Land Use

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Venue Title
ICLR 2023 An automatic mobile approach for Tree DBH Estimation Using a Depth Map and a Regression Convolutional Neural Network (Papers Track)
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Abstract: Carbon credit programs finance projects to reduce emissions, remove pollutants, improve livelihoods, and protect natural ecosystems. Ensuring the quality and integrity of such projects is essential to their success. One of the most important variables used in nature-based solutions to measure carbon sequestration is the diameter at breast height (DBH) of trees. In this paper, we propose an automatic mobile computer vision method to estimate the DBH of a tree using a single depth map on a smartphone, along with our created dataset DepthMapDBH2023. We successfully demonstrated that this dataset paired with a lightweight regression convolutional neural network is able to accurately estimate the DBH of trees distinct in appearance, shape, number of tree forks, tree density and crowding, and vine presence. Automation of these measurements will help crews in the field who are collecting data for forest inventories. Gathering as much on-the-ground data as possible is required to ensure the transparency of carbon credit projects. Access to high-quality datasets of manual measurements helps improve biomass models which are widely used in the field of ecological simulation. The code used in this paper will be publicly available on Github and the dataset on Kaggle.

Authors: Margaux Masson-Forsythe (Earthshot Labs); Margaux Masson-Forsythe (Earthshot Labs)
ICLR 2023 Remote Control: Debiasing Remote Sensing Predictions for Causal Inference (Papers Track)
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Abstract: Understanding and properly estimating the impacts of environmental interventions is of critical importance as we work towards achieving global climate goals. Remote sensing has become an essential tool for evaluating when and where climate policies have positive impacts on factors like greenhouse gas emissions and carbon sequestration. However, when machine learning models trained to predict outcomes using remotely sensed data simply minimize a standard loss function, the predictions that they generate can produce biased estimates in downstream causal inference. If prediction error in the outcome variable is correlated with policy variables or important confounders, as is the case for many widely used remote sensing data sets, estimates of the causal impacts of policies can be biased. In this paper, we demonstrate how this bias can arise, and we propose the use of an adversarial debiasing model (Zhang, Lemoine, and Mitchell 2018) in order to correct the issue when using satellite data to generate machine learning predictions for use in causal inference. We apply this method to a case study of the relationship between roads and tree cover in West Africa, where our results indicate that adversarial debiasing can recover a much more accurate estimate of the parameter of interest compared to when the standard approach is used.

Authors: Matthew Gordon (Yale); Megan Ayers (Yale University); Eliana Stone (Yale School of the Environment); Luke C Sanford (Yale School of the Environment)
ICLR 2023 Widespread increases in future wildfire risk to global forest carbon offset projects revealed by explainable AI (Papers Track)
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Abstract: Carbon offset programs are critical in the fight against climate change. One emerging threat to the long-term stability and viability of forest carbon offset projects is wildfires, which can release large amounts of carbon and limit the efficacy of associated offsetting credits. However, analysis of wildfire risk to forest carbon projects is challenging because existing models for forecasting long-term fire risk are limited in predictive accuracy. Therefore, we propose an explainable artificial intelligence (XAI) model trained on 7 million global satellite wildfire observations. Validation results suggest substantial potential for high resolution, enhanced accuracy projections of global wildfire risk, and the model outperforms the U.S. National Center for Atmospheric Research's leading fire model. Applied to a collection of 190 global forest carbon projects, we find that fire exposure is projected to increase 55% [37-76%] by 2080 under a mid-range scenario (SSP2-4.5). Our results indicate the large wildfire carbon project damages seen in the past decade are likely to become more frequent as forests become hotter and drier. In response, we hope the model can support wildfire managers, policymakers, and carbon market analysts to preemptively quantify and mitigate long-term permanence risks to forest carbon projects.

Authors: Tristan Ballard (Sust Inc); Gopal Erinjippurath (Sust Global); Matthew W Cooper (Sust Global); Chris Lowrie (Sust Global)
NeurIPS 2022 Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification (Papers Track)
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Abstract: Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.

