DivShift: Exploring Domain-Specific Distribution Shift in Volunteer-Collected Biodiversity Datasets

• URL: http://arxiv.org/abs/2410.19816v1
• Date: Thu, 17 Oct 2024 23:56:30 GMT
• Title: DivShift: Exploring Domain-Specific Distribution Shift in Volunteer-Collected Biodiversity Datasets
• Authors: Elena Sierra, Lauren E. Gillespie, Salim Soltani, Moises Exposito-Alonso, Teja Kattenborn,
• Abstract summary: We introduce DivShift North American West Coast (DivShift-NAWC), a curated dataset of almost 8 million iNaturalist plant images. We compare model performance across four known biases and observe that they indeed confound model performance. We suggest practical strategies for curating datasets to train deep learning models for monitoring climate change's impacts on the world's biodiversity.
• Score: 0.0
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• Abstract: Climate change is negatively impacting the world's biodiversity. To build automated systems to monitor these negative biodiversity impacts, large-scale, volunteer-collected datasets like iNaturalist are built from community-identified, natural imagery. However, such volunteer-based data are opportunistic and lack a structured sampling strategy, resulting in geographic, temporal, observation quality, and socioeconomic, biases that stymie uptake of these models for downstream biodiversity monitoring tasks. Here we introduce DivShift North American West Coast (DivShift-NAWC), a curated dataset of almost 8 million iNaturalist plant images across the western coast of North America, for exploring the effects of these biases on deep learning model performance. We compare model performance across four known biases and observe that they indeed confound model performance. We suggest practical strategies for curating datasets to train deep learning models for monitoring climate change's impacts on the world's biodiversity.

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