state-of-the-art with several orders of magnitude speedup and with provably better pre-release schedules. The dramatic speedups make it possible for FIDLAR to be used for real-time flood management. The main contribution of this paper is the effective use of tools for model explainability, allowing us to understand the contribution of the various environmental factors towards its decisions.

Authors: Jimeng Shi (Florida International University); Vitalii Stebliankin (FIU); Giri Narasimhan (Florida International University)
NeurIPS 2023 Improving Flood Insights: Diffusion-based SAR to EO Image Translation (Papers Track)
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Abstract: Driven by the climate crisis, the frequency and intensity of flood events are on the rise. Electro-optical (EO) satellite imagery is commonly used for rapid disaster response. However, its utility in flood situations is limited by cloud cover and during nighttime. An alternative method for flood detection involves using Synthetic Aperture Radar (SAR) data. Despite SAR's advantages over EO in these situations, it has a significant drawback: human analysts often struggle to interpret SAR data. This paper proposes a novel framework, Diffusion-based SAR-to-EO Image Translation (DSE). The DSE framework converts SAR images into EO-like imagery, thereby enhancing their interpretability for human analysis. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework provides enhanced visual information and improves performance in all flood segmentation tests.

Authors: Minseok Seo (si-analytics); YoungTack Oh (SI Analytics); Doyi Kim (SI Analytics); Dongmin Kang (SIA); Yeji Choi (SI Analytics)
NeurIPS 2023 A Wildfire Vulnerability Index for Businesses Using Machine Learning Approaches (Papers Track)
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Abstract: Climate change and housing growth in ecologically vulnerable areas are increasing the frequency and intensity of wildfire events. These events can have profound impact on communities and the economy, but the financial and operational impacts of wildfire on businesses have not been evaluated extensively yet. This paper presents a Wildfire Vulnerability Index (WVI) that measures the risk of a business failing in the 12 months following a wildfire event. An XGBoost algorithm champion model is compared to two challenger models: 1) a model that extends the champion model by incorporating building and property characteristics and 2) a model that utilizes a neural network approach. Results show that while all models perform well in predicting business failure risk post-disaster event, the model that uses building and property characteristics performs best across all performance metrics. As the natural environment shifts under climate change and more businesses are exposed to the consequences of wildfire, the WVI can help emergency managers allocate disaster aid to businesses at the highest risk of failing and can also provide valuable risk insights for portfolio managers and loan processors.

Authors: Andrew Byrnes (Dun and Bradstreet); Lisa Stites (Dun and Bradstreet)
NeurIPS 2023 GraphTransformers for Geospatial Forecasting of Hurricane Trajectories (Papers Track)
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Abstract: In this paper we introduce a novel framework for trajectory prediction of geospatial sequences using GraphTransformers. When viewed across several sequences, we observed that a graph structure automatically emerges between different geospatial points that is often not taken into account for such sequence modeling tasks. We show that by leveraging this graph structure explicitly, geospatial trajectory prediction can be significantly improved. Our GraphTransformer approach improves upon state-of-the-art Transformer based baseline significantly on HURDAT, a dataset where we are interested in predicting the trajectory of a hurricane on a 6 hourly basis. This helps inform evacuation efforts by narrowing down target location by 10 to 20 kilometers along both the north-south and east-west directions.

Authors: Satyaki Chakraborty (Carnegie Mellon University); Pallavi Banerjee (University of Washington)
ICLR 2023 Global Flood Prediction: a Multimodal Machine Learning Approach (Papers Track)
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Abstract: Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel mul- timodal machine learning approach for multi-year global flood risk prediction, combining geographical information and historical natural disaster dataset. Our multimodal framework employs state-of-the-art processing techniques to extract embeddings from each data modality, including text-based geographical data and tabular-based time-series data. Experiments demonstrate that a multimodal ap- proach, that is combining text and statistical data, outperforms a single-modality approach. Our most advanced architecture, employing embeddings extracted us- ing transfer learning upon DistilBert model, achieves 75%-77% ROCAUC score in predicting the next 1-5 year flooding event in historically flooded locations. This work demonstrates the potentials of using machine learning for long-term planning in natural disaster management

Authors: Cynthia Zeng (MIT); Dimitris Bertsimas (MIT)
ICLR 2023 Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions (Papers Track)
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Abstract: Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance. Many global regions, however, have limited streamflow observations to guide evidence-based management strategies. In this paper, we propose an attention-based domain adaptation streamflow forecaster for data-sparse regions. Our approach leverages the hydrological characteristics of a data-rich source domain to induce effective 24hr lead-time streamflow prediction in a data-constrained target domain. Specifically, we employ a deep-learning framework leveraging domain adaptation techniques to simultaneously train streamflow predictions and discern between both domains using an adversarial method. Experiments against baseline cross-domain forecasting models show improved performance for 24hr lead-time streamflow forecasting.

Authors: Roland R Oruche (University of Missouri-Columbia); Fearghal O'Donncha (IBM Research)
ICLR 2023 Bayesian Inference of Severe Hail in Australia (Papers Track)
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Abstract: Severe hailstorms are responsible for some of the most costly insured weather events in Australia and can cause significant damage to homes, businesses, and agriculture. However their response to climate change remains uncertain, in large part due to the challenges of observing severe hailstorms. We propose a novel Bayesian approach which explicitly models known biases and uncertainties of current hail observations to produce more realistic estimates of severe hail risk from existing observations. Training this model on data from south-east Queensland, Australia, suggests that previous analyses of severe hail that did not account for this uncertainty may produce poorly calibrated risk estimates. Preliminary evaluation on withheld data confirms that our model produces well-calibrated probabilities and is applicable out of sample. Whilst developed for hail, we highlight also the generality of our model and its potential applications to other severe weather phenomena and areas of climate change adaptation and mitigation.

Authors: Isabelle C Greco (University of New South Wales); Steven Sherwood (University of New South Wales); Timothy Raupach (University of New South Wales); Gab Abramowitz (University of New South Wales)
ICLR 2023 Artificial Intelligence in Tropical Cyclone Forecasting (Proposals Track)
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Abstract: Tropical cyclones (TC) in Bangladesh and other developing nations harm property and human lives. Theoretically, artificial intelligence (AI) can anticipate TC using tracking, intensity, and cyclone aftereffect phenomena. Although AI has a significant impact on predicting, poorer nations have struggled to adjust to its real-world applications. The interpretability of such a solution from an AI-based solution is the main factor in that situation, together with the infrastructure. Explainable AI has been extensively employed in the medical field because the outcome is so important. We believe that using explainable AI in TC forecasting is equally important as one large forecast can cause the thought of life loss. Additionally, it will improve the long-term viability of the AI-based weather forecasting system. To be more specific, we want to employ explainable AI in every way feasible in this study to address the problems of TC forecasting, intensity estimate, and tracking. We'll do this by using the graph neural network. The adoption of AI-based solutions in underdeveloped nations will be aided by this solution, which will boost their acceptance. With this effort, we also hope to tackle the challenge of sustainable AI in order to tackle the issue of climate change on a larger scale. However, Cyclone forecasting might be transformed by sustainable AI, guaranteeing precise and early predictions to lessen the effects of these deadly storms. The examination of vast volumes of meteorological data to increase forecasting accuracy is made possible by the combination of AI algorithms and cutting-edge technologies like machine learning and big data analytics. Improved accuracy is one of the main advantages of sustainable AI for cyclone prediction. To provide more precise forecasts, AI systems can evaluate a lot of meteorological data, including satellite imagery and ocean temperature readings.

Authors: Dr. Nusrat Sharmin (Military Institute of Science and Technology); Professor Dr. Md. Mahbubur Rahman Rahman (Military Institute of Science and Technology (MIST)); Sabbir Rahman (Military Institute of Science and Technology); Mokhlesur Rahman (Military Institute of Science and Technology)
NeurIPS 2022 Levee protected area detection for improved flood risk assessment in global hydrology models (Papers Track)
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Abstract: Precise flood risk assessment is needed to reduce human societies vulnerability as climate change increases hazard risk and exposure related to floods. Levees are built to protect people and goods from flood, which alters river hydrology, but are still not accounted for by global hydrological model. Detecting and integrating levee structures to global hydrological simulations is thus expected to enable more precise flood simulation and risk assessment, with important consequences for flood risk mitigation. In this work, we propose a new formulation to the problem of identifying levee structures: instead of detecting levees themselves, we focus on segmenting the region of the floodplain they protect. This formulation allows to better identify protected areas, to leverage the structure of hydrological data, and to simplify the integration of levee information to global hydrological models.

