DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets

作者：Elena Sierra, Lauren Gillespie, Salim Soltani, Moisés Expósito‐Alonso, Teja Kattenborn · 发表于：Proceedings of the AAAI Conference on Artificial Intelligence · 年份：2025 · DOI：10.1609/aaai.v39i27.35060 · 被引用次数：7 · 研究领域：Species Distribution and Climate Change、Environmental DNA in Biodiversity Studies、Data Analysis with R

Large-scale, volunteer-collected datasets of community-identified natural world imagery like iNaturalist have enabled marked performance gains for fine-grained visual classification of species using machine learning methods. However, such data---sometimes referred to as citizen science data---are opportunistic and lack a structured sampling strategy. This volunteer-collected biodiversity data contains geographic, temporal, taxonomic, observers, and sociopolitical biases that can have significant effects on biodiversity model performance, but whose impacts are unclear for fine-grained species recognition performance. Here we introduce Diversity Shift (DivShift), a framework for quantifying the effects of domain-specific distribution shifts on machine learning model performance. To diagnose the performance effects of biases specific to volunteer-collected biodiversity data, we also introduce DivShift - North American West Coast (DivShift-NAWC), a curated dataset of almost 7.5 million iNaturalist images across the western coast of North America partitioned across five types of expert-verified bias. We compare species recognition performance across these bias partitions using a diverse variety of species- and ecosystem-focused accuracy metrics. We observe that these biases confound model performance less than expected from the underlying label distribution shift, and that more data leads to better model performance but the magnitude of these improvements are bias-specific. These ...