Authors: Joseph Early (University of Southampton); Ying-Jung C Deweese (Georgia Insititute of Technology); Christine Evers (University of Southampton); Sarvapali Ramchurn (University of Southampton)
NeurIPS 2022 Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning (Papers Track)
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Abstract: Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentinel-1 and -2 satellite imagery and develop several extensions toward making it effective in practice. In an application to few-shot Local Climate Zone (LCZ) prediction, we show that these networks outperform multiple common baselines on datasets with a large number of classes. Further, we provide evidence that instance normalization can significantly stabilize the training process and that explicitly shaping the embedding space using supervised contrastive learning can lead to improved performance.

Authors: Marcel Hussing (University of Pennsylvania); Karen Li (University of Pennsylvania); Eric Eaton (University of Pennsylvania)
NeurIPS 2022 Bayesian State-Space SCM for Deforestation Baseline Estimation for Forest Carbon Credit (Papers Track) Best Paper: Pathway to Impact
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Abstract: In forest carbon credit, the concept of dynamic (or ex-post) baseline has been discussed to overcome the criticism of junk carbon credit, while an ex-ante baseline is still necessary in terms of project ïŹnance and risk assessment. We propose a Bayesian state-space SCM, which integrates both ex-ante and ex-post baseline estimation in a time-series causal inference framework. We apply the proposed model to a REDD+ project in Brazil, and show that it might have had a small, positive effect but had been over-credited and that the 90% predictive interval of the ex-ante baseline included the ex-post baseline, implying our ex-ante estimation can work effectively.

Authors: Keisuke Takahata (sustainacraft, Inc.); Hiroshi Suetsugu (sustainacraft, Inc.); Keiichi Fukaya (National Institute for Environmental Studies); Shinichiro Shirota (Hitotsubashi University)
NeurIPS 2022 ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning (Proposals Track)
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Abstract: Restoring ecosystems and reducing deforestation are necessary tools to mitigate the anthropogenic climate crisis. Current measurements of forest carbon stock can be inaccurate, in particular for underrepresented and small-scale forests in the Global South, hindering transparency and accountability in the Monitoring, Reporting, and Verification (MRV) of these ecosystems. There is thus need for high quality datasets to properly validate ML-based solutions. To this end, we present ForestBench, which aims to collect and curate geographically-balanced gold-standard datasets of small-scale forest plots in the Global South, by collecting ground-level measurements and visual drone imagery of individual trees. These equitable validation datasets for ML-based MRV of nature-based solutions shall enable assessing the progress of ML models for estimating above-ground biomass, ground cover, and tree species diversity.

Authors: Lucas Czech (Carnegie Institution for Science); Björn LĂŒtjens (MIT); David Dao (ETH Zurich)
NeurIPS 2022 Automating the creation of LULC datasets for semantic segmentation (Tutorials Track)
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Abstract: High resolution and accurate Land Use and Land Cover mapping (LULC) datasets are increasingly important and can be widely used in monitoring climate change impacts in agriculture, deforestation, and the carbon cycle. These datasets represent physical classifications of land types and spatial information over the surface of the Earth. These LULC datasets can be leveraged in a plethora of research topics and industries to mitigate and adapt to environmental changes. High resolution urban mappings can be used to better monitor and estimate building albedo and urban heat island impacts, and accurate representation of forests and vegetation can even be leveraged to better monitor the carbon cycle and climate change through improved land surface modelling. The advent of machine learning (ML) based CV techniques over the past decade provides a viable option to automate LULC mapping. One impediment to this has been the lack of large ML datasets. Large vector datasets for LULC are available, but can’t be used directly by ML practitioners due to a knowledge gap in transforming the input into a dataset of paired satellite images and segmentation masks. We demonstrate a novel end-to-end pipeline for LULC dataset creation that takes vector land cover data and provides a training-ready dataset. We will use Sentinel-2 satellite imagery and the European Urban Atlas LULC data. The pipeline manages everything from downloading satellite data, to creating and storing encoded segmentation masks and automating data checks. We then use the resulting dataset to train a semantic segmentation model. The aim of the pipeline is to provide a way for users to create their own custom datasets using various combinations of multispectral satellite and vector data. In addition to presenting the pipeline, we aim to provide an introduction to multispectral imagery, geospatial data and some of the challenges in using it for ML.