Authors: Masato Ikegawa (Kobe University); Tristan E.M Hascoet (Kobe University); Victor Pellet (Observatoire de Paris); Xudong Zhou (The University of Tokyo); Tetsuya Takiguchi (Kobe University); Dai Yamazaki (The University of Tokyo)
NeurIPS 2022 Deep Hydrology: Hourly, Gap-Free Flood Maps Through Joint Satellite and Hydrologic Modelling (Papers Track)
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Abstract: Climate change-driven weather disasters are rapidly increasing in both frequency and magnitude. Floods are the most damaging of these disasters, with approximately 1.46 billion people exposed to inundation depths of over 0.15m, a significant life and livelihood risk. Accurate knowledge of flood-extent for ongoing and historical events facilitates climate adaptation in flood-prone communities by enabling near real-time disaster monitoring to support planning, response, and relief during these extreme events. Satellite observations can be used to derive flood-extent maps directly; however, these observations are impeded by cloud and canopy cover, and can be very infrequent and hence miss the flood completely. In contrast, physically-based inundation models can produce spatially complete event maps but suffer from high uncertainty if not frequently calibrated with expensive land and infrastructure surveys. In this study, we propose a deep learning approach to reproduce satellite-observed fractional flood-extent maps given dynamic state variables from hydrologic models, fusing information contained within the states with direct observations from satellites. Our model has an hourly temporal resolution, contains no cloud-gaps, and generalizes to watersheds across the continental United States with a 6% error on held-out areas that never flooded before. We further demonstrate through a case study in Houston, Texas that our model can distinguish tropical cyclones that caused flooding from those that did not within two days of landfall, thereby providing a reliable source for flood-extent maps that can be used by disaster monitoring services.

Authors: Tanya Nair (Cloud To Street); Veda Sunkara (Cloud to Street); Jonathan Frame (Cloud to Street); Philip Popien (Cloud to Street); Subit Chakrabarti (Cloud To Street)
NeurIPS 2022 FIRO: A Deep-neural Network for Wildfire Forecast with Interpretable Hidden States (Papers Track)
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Abstract: Several wildfire danger systems have emerged from decades of research. One such system is the National Fire-Danger Rating System (NFDRS), which is used widely across the United States and is a key predictor in the Global ECMWF Fire Forecasting (GEFF) model. The NFDRS is composed of over 100 equations relating wildfire risk to weather conditions, climate and land cover characteristics, and fuel. These equations and the corresponding 130+ parameters were developed via field and lab experiments. These parameters, which are fixed in the standard NFDRS and GEFF implementations, may not be the most appropriate for a climate-changing world. In order to adjust the NFDRS parameters to current climate conditions and specific geographical locations, we recast NFDRS in PyTorch to create a new deep learning-based Fire Index Risk Optimizer (FIRO). FIRO predicts the ignition component, or the probability a wildfire would require suppression in the presence of a firebrand, and calibrates the uncertain parameters for a specific region and climate conditions by training on observed fires. Given the rare occurrence of wildfires, we employed the extremal dependency index (EDI) as the loss function. Using ERA5 reanalysis and MODIS burned area data, we trained FIRO models for California, Texas, Italy, and Madagascar. Across these four geographies, the average EDI improvement was 175% above the standard NFDRS implementation

Authors: Eduardo R Rodrigues (MSR); Campbell D Watson (IBM Reserch); Bianca Zadrozny (IBM Research); Gabrielle Nyirjesy (Columbia University)
NeurIPS 2022 Cross Modal Distillation for Flood Extent Mapping (Papers Track)
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Abstract: The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection module of an operational early flood warning system. Our method exploits an unlabelled dataset of paired multi-spectral and Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a purely supervised learning method. Past attempts have used such unlabelled data by creating weak labels out of them, but end up learning the label mistakes in those weak labels. Motivated by knowledge distillation and semi supervised learning, we explore the use of a teacher to train a student with the help of a small hand labeled dataset and a large unlabelled dataset. Unlike the conventional self distillation setup, we propose a cross modal distillation framework that transfers supervision from a teacher trained on richer modality (multi-spectral images) to a student model trained on SAR imagery. The trained models are then tested on the Sen1Floods11 dataset. Our model outperforms the Sen1Floods11 SAR baselines by an absolute margin of 4.15% pixel wise Intersection-over-Union (IoU) on the test split.

Authors: Shubhika Garg (Google); Ben Feinstein (Google); Shahar Timnat (Google); Vishal V Batchu (Google); gideon dror (The Academic College of Tel-Aviv-Yaffo); Adi Gerzi Rosenthal (Google); Varun Gulshan (Google Research)
NeurIPS 2022 Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes (Papers Track)
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Abstract: With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent from weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslide events obtained from several Sentinel-1 satellite passes before and after a landslide triggering event together with segmentation maps of the landslides. From this dataset, using the Hokkaido, Japan datacube, we study the feasibility of SAR-based landslide detection with supervised deep learning (DL). Our results demonstrate that DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7. We find that additional satellite visits enhance detection performance, but that early detection is possible when SAR data is combined with terrain information from a digital elevation model. This can be especially useful for time-critical emergency interventions.

Authors: Vanessa Boehm (UC Berkeley); Wei Ji Leong (The Ohio State University); Ragini Bal Mahesh (German Aerospace Center DLR); Ioannis Prapas (National Observatory of Athens); Siddha Ganju (Nvidia); Freddie Kalaitzis (University of Oxford); Edoardo Nemni (United Nations Satellite Centre (UNOSAT)); Raul Ramos-Pollan (Universidad de Antioquia)
NeurIPS 2022 Deep Learning for Global Wildfire Forecasting (Papers Track)
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Abstract: Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.

Authors: Ioannis Prapas (National Observatory of Athens); Akanksha Ahuja (NOA); Spyros Kondylatos (National Observatory of Athens); Ilektra Karasante (National Observatory of Athens); Lazaro Alonso (Max Planck Institute for Biogeochemistry); Eleanna Panagiotou (Harokopio University of Athens); Charalampos Davalas (Harokopio University of Athens); Dimitrios Michail (Harokopio University of Athens); Nuno Carvalhais (Max Planck Institute for Biogeochemistry); Ioannis Papoutsis (National Observatory of Athens)
NeurIPS 2022 Image-based Early Detection System for Wildfires (Papers Track)
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Abstract: Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person.

Authors: Omkar Ranadive (Alchera X); Jisu Kim (Alchera); Serin Lee (Alchera X); Youngseo Cha (Alchera); Heechan Park (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera)
NeurIPS 2022 Multimodal Wildland Fire Smoke Detection (Papers Track)
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Abstract: Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. Our results show that incorporating multimodal data in SmokeyNet improves performance in terms of both F1 and time-to-detection over the baseline with a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Authors: Mai Nguyen (University of California San Diego); Shreyas Anantha Ramaprasad (University of California San Diego); Jaspreet Kaur Bhamra (University of California San Diego); Siddhant Baldota (University of California San Diego); Garrison Cottrell (UC San Diego)
NeurIPS 2022 Accessible Large-Scale Plant Pathology Recognition (Papers Track)
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Abstract: Plant diseases are costly and threaten agricultural production and food security worldwide. Climate change is increasing the frequency and severity of plant diseases and pests. Therefore, detection and early remediation can have a significant impact, especially in developing countries. However, AI solutions are yet far from being in production. The current process for plant disease diagnostic consists of manual identification and scoring by humans, which is time-consuming, low-supply, and expensive. Although computer vision models have shown promise for efficient and automated plant disease identification, there are limitations for real-world applications: a notable variation in visual symptoms of a single disease, different light and weather conditions, and the complexity of the models. In this work, we study the performance of efficient classification models and training "tricks" to solve this problem. Our analysis represents a plausible solution for these ecological disasters and might help to assist producers worldwide. More information available at: https://github.com/mv-lab/mlplants

Authors: Marcos V. Conde (University of Würzburg); Dmitry Gordeev (H2O.ai)
NeurIPS 2022 Continual VQA for Disaster Response Systems (Papers Track)
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Abstract: Visual Question Answering (VQA) is a multi-modal task that involves answering questions from an input image, semantically understanding the contents of the image and answering it in natural language. Using VQA for disaster management is an important line of research due to the scope of problems that are answered by the VQA system. However, the main challenge is the delay caused by the generation of labels in the assessment of the affected areas. To tackle this, we deployed pre-trained CLIP model, which is trained on visual-image pairs. however, we empirically see that the model has poor zero-shot performance. Thus, we instead use pre-trained embeddings of text and image from this model for our supervised training and surpass previous state-of-the-art results on the FloodNet dataset. We expand this to a continual setting, which is a more real-life scenario. We tackle the problem of catastrophic forgetting using various experience replay methods.

Authors: Aditya Kane (Pune Institute of Computer Technology); V Manushree (Manipal Institute Of Technology); Sahil S Khose (Georgia Institute of Technology)
NeurIPS 2022 Identification of medical devices using machine learning on distribution feeder data for informing power outage response (Proposals Track)
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Abstract: Power outages caused by extreme weather events due to climate change have doubled in the United States in the last two decades. Outages pose severe health risks to over 4.4 million individuals dependent on in-home medical devices. Data on the number of such individuals residing in a given area is limited. This study proposes a load disaggregation model to predict the number of medical devices behind an electric distribution feeder. This data can be used to inform planning and response to power outages and other emergencies. The proposed solution serves as a measure for climate change adaptation.

Authors: Paraskevi Kourtza (University of Edinburgh); Maitreyee Marathe (University of Wisconsin-Madison); Anuj Shetty (Stanford University); Diego Kiedanski (Yale University)
NeurIPS 2022 Detecting Floods from Cloudy Scenes: A Fusion Approach Using Sentinel-1 and Sentinel-2 Imagery (Proposals Track)
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Abstract: As the result of climate change, extreme flood events are becoming more frequent. To better respond to such disasters, and to test and calibrate flood models, we need accurate real-world data on flooding extent. Detection of floods from remote sensed imagery suffers from a widespread problem: clouds block flood scenes in images, leading to degraded and fragmented flood datasets. To address this challenge, we propose a workflow based on U-Net, and a dataset that detects flood in cloud-prone areas by fusing information from the Sentinel-1 and Sentinel-2 satellites. The expected result will be a reliable and detailed catalogue of flood extents and how they change through time, allowing us to better understand flooding in different morphological settings and climates.

