SuoiAI: Building a Dataset for Aquatic Invertebrates in Vietnam (Proposals Track)
MINH TUE VO THANH (NUOC SOLUTIONS); Lakshay Sharma (Microsoft); Tuan Dinh (NUOC SOLUTIONS); Khuong Dinh (University of Oslo); Trang Nguyen (Bowdoin); Trung Phan (N/A); Minh Do (NUOC SOLUTIONS); Duong Vu (Royal Netherlands Academy of Arts and Sciences)
Abstract
Understanding and monitoring aquatic biodiversity is critical for ecological health and conservation efforts. This paper proposes SuoiAI, an end-to end pipeline for building a dataset of aquatic invertebrates in Vietnam and employing machine learning (ML) techniques for species classification. We outline the methods for data collection, annotation, and model training, focusing on reducing annotation effort through semi-supervised learning and leveraging state-of-the-art object detection and classification models. Our approach aims to overcome challenges such as data scarcity, fine-grained classification, and deployment in diverse environmental conditions.