Authors: Sambhav S Rohatgi (Spacesense.ai); Anthony Mucia (Spacesense.ai)
NeurIPS 2021 Mapping Post-Climate Change Biogeographical Regions with Deep Latent Variable Models (Papers Track)
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Abstract: Forecasting future changes to biodiversity due to shifts in climate is challenging due to nonlinear interactions between species as recorded in their presence/absence data. This work proposes using variational autoencoders with environmental covariates to identify low-dimensional structure in species’ joint co-occurrence patterns and leveraging this simplified representation to provide multivariate predictions of their habitat extent under future climate scenarios. We pursue a latent space clustering approach to map biogeographical regions of frequently co-occurring species and apply this methodology to a dataset from northern Belgium, generating predictive maps illustrating how these regions may expand or contract with changing temperature under a future climate scenario.

Authors: Christopher Krapu (Oak Ridge National Lab - Oak Ridge, TN)
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 LĂŒtjens (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 Urban Tree Species Classification Using Aerial Imagery (Papers Track)
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Abstract: Urban trees help regulate temperature, reduce energy consumption, improve urban air quality, reduce wind speeds, and mitigating the urban heat island effect. Urban trees also play a key role in climate change mitigation and global warming by capturing and storing atmospheric carbon-dioxide which is the largest contributor to greenhouse gases. Automated tree detection and species classification using aerial imagery can be a powerful tool for sustainable forest and urban tree management. Hence, This study first offers a pipeline for generating labelled dataset of urban trees using Google Map's aerial images and then investigates how state of the art deep Convolutional Neural Network models such as VGG and ResNet handle the classification problem of urban tree aerial images under different parameters. Experimental results show our best model achieves an average accuracy of 60% over 6 tree species.

Authors: Emily Waters (Anglia Ruskin University); Mahdi Maktabdar Oghaz (Anglia Ruskin University); Lakshmi Babu Saheer (Anglia Ruskin University)
ICML 2021 Forest Terrain Identification using Semantic Segmentation on UAV Images (Papers Track)
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Abstract: Beavers' habitat is known to alter the terrain, providing biodiversity in the area, and recently their lifestyle is linked to climatic changes by reducing greenhouse gases levels in the region. To analyse the impact of beavers’ habitat on the region, it is, therefore, necessary to estimate the terrain alterations caused by beaver actions. Furthermore, such terrain analysis can also play an important role in domains like wildlife ecology, deforestation, land-cover estimations, and geological mapping. Deep learning models are known to provide better estimates on automatic feature identification and classification of a terrain. However, such models require significant training data. Pre-existing terrain datasets (both real and synthetic) like CityScapes, PASCAL, UAVID, etc, are mostly concentrated on urban areas and include roads, pathways, buildings, etc. Such datasets, therefore, are unsuitable for forest terrain analysis. This paper contributes, by providing a finely labelled novel dataset of forest imagery around beavers’ habitat, captured from a high-resolution camera on an aerial drone. The dataset consists of 100 such images labelled and classified based on 9 different classes. Furthermore, a baseline is established on this dataset using state-of-the-art semantic segmentation models based on performance metrics including Intersection Over Union (IoU), Overall Accuracy (OA), and F1 score.

Authors: Muhammad Umar (Anglia Ruskin University); Lakshmi Babu Saheer (Anglia Ruskin University); Javad Zarrin (Anglia Ruskin University)
ICML 2021 Quantification of Carbon Sequestration in Urban Forests (Papers Track)
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Abstract: Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere, however, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species, which are crucial attributes in carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from remote imagery in order to calculate the tree's biomass. Specifically, for Manhattan, New York City, we estimate a total of 52,000 tons of carbon sequestered in trees.