Authors: Qiuyang Chen (University of Edinburgh); Xenofon Karagiannis (Earth-i Ltd.); Simon M. Mudd (University of Edinburgh)
NeurIPS 2022 Urban Heat Island Detection and Causal Inference Using Convolutional Neural Networks (Proposals Track)
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Abstract: Compared to rural areas, urban areas experience higher temperatures for longer periods of time because of the urban heat island (UHI) effect. This increased heat stress leads to greater mortality, increased energy demand, regional changes to precipitation patterns, and increased air pollution. Urban developers can minimize the UHI effect by incorporating features that promote air flow and heat dispersion (e.g., increasing green space). However, understanding which urban features to implement is complex, as local meteorology strongly dictates how the environment responds to changes in urban form. In this proposal we describe a methodology for estimating the causal relationship between changes in urban form and changes in the UHI effect. Changes in urban form and temperature changes are measured using convolutional neural networks, and a causal inference matching approach is proposed to estimate causal relationships. The success of this methodology will enable urban developers to implement city-specific interventions to mitigate the warming planet's impact on cities.

Authors: Zachary D Calhoun (Duke University); Ziyang Jiang (Duke University); Mike Bergin (Duke University); David Carlson (Duke University)
NeurIPS 2022 Forecasting Global Drought Severity and Duration Using Deep Learning (Proposals Track)
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Abstract: Drought detection and prediction is challenging due to the slow onset of the event and varying degrees of dependence on numerous physical and socio-economic factors that differentiate droughts from other natural disasters. In this work, we propose DeepXD (Deep learning for Droughts), a deep learning model with 26 physics-informed input features for SPI (Standardised Precipitation Index) forecasting to identify and classify droughts using monthly oceanic indices, global meteorological and vegetation data, location (latitude, longitude) and land cover for the years 1982 to 2018. In our work, we propose extracting features by considering the atmosphere and land moisture and energy budgets and forecasting global droughts on a seasonal and an annual scale at 1, 3, 6, 9, 12 and 24 months lead times. SPI helps us to identify the severity and the duration of the drought to classify them as meteorological, agricultural and hydrological.

Authors: Akanksha Ahuja (NOA); Xin Rong Chua (Centre for Climate Research Singapore)
NeurIPS 2022 Disaster Risk Monitoring Using Satellite Imagery (Tutorials Track)
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Abstract: Natural disasters such as flood, wildfire, drought, and severe storms wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems, and economies. Unfortunately, flooding events are on the rise due to climate change and sea level rise. The ability to detect and quantify them can help us minimize their adverse impacts on the economy and human lives. Using satellites to study flood is advantageous since physical access to flooded areas is limited and deploying instruments in potential flood zones can be dangerous. We are proposing a hands-on tutorial to highlight the use of satellite imagery and computer vision to study natural disasters. Specifically, we aim to demonstrate the development and deployment of a flood detection model using Sentinel-1 satellite data. The tutorial will cover relevant fundamental concepts as well as the full development workflow of a deep learning-based application. We will include important considerations such as common pitfalls, data scarcity, augmentation, transfer learning, fine-tuning, and details of each step in the workflow. Importantly, the tutorial will also include a case study on how the application was used by authorities in response to a flood event. We believe this tutorial will enable machine learning practitioners of all levels to develop new technologies that tackle the risks posed by climate change. We expect to deliver the below learning outcomes: • Develop various deep learning-based computer vision solutions using hardware-accelerated open-source tools that are optimized for real-time deployment • Create an optimized pipeline for the machine learning development workflow • Understand different performance metrics for model evaluation that are relevant for real world datasets and data imbalances • Understand the public sector’s efforts to support climate action initiatives and point out where the audience can contribute

Authors: Kevin Lee (NVIDIA); Siddha Ganju (NVIDIA); Edoardo Nemni (UNOSAT)
AAAI FSS 2022 Wildfire Forecasting with Satellite Images and Deep Generative Model
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Abstract: Wildfire prediction has been one of the most critical tasks that humanities want to thrive at. While it plays a vital role in protecting human life, it is also difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images into a video and used it to anticipate how the fire would behave in the future. However, creating video prediction models that account for the inherent uncertainty of the future is challenging. The bulk of published attempts are based on stochastic image-autoregressive recurrent networks, which raise various performance and application difficulties such as computational cost and limited efficiency on massive datasets. Another possibility is to use entirely latent temporal models that combine frame synthesis with temporal dynamics. However, due to design and training issues, no such model for stochastic video prediction has yet been proposed in the literature. This paper addresses these issues by introducing a novel stochastic temporal model whose dynamics are driven in a latent space. It naturally predicts video dynamics by allowing our lighter, more interpretable latent model to beat previous state-of-the-art approaches on the GOES-16 dataset. Results are compared using various benchmarking models.

Authors: Thai-Nam Hoang (University of Wisconsin - Madison), Sang Truong (Stanford University) and Chris Schmidt (University of Wisconsin - Madison)
AAAI FSS 2022 NADBenchmarks - a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters
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Abstract: Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning(ML) for this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing the datasets according to the disaster management cycle. We compile a list of existing benchmark datasets that have been introduced in the past five years. We propose a web platform where researchers can search for benchmark datasets in this domain, and develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions in for topics where they can contribute new benchmark datasets.

Authors: Adiba Proma (University of Rochester), Md Saiful Islam (University of Rochester), Stela Ciko (University of Rochester), Raiyan Abdul Baten (University of Rochester) and Ehsan Hoque (University of Rochester)
AAAI FSS 2022 Predicting Wildfire Risk Under Novel 21st-Century Climate Conditions
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Abstract: Wildfires are one of the most impactful hazards associated with climate change, and in a hotter, drier world, wildfires will be much more common than they have historically been. However, the exact severity and frequency of future wildfires are difficult to estimate, because climate change will create novel combinations of vegetation and fire weather outside what has been historically observed. This provides a challenge for AI-based approaches to long-term fire risk modeling, as much future fire risk is outside of the available feature space provided by the historical record. Here, we give an overview of this problem that is inherent to many climate change impacts and propose a restricted model form that makes monotonic and interpretable predictions in novel fire weather environments. We then show how our model outperforms other neural networks and logistic regression models when making predictions on unseen data from a decade into the future.

Authors: Matthew Cooper (Sust Global).
NeurIPS 2021 Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning (Papers Track)
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Abstract: Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Emerging Techniques in Computational Intelligence (ETCI) competition on Flood Detection tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, thereby progressively improving accuracy. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the provided high confidence hand-labeled data and, generated pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combine the generated labels with the previously available high confidence hand-labeled dataset. This assimilated dataset is used for the next round of training ensemble models. This cyclical process is repeated until the performance improvement plateaus. Additionally, we post process our results with Conditional Random Fields. Our approach sets a high score, and a new state-of-the-art on the Sentinel-1 dataset for the ETCI competition with 0.7654 IoU, an impressive improvement over the 0.60 IOU baseline. Our method, which we release with all the code including trained models, can also be used as an open science benchmark for the Sentinel-1 released dataset.

Authors: Siddha Ganju (Nvidia Corporation); Sayak Paul (Carted)
NeurIPS 2021 Predicting Discharge in Catchment Outlet Using Deep Learning: Case Study of the Ansongo-Niamey Basin (Papers Track)
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Abstract: Hydrological models are one of the key challenges in hydrology. Their goal is to understand, predict and manage water resources. Most of the hydrological models so far were either physical or conceptual models. But in the past two decades, fully data-driven (empirical) models started to emerge with the breakthroughs of novel deep learning methods in runoff prediction. These breakthroughs were mostly favored by the large volume, variety and velocity of water-related data. Long Short-Term Memory and Gated Recurrent Unit neural networks, particularly achieved the outstanding milestone of outperforming classic hydrological models in less than a decade. Moreover, they have the potential to change the way hydrological modeling is performed. In this study, precipitation, minimal and maximum temperature at the Ansongo-Niamey basin combined with the discharge at Ansongo and Kandadji were used to predict the discharge at Niamey using artificial neural networks. After data preprocessing and hyperparameter optimization, the deep learning models performed well with the LSTM and GRU respectively scoring a Nash-Sutcliffe Efficiency of 0.933 and 0.935. This performance matches those of well-known physically-based models used to simulate Niamey’s discharge and therefore demonstrates the efficiency of deep learning methods in a West African context, especially in Niamey which has been facing severe floods due to climate change.

Authors: Peniel J. Y. Adounkpe (WASCAL); Eric Alamou (Université d'Abomey-Calavi); Belko Diallo (WASCAL); Abdou Ali (AGRHYMET Regional Centre)
NeurIPS 2021 Hurricane Forecasting: A Novel Multimodal Machine Learning Framework (Papers Track)
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Abstract: This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-learning feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, and visual data such as reanalysis atmospheric images. We evaluate our models with current operational forecasts in North Atlantic (NA) and Eastern Pacific (EP) basins on 2016-2019 for 24-hour lead time, and show our models consistently outperform statistical-dynamical models and compete with the best dynamical models. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches.

