ArticleProceedings of the National Academy of Sciences of the United States of America2024
Deep learning models map rapid plant species changes from citizen science and remote sensing data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Risk prediction of echinococcosis in China under climate change: a One Health approach.Parasites & vectors · 2026Article
- China's "greening but weakening" protected areas reveal a global conservation quality-quantity dilemma.Science China. Life sciences · 2026Article
- Migrating in a Warming World: A Deep Learning Approach to Predict Pan-American Seasonal Shifts in the Monarch Butterfly Niche.Global change biology · 2026Article
- Trait mediation explains decadal distributional shifts for a wide range of insect taxa.Nature communications · 2025Article
- The pipelines of deep learning-based plant image processing.Quantitative plant biology · 2025Review
- Deep learning sharpens vistas on biodiversity mapping.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep learning models map rapid plant species changes from citizen science and remote sensing data.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Abstract
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model-
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