Authors: Levente Klein (IBM Research); Wang Zhou (IBM Research); Conrad M Albrecht (IBM Research)
ICML 2021 FIRE-ML: A Remotely-sensed Daily Wildfire Forecasting Dataset for the Contiguous United States (Papers Track)
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Abstract: Wildfires are natural phenomena that can have devastating effects on ecosystems, urban developments, and the environment. Improving the scientific understanding of these events and the ability to forecast how they will evolve in the short- and long-term are ongoing multi-decadal challenges. We present a large-scale dataset, well-suited to machine learning, that aggregates and aligns multiple remotely-sensed and forecasted data products to provide a holistic set of features for forecasting wildfires on daily timescales. This dataset includes 4.2 million unique active fire detections, covers the majority of the contiguous United States from 2012 to 2020, and includes active fire detections, land cover, topography, and meteorology.

Authors: Casey A Graff (UC Irvine)
NeurIPS 2020 Spatio-Temporal Learning for Feature Extraction inTime-Series Images (Papers Track)
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Abstract: Earth observation programs have provided highly useful information in global climate change research over the past few decades and greatly promoted its development, especially through providing biological, physical, and chemical parameters on a global scale. Programs such as Landsat, Sentinel, SPOT, and Pleiades can be used to acquire huge volume of medium to high resolution images every day. In this work, we organize these data in time series and we exploit both temporal and spatial information they provide to generate accurate and up-to-date land cover maps that can be used to monitor vulnerable areas threatened by the ongoing climatic and anthropogenic global changes. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are described. Examples are provided for the monitoring of roads and mangrove forests on the West African coast.

Authors: Gael Kamdem De Teyou (Huawei)
NeurIPS 2020 Predicting Landsat Reflectance with Deep Generative Fusion (Papers Track)
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Abstract: Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution as no single sensor provides fine-grained acquisitions with frequent coverage. This hinders their potential to assist vegetation monitoring or humanitarian actions, which require detecting rapid and detailed terrestrial surface changes. In this work, we probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics. We introduce a dataset of co-registered Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat surface reflectance time series and demonstrate the ability of our generative model to blend coarse daily reflectance information into low-paced finer acquisitions. We benchmark our proposed model against state-of-the-art reflectance fusion algorithms.

Authors: Shahine Bouabid (University of Oxford); Jevgenij Gamper (Cervest Ltd.)
NeurIPS 2020 ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery (Papers Track)
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Abstract: Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpreters. We use the dataset to train and validate the models and demonstrate that ForestNet substantially outperforms other standard driver classification approaches. In order to support future research on automated approaches to deforestation driver classification, the dataset curated in this study is publicly available at https://stanfordmlgroup.github.io/projects/forestnet .

Authors: Jeremy A Irvin (Stanford); Hao Sheng (Stanford University); Neel Ramachandran (Stanford University); Sonja Johnson-Yu (Stanford University); Sharon Zhou (Stanford University); Kyle Story (Descartes Labs); Rose Rustowicz (Descartes Labs); Cooper Elsworth (Descartes Labs); Kemen Austin (RTI International); Andrew Ng (Stanford University)
NeurIPS 2020 Satellite imagery analysis for Land Use, Land Use Change and Forestry: A pilot study in Kigali, Rwanda (Papers Track)
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Abstract: Estimating greenhouse gases from the Agriculture, Forestry and Other land Use (AFOLU) sector is very challenging partly due to the unavailability of data (particularly for land use and land use change sectors) and inadequate experts to analyze this data in case it is available. We used Collect Earth together with Machine Learning techniques to be able to predict and classify all the land use types based on some few points collected using Collect Earth. We investigated the adoption of this tool and technology in Rwanda to help its national and sub-national inventories. The use of Collect Earth and the Machine Learning (ML) implementation will help Rwanda monitor and predict its Land Use, Land Use Change, and Forestry in a cost-effective manner whiles enhancing the quality of reports submitted to national and international bodies whiles introducing a new approach. Among the classification algorithms we tested, we had an overall classification accuracy of 97% using the Classification and Regression Trees (CART) algorithm to to predict the six land Use classes across the country.