Authors: Léonard Boussioux (MIT, CentraleSupélec); Cynthia Zeng (MIT); Dimitris Bertsimas (MIT); Théo J Guenais (Harvard University)
NeurIPS 2021 Improved Drought Forecasting Using Surrogate Quantile And Shape (SQUASH) Loss (Papers Track)
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Abstract: Droughts are amongst the most damaging natural hazard with cascading impacts across multiple sectors of the economy and society. Improved forecasting of drought conditions ahead of time can significantly improve strategic planning to mitigate the impacts and enhance resilience. Though significant progress in forecasting approaches has been made, the current approaches focus on the overall improvement of the forecast, with less attention on the extremeness of drought events. In this paper, we focus on improving the accuracy of forecasting extreme and severe drought events by introducing a novel loss function Surrogate Quantile and Shape loss (SQUASH) that combines weighted quantile loss and dynamic time-warping-based shape loss. We show the effectiveness of the proposed loss functions for imbalanced time-series drought forecasting tasks on two regions in India and the USA.

Authors: Devyani Lambhate Lambhate (Indian Institute of Science); Smit Marvaniya (IBM Research India); Jitendra Singh (IBM Research - India); David Gold (IBM)
NeurIPS 2021 Global ocean wind speed estimation with CyGNSSnet (Papers Track)
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Abstract: The CyGNSS (Cyclone Global Navigation Satellite System) satellite system measures GNSS signals reflected off the Earth's surface. A global ocean wind speed dataset is derived, which fills a gap in Earth observation data, will improve cyclone forecasting, and could be used to mitigate effects of climate change. We propose CyGNSSnet, a deep learning model for predicting wind speed from CyGNSS observables, and evaluate its potential for operational use. With CyGNSSnet, performance improves by 29\% over the current operational model. We further introduce a hierarchical model, that combines an extreme value classifier and a specialized CyGNSSnet and slightly improves predictions for high winds.

Authors: Caroline Arnold (German Climate Computing Center); Milad Asgarimehr (German Research Centre for Geosciences)
NeurIPS 2021 On the Generalization of Agricultural Drought Classification from Climate Data (Papers Track)
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Abstract: Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon soil moisture index (SMI) obtained from a hydrological model. This index is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with land use information from MODIS satellite observation, we compare different models with and without sequential inductive bias in classifying droughts based on SMI. We use PR-AUC as the evaluation measure to account for the class imbalance and obtain promising results despite a challenging time-based split. We further show in an ablation study that the models retain their predictive capabilities given input data of coarser resolutions, as frequently encountered in climate models.

Authors: Julia Gottfriedsen (1Deutsches Zentrum für Luft- und Raumfahrt (DLR), LMU); Max Berrendorf (Ludwig-Maximilians-Universität München); Pierre Gentine (Columbia University); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena); Katja Weigel (niversity of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany); Birgit Hassler (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Veronika Eyring (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany)
NeurIPS 2021 MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather (Papers Track)
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Abstract: We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours. By stacking a large input receptive field along the feature dimension and conditioning the model's forecaster with predictions from the physics-based High Resolution Rapid Refresh (HRRR) model, we are able to outperform optical flow and HRRR baselines by 20-25% on multiple metrics averaged over all lead times.

Authors: Sylwester Klocek (Microsoft Corporation); Haiyu Dong (Microsoft); Matthew Dixon (Microsoft Corporation); Panashe Kanengoni (Microsoft Corporation); Najeeb Kazmi (Microsoft); Pete Luferenko (Microsoft Corporation); Zhongjian Lv (Microsoft Corporation); Shikhar Sharma (); Jonathan Weyn (Microsoft); Siqi Xiang (Microsoft Corporation)
NeurIPS 2021 Being the Fire: A CNN-Based Reinforcement Learning Method to Learn How Fires Behave Beyond the Limits of Physics-Based Empirical Models (Papers Track)
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Abstract: Wildland fires pose an increasing threat in light of anthropogenic climate change. Fire-spread models play an underpinning role in many areas of research across this domain, from emergency evacuation to insurance analysis. We study paths towards advancing such models through deep reinforcement learning. Aggregating 21 fire perimeters from the Western United States in 2017, we construct 11-layer raster images representing the state of the fire area. A convolution neural network based agent is trained offline on one million sub-images to create a generalizable baseline for predicting the best action - burn or not burn - given the then-current state on a particular fire edge. A series of online, TD(0) Monte Carlo Q-Learning based improvements are made with final evaluation conducted on a subset of holdout fire perimeters. We examine the performance of the learned agent/model against the FARSITE fire-spread model. We also make available a novel data set and propose more informative evaluation metrics for future progress.

Authors: William L Ross (Stanford)
NeurIPS 2021 FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection (Papers Track)
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Abstract: The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high fire-risk days, a small fire ignition can rapidly grow and get out of control. Early detection of fire ignitions from initial smoke can assist response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly-available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatio-temporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems to reduce the time to wildfire response.

Authors: Anshuman Dewangan (University of California, San Diego); Mai Nguyen (University of California, San Diego); Garrison Cottrell (UC San Diego)
NeurIPS 2021 A data integration pipeline towards reliable monitoring of phytoplankton and early detection of harmful algal blooms (Papers Track)
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Abstract: Climate change is making oceans warmer and more acidic. Under these conditions phytoplankton can produce harmful algal blooms which cause rapid oxygen depletion and consequent death of marine plants and animals. Some species are even capable of releasing toxic substances endangering water quality and human health. Monitoring of phytoplankton and early detection of harmful algal blooms is essential for protection of marine flaura and fauna. Recent technological advances have enabled in-situ plankton image capture in real-time at low cost. However, available phytoplankton image databases have several limitations that prevent the practical usage of artificial intelligent models. We present a pipeline for integration of heterogeneous phytoplankton image datasets from around the world into a unified database that can ultimately serve as a benchmark dataset for phytoplankton research and therefore act as an important tool in building versatile machine learning models for climate adaptation planning. A machine learning model for early detection of harmful algal blooms is part of ongoing work.

Authors: Bruna Guterres (Universidade Federal do Rio Grande - FURG); Sara khalid (University of Oxford); Marcelo Pias (Federal University of Rio Grande); Silvia Botelho (Federal University of Rio Grande)
NeurIPS 2021 Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones (Papers Track)
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Abstract: Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite imagery) and model output (e.g., numerical weather prediction, statistical models) to produce forecasts every 6 hours. Within these time constraints, it can be challenging to draw insight from such data. While high-capacity machine learning methods are well suited for prediction problems with complex sequence data, extracting interpretable scientific information with such methods is difficult. Here we leverage powerful AI prediction algorithms and classical statistical inference to identify patterns in the evolution of TC convective structure leading up to the rapid intensification of a storm, hence providing forecasters and scientists with key insight into TC behavior.

Authors: Irwin H McNeely (Carnegie Mellon University); Galen Vincent (Carnegie Mellon University); Rafael Izbicki (UFSCar); Kimberly Wood (Mississippi State University); Ann B. Lee (Carnegie Mellon University)
NeurIPS 2021 High-resolution rainfall-runoff modeling using graph neural network (Papers Track)
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Abstract: Time-series modeling has shown great promise in recent studies using the latest deep learning algorithms such as LSTM (Long Short-Term Memory). These studies primarily focused on watershed-scale rainfall-runoff modeling or streamflow forecasting, but the majority of them only considered a single watershed as a unit. Although this simplification is very effective, it does not take into account spatial information, which could result in significant errors in large watersheds. Several studies investigated the use of GNN (Graph Neural Networks) for data integration by decomposing a large watershed into multiple sub-watersheds, but each sub-watershed is still treated as a whole, and the geoinformation contained within the watershed is not fully utilized. In this paper, we propose the GNRRM (Graph Neural Rainfall-Runoff Model), a novel deep learning model that makes full use of spatial information from high-resolution precipitation data, including flow direction and geographic information. When compared to baseline models, GNRRM has less over-fitting and significantly improves model performance. Our findings support the importance of hydrological data in deep learning-based rainfall-runoff modeling, and we encourage researchers to include more domain knowledge in their models.

Authors: Zhongrun Xiang (University of Iowa); Ibrahim Demir (The University of Iowa)
NeurIPS 2021 DEM Super-Resolution with EfficientNetV2 (Papers Track)
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Abstract: Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisition of high-quality data for many environmental disciplines is costly. Digital Elevation Model (DEM) datasets are such examples whereas their low-resolution versions are widely available, high-resolution ones are scarce. In an effort to rectify this problem, we propose and assess an EfficientNetV2 based model. The proposed model increases the spatial resolution of DEMs up to 16 times without additional information.

Authors: Bekir Z Demiray (University of Iowa); Muhammed A Sit (The University of Iowa); Ibrahim Demir (The University of Iowa)
NeurIPS 2021 PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities (Papers Track)
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Abstract: The machine learning community has recently had increased interest in the climate and disaster damage domain due to a marked increased occurrences of natural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However, not enough attention has been devoted to mitigating probable destruction from impending natural hazards. We explore this crucial space by predicting building-level damages on a before-the-fact basis that would allow state actors and non-governmental organizations to be best equipped with resource distribution to minimize or preempt losses. We introduce PreDisM that employs an ensemble of ResNets and fully connected layers over decision trees to capture image-level and meta-level information to accurately estimate weakness of man-made structures to disaster-occurrences. Our model performs well and is responsive to tuning across types of disasters and highlights the space of preemptive hazard damage modelling.

Authors: Vishal Anand (Columbia University); Yuki Miura (Columbia University)
NeurIPS 2021 Semi-Supervised Classification and Segmentation on High Resolution Aerial Images (Papers Track)
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Abstract: FloodNet is a high-resolution image dataset acquired by a small UAV platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a unique challenge of advancing the damage assessment process for post-disaster scenarios using unlabeled and limited labeled dataset. We propose a solution to address their classification and semantic segmentation challenge. We approach this problem by generating pseudo labels for both classification and segmentation during training and slowly incrementing the amount by which the pseudo label loss affects the final loss. Using this semi-supervised method of training helped us improve our baseline supervised loss by a huge margin for classification, allowing the model to generalize and perform better on the validation and test splits of the dataset. In this paper, we compare and contrast the various methods and models for image classification and semantic segmentation on the FloodNet dataset.

Authors: Sahil S Khose (Manipal Institute of Technology); Abhiraj Tiwari (Manipal Institute of Technology); Ankita Ghosh (Manipal Institute of Technology)
NeurIPS 2021 A Risk Model for Predicting Powerline-induced Wildfires in Distribution System (Proposals Track)
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Abstract: The power grid is one of the most common causes of wildfires that result in tremendous economic loss and significant life risk. In this study, we propose to use machine learning techniques to build a risk model for predicting powerline-induced wildfires in distribution system. We collect weather, vegetation, and infrastructure data for all feeders in Pacific Gas & Electricity territory. This study will contribute to a deeper understanding of powerline-induced wildfire prediction and provide valuable suggestions for wildfire mitigation planning.

Authors: Mengqi Yao (University of California Berkeley)
NeurIPS 2021 Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark (Proposals Track)
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Abstract: Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks. Such models, recently coined as foundation models, have been transformational to the field of natural language processing. While similar models have also been trained on large corpuses of images, they are not well suited for remote sensing data. To stimulate the development of foundation models for Earth monitoring, we propose to develop a new benchmark comprised of a variety of downstream tasks related to climate change. We believe that this can lead to substantial improvements in many existing applications and facilitate the development of new applications. This proposal is also a call for collaboration with the aim of developing a better evaluation process to mitigate potential downsides of foundation models for Earth monitoring.

Authors: Alexandre Lacoste (ServiceNow); Evan D Sherwin (Stanford University, Energy and Resources Engineering); Hannah R Kerner (University of Maryland); Hamed Alemohammad (Radiant Earth Foundation); Björn Lütjens (MIT); Jeremy A Irvin (Stanford); David Dao (ETH Zurich); Alex Chang (Service Now); Mehmet Gunturkun (Element Ai); Alexandre Drouin (ServiceNow); Pau Rodriguez (Element AI); David Vazquez (ServiceNow)
ICML 2021 Semantic Segmentation on Unbalanced Remote Sensing Classes for Active Fire (Papers Track)
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Abstract: Wildfires generate considerable research interest due to their high frequency of occurrence along with global climate change. Future wildfire detection sensors would equip an on-orbit processing module that filters the useless raw images before data transmission. To efficiently detect heat anomalies from the single large scene, we need to handle the unbalanced sample sets between small active fire pixels and large-size complex background information. In this study, we contribute to solving this problem by enhancing the target feature representation in three ways. We first preprocess training images by constraining sampling ranges and removing background patches. Then we use the object-contextual representation (OCR) module to strengthen the active fire pixel representation based on the self-attention unit. The HRNet backbone provides multi-scale pixel representation as input to the OCR module. Finally, the combined loss of weighted cross-entropy loss and Lovasz hinge loss improve the segmentation accuracy further by optimizing the IoU of the foreground class. The performance is tested on the aerial FLAME dataset, whose ratio between labeled active fire and background pixels is 5.6%. The proposed framework improves the mIoU from 83.10% (baseline U-Net) to 90.81%. Future research will expand the technique for active fire detection using satellite images.

Authors: Xikun Hu (KTH Royal Institute of Technology); Alberto Costa Nogueira Junior (IBM Research Brazil); Tian Jin (College of Electronic Science, National University of Defense Technology)
ICML 2021 Prediction of Boreal Peatland Fires in Canada using Spatio-Temporal Methods (Papers Track)
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Abstract: Peat fires are the largest fires on earth in terms of fuel consumption and are responsible for a significant portion of global carbon emissions. Predicting fires in the peatlands can help decision-makers and researchers monitor and prevent peat fires. Despite this, research on predicting peatland fires remains largely understudied as compared to the prediction of other forms of fires. However, peatland fires are unique among fires and therefore require datasets and architectures attuned to their particular characteristics. In this paper, we present a new dataset, PeatSet, designed specifically for the problem of peatland fire prediction. In addition, we propose several models to tackle the problem of fire prediction for the peatlands. We develop novel neural architectures for peatland fire prediction, PeatNet, and PT-Net, with a graph-based and a transformer-based architecture, respectively. Our results indicate that these new deep-learning architectures outperform a regression baseline from existing peatland research. Among all the tested models, PT-Net achieves the highest F1 score of 0.1006 and an overall accuracy of 99.84%.

Authors: Shreya Bali (Carnegie Mellon University); Sydney Zheng (Carnegie Mellon University); Akshina Gupta (Carnegie Mellon University); Yue Wu (None); Blair Chen (Carnegie Mellon University); Anirban Chowdhury (Carnegie Mellon University); Justin Khim (Carnegie Mellon University)
ICML 2021 DroughtED: A dataset and methodology for drought forecasting spanning multiple climate zones (Papers Track)
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Abstract: Climate change exacerbates the frequency, duration and extent of extreme weather events such as drought. Previous attempts to forecast drought conditions using machine learning have focused on regional models which have two major limitations for national drought management: (i) they are trained on localised climate data and (ii) their architectures prevent them from being applied to new heterogeneous regions. In this work, we present a new large-scale dataset for training machine learning models to forecast national drought conditions, named DroughtED. The dataset consists of globally available meteorological features widely used for drought prediction, paired with location meta-data which has not previously been utilised for drought forecasting. Here we also establish a baseline on DroughtED and present the first research to apply deep learning models - Long Short-Term Memory (LSTMs) and Transformers - to predict county-level drought conditions across the full extent of the United States. Our results indicate that DroughtED enables deep learning models to learn cross-region patterns in climate data that contribute to drought conditions and models trained on DroughtED compare favourably to state-of-the-art drought prediction models trained on individual regions.

Authors: Christoph D Minixhofer (The University of Edinburgh); Mark Swan (The University of Edinburgh); Calum McMeekin (The University of Edinburgh); Pavlos Andreadis (The University of Edinburgh)
ICML 2021 Power Grid Cascading Failure Mitigation by Reinforcement Learning (Papers Track)
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Abstract: This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is formulated, and its challenges are investigated. The problem is then tackled by the RL based on DCOPF (Direct Current Optimal Power Flow). Designs of the RL framework (rewards, states, etc.) are illustrated in detail. Experiments on the IEEE 118-bus system by the proposed RL method demonstrate promising performance in reducing system collapses.

Authors: Yongli Zhu (Texas A&M University)
ICML 2021 TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data (Papers Track)
Abstract and authors: (click to expand)

Abstract: Acquiring a better understanding of drought impacts becomes increasingly vital under a warming climate. Traditional drought indices describe mainly biophysical variables and not impacts on social, economic, and environmental systems. We utilized natural language processing and bidirectional encoder representation from Transformers (BERT) based transfer learning to fine-tune the model on the data from the news-based Drought Impact Report (DIR) and then apply it to recognize seven types of drought impacts based on the filtered Twitter data from the United States. Our model achieved a satisfying macro-F1 score of 0.89 on the DIR test set. The model was then applied to California tweets and validated with keyword-based labels. The macro-F1 score was 0.58. However, due to the limitation of keywords, we also spot-checked tweets with controversial labels. 83.5% of BERT labels were correct compared to the keyword labels. Overall, the fine-tuned BERT-based recognizer provided proper predictions and valuable information on drought impacts. The interpretation and analysis of the model were consistent with experiential domain expertise.

Authors: Beichen Zhang (University of Nebraska-Lincoln); Frank Schilder (Thomson Reuters); Kelly Smith (National Drought Mitigation Center); Michael Hayes (University of Nebraska-Lincoln); Sherri Harms (University of Nebraska-Kearney); Tsegaye Tadesse (University of Nebraska-Lincoln)
ICML 2021 Attention For Damage Assessment (Papers Track)
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Abstract: Due to climate change the hurricanes are getting stronger and having longer impacts. To reduce the detrimental effects of these hurricanes faster and accurate assessments of damages are essential to the rescue teams. Like other computer vision techniques semantic segmentation can identify the damages and help in proper and prompt damage assessment. Current segmentation methods can be classified into attention and non-attention based methods. Existing non-attention based methods suffers from low accuracy and therefore attention based methods are becoming popular. Self-attention based methods can map the mutual relationship and dependencies among pixels of an image and thus improve semantic segmentation accuracy. In this paper, we present a self-attention semantic segmentation method on UAV imageries to assess the damages inflicted by a natural disaster. The proposed method outperforms four state-of-art segmentation methods both quantitatively and qualitatively with a mean IoU score of 84.03 %.