Authors: Bright Aboh (African Institute for Mathematical Sciences); Alphonse Mutabazi (UN Environment Program)
NeurIPS 2020 Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a proposed general purpose sensor-fusion semantic embedding model (Proposals Track)
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Abstract: Earth observation offers new insight into anthropogenic changes to nature, and how these changes are effecting (and are effected by) the built environment and the real economy. With the global availability of medium-resolution (10-30m) synthetic aperature radar (SAR) Sentinel-1 and multispectral Sentinel-2 imagery, machine learning can be employed to offer these insights at scale, unbiased to company- and country-level reporting. In this proposal, we document the development of an extensible corpus of labelled and unlabelled Sentinel-1 and Sentinel-2 imagery for the purposes of sensor fusion research. We make a large corpus and supporting code publicly available. We propose our own experiment design for the development of \emph{DeepSentinel}, a general-purpose semantic embedding model. Our aspiration is to provide pretrained models for transfer learning applications, significantly accelerating the impact of machine learning-enhanced earth observation on climate change mitigation.

Authors: Lucas Kruitwagen (University of Oxford)
ICLR 2020 DETECTION OF HOUSING AND AGRICULTURE AREAS ON DRY-RIVERBEDS FOR THE EVALUATION OF RISK BY LANDSLIDES USING LOW-RESOLUTION SATELLITE IMAGERY BASED ON DEEP LEARNING. STUDY ZONE: LIMA, PERU (Papers Track)
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Abstract: The expansion of human settlements in Peru has caused risk exposure to landslides. However, this risk could increase because the intensity of the El niño phenomenon will be greater in the coming years, increasing rainfall on the Peruvian coast. In this paper, we present a novel methodology for detecting housing areas and agricultural lands in low-resolution satellite imagery in order to analyze potential risk in case of unexpected landslides. It was developed by creating two datasets from Lima Metropolitana in Peru, one of which is for detecting dry riverbeds and agriculture lands, and the other for classifying housing areas. We applied data augmentation based on geometrical methods and trained architectures based on U-net methods separately and then, overlap the results for risk assessment. We found that there are areas with significant potential risk that have been classified by the Peruvian government as medium or low risk areas. On this basis, it is recommended obtain a dataset with better resolution that can identify how many housing areas will be affected and take the appropriate prevention measures. Further research in post-processing is needed for suppress noise in our results.

Authors: Brian CerrĂłn (National University of Engineering); Cristopher Bazan (National University of Engineering); Alberto Coronado (National University of Engineering)
ICLR 2020 Using ML to close the vocabulary gap in the context of environment and climate change in Chichewa (Proposals Track)
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Abstract: In the west, alienation from nature and deteriorating opportunities to experience it, have led educators to incorporate educational programs in schools, to bring pupils in contact with nature and to enhance their understanding of issues related to the environment and its protection. In Africa, and in Malawi, where most people engage in agriculture, and spend most of their time in the 'outdoors', alienation from nature is happening too, although in different ways. Large portion of the indigenous vocabulary and knowledge remains unknown or is slowly disappearing, and there is a need to build a glossary of terms regarding environment and climate change in the vernacular to improve the dialog regarding climate change and environmental protection.. We believe that ML has a role to play in closing the ‘vocabulary gap’ of terms and concepts regarding the environment and climate change that exists in Chichewa and other Malawian languages by helping to creating a visual dictionary of key terms used to describe the environment and explain the issues involved in climate change and their meaning. Chichewa is a descriptive language, one English term may be translated using several words. Thus, the task is not to detect just literal translations, but also translations by means of ‘descriptions’ and illustrations and thus extract correspondence between terms and definitions and to measure how appropriate a term is to convey the meaning intended. As part of this project, ML can be used to identify ‘loanword patterns’, which may be useful in understanding the transmission of cultural items.

Authors: Amelia Taylor (University of Malawi, The Polytechnic)
NeurIPS 2019 Quantifying Urban Canopy Cover with Deep Convolutional Neural Networks (Papers Track)
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Abstract: Urban canopy cover is important to mitigate the impact of climate change. Yet, existing quantification of urban greenery is either manual and not scalable, or use traditional computer vision methods that are inaccurate. We train deep convolutional neural networks (DCNNs) on datasets used for self-driving cars to estimate urban greenery instead, and find that our semantic segmentation and direct end-to-end estimation method are more accurate and scalable, reducing mean absolute error of estimating the Green View Index (GVI) metric from 10.1% to 4.67%. With the revised DCNN methods, the Treepedia project was able to scale and analyze canopy cover in 22 cities internationally, sparking interest and action in public policy and research fields.

Authors: Bill Cai (Massachusetts Institute of Technology); Xiaojiang Li (Temple University); Carlo Ratti (Massachusetts Institute of Technology )
NeurIPS 2019 Human-Machine Collaboration for Fast Land Cover Mapping (Papers Track)
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Abstract: We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model's predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. Our hypothesis is that this rich feedback allows human labelers to create mental models that enable them to better choose which biases to introduce to the model. We implement this framework for fine-tuning high-resolution land cover segmentation models and evaluate it against traditional active learning based approaches. More specifically, we fine-tune a deep neural network -- trained to segment high-resolution aerial imagery into different land cover classes in Maryland, USA -- to a new spatial area in New York, USA. We find that the tight loop turns the algorithm and the human operator into a hybrid system that can produce land cover maps of large areas more efficiently than the traditional workflows.

Authors: Caleb Robinson (Georgia Institute of Technology); Anthony Ortiz (University of Texas at El Paso); Nikolay Malkin (Yale University); Blake Elias (Microsoft); Andi Peng (Microsoft); Dan Morris (Microsoft); Bistra Dilkina (University of Southern California); Nebojsa Jojic (Microsoft Research)
NeurIPS 2019 Measuring Impact of Climate Change on Tree Species: analysis of JSDM on FIA data (Papers Track) Honorable Mention
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Abstract: One of the first beings affected by changes in the climate are trees, one of our most vital resources. In this study tree species interaction and the response to climate in different ecological environments is observed by applying a joint species distribution model to different ecological domains in the United States. Joint species distribution models are useful to learn inter-species relationships and species response to the environment. The climates’ impact on the tree species is measured through species abundance in an area. We compare the model’s performance across all ecological domains and study the sensitivity of the climate variables. With the prediction of abundances, tree species populations can be predicted in the future and measure the impact of climate change on tree populations.

Authors: Hyun Choi (University of Florida); Sergio Marconi (University of Florida); Ali Sadeghian (University of Florida); Ethan White (University of Florida); Daisy Zhe Wang (Univeresity of Florida)
ICML 2019 Mapping land use and land cover changes faster and at scale with deep learning on the cloud (Research Track)
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Abstract: Policymakers rely on Land Use and Land Cover (LULC) maps for evaluation and planning. They use these maps to plan climate-smart agriculture policy, improve housing resilience (to earthquakes or other natural disasters), and understand how to grow commerce in small communities. A number of institutions have created global land use maps from historic satellite imagery. However, these maps can be outdated and are often inaccurate, particularly in their representation of developing countries. We worked with the European Space Agency (ESA) to develop a LULC deep learning workflow on the cloud that can ingest Sentinel-2 optical imagery for a large scale LULC change detection. It’s an end-to-end workflow that sits on top of two comprehensive tools, SentinelHub, and eo-learn, which seamlessly link earth observation data with machine learning libraries. It can take in the labeled LULC and associated AOI in shapefiles, set up a task to fetch cloud-free, time series imagery stacks within the defined time interval by the users. It will pair the satellite imagery tile with it’s labeled LULC mask for the supervised deep learning model training on the cloud. Once a well-performing model is trained, it can be exported as a Tensorflow/Pytorch serving docker image to work with our cloud-based model inference pipeline. The inference pipeline can automatically scale with the number of images to be processed. Changes in land use are heavily influenced by human activities (e.g. agriculture, deforestation, human settlement expansion) and have been a great source of greenhouse gas emissions. Sustainable forest and land management practices vary from region to region, which means having flexible, scalable tools will be critical. With these tools, we can empower analysts, engineers, and decision-makers to see where contributions to climate-smart agricultural, forestry and urban resilience programs can be made.

Authors: Zhuangfang Yi (Development Seed); Drew Bollinger (Development Seed); Devis Peressutti (Sinergise)
ICML 2019 Learning representations to predict landslide occurrences and detect illegal mining across multiple domains (Ideas Track)
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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)