Authors: Tashnim Chowdhury (University of Maryland Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)
ICML 2021 Online LSTM Framework for Hurricane Trajectory Prediction (Papers Track)
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Abstract: Hurricanes are high-intensity tropical cyclones that can cause severe damages when the storms make landfall. Accurate long-range prediction of hurricane trajectories is an important but challenging problem due to the complex interactions between the ocean and atmosphere systems. In this paper, we present a deep learning framework for hurricane trajectory forecasting by leveraging the outputs from an ensemble of dynamical (physical) models. The proposed framework employs a temporal decay memory unit for imputing missing values in the ensemble member outputs, coupled with an LSTM architecture for dynamic path prediction. The framework is extended to an online learning setting to capture concept drift present in the data. Empirical results suggest that the proposed framework significantly outperforms various baselines including the official forecasts from U.S. National Hurricane Center (NHC).

Authors: Ding Wang (Michigan State University); Pang-Ning Tan (MSU)
ICML 2021 Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network (Papers Track)
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Abstract: An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at seasonal scales remains an outstanding challenge. Here, we present an approach to forecasting the quantiles of the maximum daily precipitation in each week up to six months ahead using the temporal fusion transformer (TFT) model. Through experiments in two regions, we compare TFT predictions with those of two baselines: climatology and a calibrated ECMWF SEAS5 ensemble forecast (S5). Our results show that, in terms of quantile risk at six month lead time, the TFT predictions significantly outperform those from S5 and show an overall small improvement compared to climatology. The TFT also responds positively to departures from normal that climatology cannot.

Authors: Daniel Salles Civitarese (IBM Research, Brazil); Daniela Szwarcman (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)
ICML 2021 Visual Question Answering: A Deep Interactive Framework for Post-Disaster Management and Damage Assessment (Papers Track)
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Abstract: Each natural disaster has left a trail of destruction and damage, which must be managed very effectively to reduce the disaster's impact. Lack of proper decision making in post-disaster managerial level can increase human suffering and waste a great amount of money. Our objective is to incorporate a deep interactive approach in the decision-making system especially in a rescue mission after any natural disaster for the systematic distribution of the limited resources and accelerating the recovery process. We believe that Visual Question Answering (VQA) is the finest way to address this issue. In visual question answering, a query-based answer regarding the situation of the affected area can add value to the decision-making system. Our main purpose of this study is to develop a Visual Question Answering model for post-disaster damage assessment purposes. With this aim, we collect the images by UAV (Unmanned Aerial Vehicle) after Hurricane Harvey and develop a dataset that includes the questions that are very important in the decision support system after a natural disaster. In addition, We propose a supervised attention-based approach in the modeling segment. We compare our approach with the two other baseline attention-based VQA algorithms namely Multimodal Factorized Bilinear (MFB) and Stacked Attention Network (SAN). Our approach outperforms in providing answers for several types of queries including simple counting, complex counting compares to the baseline models.

Authors: Argho Sarkar (University of Maryland, Baltimore County); Maryam Rahnemoonfar (University of Maryland Baltimore County)
ICML 2021 Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks (Papers Track)
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Abstract: The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.

Authors: Muhammed A Sit (The University of Iowa); Bekir Demiray (The University of Iowa); Ibrahim Demir (The University of Iowa)
ICML 2021 Wildfire Smoke Plume Segmentation Using Geostationary Satellite Imagery (Papers Track)
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Abstract: Wildfires have increased in frequency and severity over the past two decades, especially in the Western United States. Beyond physical infrastructure damage caused by these wildfire events, researchers have increasingly identified harmful impacts of particulate matter generated by wildfire smoke on respiratory, cardiovascular, and cognitive health. This inference is difficult due to the spatial and temporal uncertainty regarding how much particulate matter is specifically attributable to wildfire smoke. One factor contributing to this challenge is the reliance on manually drawn smoke plume annotations, which are often noisy representations limited to the United States. This work uses deep convolutional neural networks to segment smoke plumes from geostationary satellite imagery. We compare the performance of predicted plume segmentations versus the noisy annotations using causal inference methods to estimate the amount of variation each explains in Environmental Protection Agency (EPA) measured surface level particulate matter <2.5μm in diameter (PM2.5).

Authors: Jeffrey L Wen (Stanford University); Marshall Burke (Stanford University)
ICML 2021 Long-term Burned Area Reconstruction through Deep Learning (Proposals Track)
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Abstract: Wildfire impact studies are significantly hampered by the absence of a global long-term burned area dataset. This prevents conclusive statements on the role of anthropogenic activity on wildfire impacts over the last century. Here, we propose a workflow to construct a 1901-2014 reanalysis of monthly global burned area at a 0.5° by 0.5° scale. A neural network will be trained with weather-related, vegetational, societal and economic input parameters, and burned area as output label for the 1982-2014 time period. This model can then be applied to the whole 1901-2014 time period to create a data-driven, long-term burned area reanalysis. This reconstruction will allow to investigate the long-term effect of anthropogenic activity on wildfire impacts, will be used as basis for detection and attribution studies and could help to reduce the uncertainties in future predictions.

Authors: Seppe Lampe (Vrije Universiteit Brussel); Bertrand Le Saux (European Space Agency (ESA)); Inne Vanderkelen (Vrije Universiteit Brussel); Wim Thiery (Vrije Universiteit Brussel)
NeurIPS 2020 Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter (Papers Track)
Abstract and authors: (click to expand)

Abstract: Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not identified well yet because of the complexity and data scarcity. In this paper, we proposed a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts and connected a typical drought indicator, Standardized Precipitation Index (SPI), to the text-based impacts from the Drought Impact Reporter (DIR). The preliminary results of this study showed an outstanding performance of the well-trained models to assess drought impacts on agriculture, fire, society & public health, plants & wildlife, as well as relief, response & restrictions in Texas. It also provided a possibility to appraise drought impacts using hydro-meteorological indicators with the proposed framework in the United States, which could help drought risk management by giving additional information and improving the updating frequency of drought impacts. Our interpretation results using the Shapley additive explanation (SHAP) interpretability technique revealed that the rules guiding the predictions of XGBoost comply with domain expertise knowledge around the role that SPI indicators play around drought impacts.

Authors: Beichen Zhang (University of Nebraska-Lincoln); Fatima K Abu Salem (American University of Beirut); Michael Hayes (University of Nebraska-Lincoln); Tsegaye Tadesse (University of Nebraska-Lincoln)
NeurIPS 2020 Monitoring the Impact of Wildfires on Tree Species with Deep Learning (Papers Track)
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Abstract: One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species. Here a deep learning model is customized to classify land covers from four-band aerial imagery before and after wildfires to study the prolonged consequences of wildfires on tree species. The tree species labels are generated from manually delineated maps for five land cover classes: Conifer, Hardwood, Shrub, ReforestedTree, and Barren land. With an accuracy of 92% on the test split, the model is applied to three wildfires on data from 2009 to 2018. The model accurately delineates areas damaged by wildfires, changes in tree species, and regrowth in burned areas. The result shows clear evidence of wildfires impacting the local ecosystem and the outlined approach can help monitor reforested areas, observe changes in forest composition, and track wildfire impact on tree species.

Authors: Wang Zhou (IBM Research); Levente Klein (IBM Research)
NeurIPS 2020 Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation (Papers Track)
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Abstract: To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground truth data into the segmentation of satellite imagery of floods. We propose this approach as a solution to the labor-intensive task of generating high-quality, hand-labeled training data, and demonstrate successes and failures of different plausible crowdsourcing approaches in our model. Street to Cloud leverages community reporting and machine learning to generate novel, near-real time insights into the extent of floods to be used for emergency response.

Authors: Veda Sunkara (Cloud to Street); Matthew Purri (Rutgers University); Bertrand Le Saux (European Space Agency (ESA)); Jennifer Adams (European Space Agency (ESA))
NeurIPS 2020 Accurate river level predictions using a Wavenet-like model (Papers Track)
Abstract and authors: (click to expand)

Abstract: The effects of climate change on river levels are noticeable through a higher occurrence of floods with disastrous social and economic impacts. As such, river level forecasting is essential in flood mitigation, infrastructure management and secure shipping. Historical records of river levels and influencing factors such as rainfall or soil conditions are used for predicting future river levels. The current state-of-the-art time-series prediction model is the LSTM network, a recurrent neural network. In this work we study the efficiency of convolutional models, and specifically the WaveNet model in forecasting one-day ahead river levels. We show that the additional benefit of the WaveNet model is the computational ease with which other input features can be included in the predictions of river stage and river flow. The conditional WaveNet models outperformed conditional LSTM models for river level prediction by capturing short-term, non-linear dependencies between input data. Furthermore, the Wavenet model offers a faster computation time, stable results and more possibilities for fine-tuning.

Authors: Shannon Doyle (UvA); Anastasia Borovykh (Imperial College London)
NeurIPS 2020 Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse (Papers Track)
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Abstract: People often turn to social media to comment upon and share information about major global events. Accordingly, social media is receiving increasing attention as a rich data source for understanding people's social, political and economic experiences of extreme weather events. In this paper, we contribute two novel methodologies that leverage Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people in May 2020.

Authors: Ancil S Crayton (Booz Allen Hamilton); Joao Fonseca (NOVA Information Management School); Kanav Mehra (Independent Researcher); Jared Ross (Booz Allen Hamilton); Marcelo Sandoval-Castañeda (New York University Abu Dhabi); Michelle Ng (International Water Management Institute); Rachel von Gnechten (International Water Management Institute)
NeurIPS 2020 EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts (Papers Track)
Abstract and authors: (click to expand)

Abstract: Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variables and Earth’s surface at such local scales remains unresolved by current physical models. Large Earth observation datasets now enable us to create machine learning models capable of translating coarse weather information into high-resolution Earth surface forecasts encompassing localized climate impacts. Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts. Video prediction has been tackled with deep learning models. Developing such models requires analysis-ready datasets. We introduce EarthNet2021, a new, curated dataset containing target spatio-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale (1.28 km) weather variables. With over 32000 samples it is suitable for training deep neural networks. Comparing multiple Earth surface forecasts is not trivial. Hence, we define the EarthNetScore, a novel ranking criterion for models forecasting Earth surface reflectance. For model intercomparison we frame EarthNet2021 as a challenge with four tracks based on different test sets. These allow evaluation of model validity and robustness as well as model applicability to extreme events and the complete annual vegetation cycle. In addition to forecasting directly observable weather impacts through satellite-derived vegetation indices, capable Earth surface models will enable downstream applications such as crop yield prediction, forest health assessments, coastline management, or biodiversity monitoring. Find data, code, and how to participate at www.earthnet.tech .

Authors: Christian Requena-Mesa (Computer Vision Group, Friedrich Schiller University Jena; DLR Institute of Data Science, Jena; Max Planck Institute for Biogeochemistry, Jena); Vitus Benson (Max-Planck-Institute for Biogeochemistry); Jakob Runge (Institute of Data Science, German Aerospace Center (DLR)); Joachim Denzler (Computer Vision Group, Friedrich Schiller University Jena, Germany); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena)
NeurIPS 2020 Understanding global fire regimes using Artificial Intelligence (Papers Track)
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Abstract: Improved understanding of fire activity and its influencing factors will impact the way we interact and coexist with not only the fire itself but also with the ecosystem as a whole. We consolidate more than 20 million wildfire records between 2000 and 2018 across the six continents. This data is processed with artificial intelligence methods to discover global fire regimes, areas with characteristic fire behavior over long periods. We discover 15 groups with clear differences in fire-related historical behavior. Despite sharing historical fire behavior, regions belonging to the same group present significant differences in location and influencing factors. Groups are further divided into 62 regimes based on spatial aggregation patterns, providing a comprehensive characterization. This allows an interpretation of how a combination of vegetation, climate, and demographic features results in a specific fire regime. The current work expands on existing classification efforts and is a step forward in addressing the complex challenge of characterizing global fire regimes.

Authors: Cristobal Pais (University of California Berkeley); Jose-Ramon Gonzalez (CTFC); Pelagy Moudio (University of California Berkeley); Jordi Garcia-Gonzalo (CTFC); Marta C. González (Berkeley); Zuo-Jun Shen (University of California, Berkeley)
NeurIPS 2020 Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery (Papers Track)
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Abstract: Under the effects of global warming, extreme events such as floods and droughts are increasing in frequency and intensity. This trend directly affects communities and make all the more urgent widening the access to accurate precipitation forecasting systems for disaster preparedness. Nowadays, weather forecasting relies on numerical models necessitating massive computing resources that most developing countries cannot afford. Machine learning approaches are still in their infancy but already show the promise for democratizing weather predictions, by leveraging any data source and requiring less compute. In this work, we propose a methodology for data-driven and physics-aware global precipitation forecasting from satellite imagery. To fully take advantage of the available data, we design the system as three elements: 1. The atmospheric state is estimated from recent satellite data. 2. The atmospheric state is propagated forward in time. 3. The atmospheric state is used to derive the precipitation intensity within a nearby time interval. In particular, our use of stochastic methods for forecasting the atmospheric state represents a novel application in this domain.

Authors: Valentina Zantedeschi (GE Global Research); Daniele De Martini (University of Oxford); Catherine Tong (University of Oxford); Christian A Schroeder de Witt (University of Oxford); Piotr Bilinski (University of Warsaw / University of Oxford); Alfredo Kalaitzis (University of Oxford); Matthew Chantry (University of Oxford); Duncan Watson-Parris (University of Oxford)
NeurIPS 2020 Deep Fire Topology: Understanding the role of landscape spatial patterns in wildfire susceptibility (Papers Track)
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Abstract: Increasing wildfire activity across the globe has become an urgent issue with enormous ecological and social impacts. While there is evidence that landscape topology affects fire growth, no study has yet reported its potential influence on fire ignition. This study proposes a deep learning framework focused on understanding the impact of different landscape topologies on the ignition of a wildfire and the rationale behind these results. Our model achieves an accuracy of above 90\% in fire occurrence prediction, detection, and classification of risky areas by only exploiting topological pattern information from 17,579 landscapes. This study reveals the strong potential of landscape topology in wildfire occurrence prediction and its implications for similar research. The proposed methodology can be applied to multiple fields/studies to understand and capture the role and impact of different topological features and their interactions.

Authors: Cristobal Pais (University of California Berkeley); Alejandro Miranda (University of Chile); Jaime Carrasco (University of Chile); Zuo-Jun Shen (University of California, Berkeley)
NeurIPS 2020 Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning (Proposals Track)
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Abstract: Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes.

Authors: Irwin H McNeely (Carnegie Mellon University); Kimberly Wood (Mississippi State University); Niccolo Dalmasso (Carnegie Mellon University); Ann Lee (Carnegie Mellon University)
NeurIPS 2020 Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management (Proposals Track)
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Abstract: Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke. Here we show how machine learning, such as spectral clustering and manifold learning, can provide interpretable representations and powerful tools for differentiating between smoke types, hence providing forest managers with vital information on effective strategies to reduce climate-induced wildfires while minimizing production of harmful smoke.

Authors: Lorenzo Tomaselli (Carnegie Mellon University); Coty Jen (Carnegie Mellon University); Ann Lee (Carnegie Mellon University)
NeurIPS 2020 Graph Neural Networks for Improved El Niño Forecasting (Proposals Track)
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Abstract: Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheric patterns called teleconnections. Hence, we propose the application of spatiotemporal Graph Neural Networks (GNN) to forecast ENSO at long lead times, finer granularity and improved predictive skill than current state-of-the-art methods. The explicit modeling of information flow via edges may also allow for more interpretable forecasts. Preliminary results are promising and outperform state-of-the art systems for projections 1 and 3 months ahead.

Authors: Salva Rühling Cachay (Technical University of Darmstadt); Emma Erickson (University of Illinois at Urbana-Champaign); Arthur F C Bucker (University of São Paulo); Ernest J Pokropek (Warsaw University of Techology); Willa Potosnak (Duquesne University); Salomey Osei (African Master's of Machine Intelligence(AMMI-GH)); Björn Lütjens (MIT)
NeurIPS 2020 Expert-in-the-loop Systems Towards Safety-critical Machine Learning Technology in Wildfire Intelligence (Proposals Track)
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Abstract: With the advent of climate change, wildfires are becoming more frequent and severe across several regions worldwide. To prevent and mitigate its effects, wildfire intelligence plays a pivotal role, e.g. to monitor the evolution of wildfires and for early detection in high-risk areas such as wildland-urban-interface regions. Recent works have proposed deep learning solutions for fire detection tasks, however the current limited databases prevent reliable real-world deployments. We propose the development of expert-in-the-loop systems that combine the benefits of semi-automated data annotation with relevant domain knowledge expertise. Through this approach we aim to improve the data curation process and contribute to the generation of large-scale image databases for relevant wildfire tasks and empower the application of machine learning techniques in wildfire intelligence in real scenarios.

Authors: Maria João Sousa (IDMEC, Instituto Superior Técnico, Universidade de Lisboa); Alexandra Moutinho (IDMEC, Instituto Superior Técnico, Universidade de Lisboa); Miguel Almeida (ADAI, University of Coimbra)
ICLR 2020 A Machine Learning Pipeline to Predict Vegetation Health (Papers Track)
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Abstract: Agricultural droughts can exacerbate poverty and lead to famine. Timely distribution of disaster relief funds is essential to help minimise the impact of drought. Indices of vegetation health are indicative of higher risk of agricultural drought, but their prediction remains challenging, particularly in Africa. Here, we present an open-source machine learning pipeline for climate-related data. Specifically, we train and analyse a recurrent model to predict pixel-wise vegetation health in Kenya.

Authors: Thomas Lees (University of Oxford); Gabriel Tseng (Okra Solar); Simon Dadson (University of Oxford); Alex Hernández (University of Osnabrück); Clement G. Atzberger (University of Natural Resources and Life Sciences); Steven Reece (University of Oxford)
ICLR 2020 A CONTINUAL LEARNING APPROACH FOR LOCAL LEVEL ENVIRONMENTAL MONITORING IN LOW-RESOURCE SETTINGS (Papers Track)
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Abstract: An increasingly important dimension in the quest for mitigation and monitoring of environmental change is the role of citizens. The crowd-based monitoring of local level anthropogenic alterations is essential towards measurable changes in different contributing factors to climate change. With the proliferation of mobile technologies here in the African continent, it is useful to have machine learning based models that are deployed on mobile devices and that can learn continually from streams of data over extended time, possibly pertaining to different tasks of interest. In this paper, we demonstrate the localisation of deforestation indicators using lightweight models and extend to incorporate data about wildfires and smoke detection. The idea is to show the need and potential of continual learning approaches towards building robust models to track local environmental alterations.

Authors: Arijit Patra (University of Oxford)
ICLR 2020 Hurricane Nowcasting with Irregular Time-step using Neural-ODE and Video Prediction (Proposals Track)
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Abstract: Fast and accurate prediction of extreme climate events is critical especially in the recent globally warming environment. Considering recent advancements in deep neural networks, it is worthwhile to tackle this problem as data-driven spatio-temporal prediction using neural networks. However, a nontrivial challenge in practice lies in irregular time gaps between which climate observation data are collected due to sensor errors and other issues. This paper proposes an approach for spatio-temporal hurricane prediction that can address this issue of irregular time gaps in collected data with a simple but robust end-to-end model based on Neural Ordinary Differential Equation and video prediction model based on Retrospective Cycle-GAN.

Authors: Sunghyun Park (KAIST); Kangyeol Kim (KAIST); Sookyung Kim (Lawrence Livermore National Laboratory); Joonseok Lee (Google Research); Junsoo Lee (KAIST); Jiwoo Lee (Lawrence Livermore National Laboratory); Jaegul Choo (KAIST)
ICLR 2020 Indigenous Knowledge Aware Drought Monitoring, Forecasting and Prediction Using Deep Learning Techniques (Proposals Track)
Abstract and authors: (click to expand)

Abstract: The general objective of this proposed research work is to design deep learning based hybrid comprehensive framework for drought monitoring, forecasting and prediction using scientific and indigenous knowledge as an integration of connectionist and symbolic AI. In Ethiopia, among all extreme climate events, drought is considered as the most complex phenomenon affecting the country and its impact is also high due to absence of locally grounded intelligent and explainable technology-oriented drought early warning and monitoring system. Thus, studying Ethiopic perspective of drought monitoring and prediction in line with continental and global climate change is vital for drought impact minimization and sustainable development of the country. Moreover, having technology assisted early protective, preventative action is also many times cheaper than the associated response to humanitarian crisis. Accordingly, this proposed work will have different expected outputs, including: drought risk identification, drought monitoring, drought preparedness, drought forecasting, drought mitigation, and post drought best practice recommendation models with interactive visualizations and explanations.

Authors: Kidane W Degefa (Haramaya University)
NeurIPS 2019 Streamflow Prediction with Limited Spatially-Distributed Input Data (Papers Track)
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Abstract: Climate change causes more frequent and extreme weather phenomena across the globe. Accurate streamflow prediction allows for proactive and mitigative action in some of these events. As a first step towards models that predict streamflow in watersheds for which we lack ground truth measurements, we explore models that work on spatially-distributed input data. In such a scenario, input variables are more difficult to acquire, and thus models have access to limited training data. We present a case study focusing on Lake Erie, where we find that tree-based models can yield more accurate predictions than both neural and physically-based models.

Authors: Martin Gauch (University of Waterloo); Juliane Mai (University of Waterloo); Shervan Gharari (University of Saskatchewan); Jimmy Lin (University of Waterloo)
NeurIPS 2019 Machine Learning for Generalizable Prediction of Flood Susceptibility (Papers Track)
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Abstract: Flooding is a destructive and dangerous hazard and climate change appears to be increasing the frequency of catastrophic flooding events around the world. Physics-based flood models are costly to calibrate and are rarely generalizable across different river basins, as model outputs are sensitive to site-specific parameters and human-regulated infrastructure. Statistical models trained primarily from remotely-sensed Earth observation data could reduce the need for extensive in-situ measurements. In this work, we develop generalizable, multi-basin models of river flooding susceptibility using geographically-distributed data from the USGS stream gauge network. Machine learning models are trained in a supervised framework to predict two measures of flood susceptibility from a mix of river basin attributes, impervious surface cover information derived from satellite imagery, and historical records of rainfall and stream height. We report prediction performance of multiple models using precision-recall curves, and compare with performance of naive baselines. This work on multi-basin flood prediction represents a step in the direction of making flood prediction accessible to all at-risk communities.

Authors: Dylan Fitzpatrick (Carnegie Mellon University); Chelsea Sidrane (Stanford University); Andrew Annex (Johns Hopkins University); Diane O'Donoghue (kx); Piotr Bilinski (University of Warsaw)
NeurIPS 2019 Predictive Inference of a Wildfire Risk Pipeline in the United States (Proposals Track)
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Abstract: Wildfires are rare events that present severe threats to life and property. Understanding their propagation is of key importance to mitigate and contain their impact, especially since climate change is increasing their occurrence. We propose an end-to-end sequential model of wildfire risk components, including wildfire location, size, duration, and risk exposure. We do so through a combination of marked spatio-temporal point processes and conditional density estimation techniques. Unlike other approaches that use regression-based methods, this approach allows both predictive accuracy and an associated uncertainty measure for each risk estimate, accounting for the uncertainty in prior model components. This is particularly beneficial for timely decision-making by different wildfire risk management stakeholders. To allow us to build our models without limiting them to a specific state or county, we have collected open wildfire and climate data for the entire continental United States. We are releasing this aggregated dataset to enable further o pen research on wildfire models at a national scale.

Authors: Shamindra Shrotriya (Carnegie Mellon University); Niccolo Dalmasso (Carnegie Mellon University); Alex Reinhart (Carnegie Mellon University)
ICML 2019 Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions (Research Track)
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Abstract: Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc.. The recently available satellite-based observations give us a unique opportunity to directly build data-driven models to predict soil moisture instead of using land surface models, but previously there was no uncertainty estimate. We tested Monte Carlo dropout with an aleatoric term (MCD+A) for our long short-term memory models for this problem, and ask if the uncertainty terms behave as they were argued to. We show that MCD+A indeed gave a good estimate of our predictive error, provided we tune a hyperparameter and use a representative training dataset. The aleatoric term responded strongly to observational noise and the epistemic term clearly acted as a detector for physiographic dissimilarity from the training data. However, when the training and test data are characteristically different, the aleatoric term could be misled, undermining its reliability. We will also discuss some of the major challenges for which we anticipate the geoscientific communities will need help from computer scientists in applying AI to climate or hydrologic modeling.

Authors: Chaopeng Shen (Pennsylvania State University)
ICML 2019 Planetary Scale Monitoring of Urban Growth in High Flood Risk Areas (Research Track)
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Abstract: Climate change is increasing the incidence of flooding. Many areas in the developing world are experiencing strong population growth but lack adequate urban planning. This represents a significant humanitarian risk. We explore the use of high-cadence satellite imagery provided by Planet, who’s flock of over one hundred ’Dove’ satellites image the entire earth’s landmass everyday at 3-5m resolution. We use a deep learning-based computer vision approach to measure flood-related humanitarian risk in 5 cities in Africa.

Authors: Christian F Clough (Planet); Ramesh Nair (Planet); Gopal Erinjippurath (Planet); Matt George (Planet); Jesus Martinez Manso (Planet)
ICML 2019 Machine Intelligence for Floods and the Built Environment Under Climate Change (Ideas Track)
Abstract and authors: (click to expand)

Abstract: While intensification of precipitation extremes has been attributed to anthropogenic climate change using statistical analysis and physics-based numerical models, understanding floods in a climate context remains a grand challenge. Meanwhile, an increasing volume of Earth science data from climate simulations, remote sensing, and Geographic Information System (GIS) tools offers opportunity for data-driven insight and action plans. Defining Machine Intelligence (MI) broadly to include machine learning and network science, here we develop a vision and use preliminary results to showcase how scientific understanding of floods can be improved in a climate context and translated to impacts with a focus on Critical Lifeline Infrastructure Networks (CLIN).

Authors: Kate Duffy (Northeastern University); Auroop Ganguly (Northeastern University)
ICML 2019 Learning representations to predict landslide occurrences and detect illegal mining across multiple domains (Ideas Track)
Abstract and authors: (click to expand)

Abstract: Modelling landslide occurrences is challenging due to lack of valuable prior information on the trigger. Satellites can provide crucial insights for identifying landslide activity and characterizing patterns spatially and temporally. We propose to analyze remote sensing data from affected regions using deep learning methods, find correlation in the changes over time, and predict future landslide occurrences and their potential causes. The learned networks can then be applied to generate task-specific imagery, including but not limited to, illegal mining detection and disaster relief modelling.

Authors: Aneesh Rangnekar (Rochester Institute of Technology); Matthew J Hoffman (Rochester Institute of Technology)