Multi-Scale and Multimodal Species Distribution Modeling
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916470412279808 |
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| author | van Tiel, Nina Zbinden, Robin Dalsasso, Emanuele Kellenberger, Benjamin Pellissier, Loïc Tuia, Devis |
| author_facet | van Tiel, Nina Zbinden, Robin Dalsasso, Emanuele Kellenberger, Benjamin Pellissier, Loïc Tuia, Devis |
| contents | Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (environmental rasters, satellite images) as model predictors, allowing the model to consider the spatial context around each species' observations. However, the appropriate spatial extent of the images is not straightforward to determine and may affect the performance of the model, as scale is recognized as an important factor in SDMs. We develop a modular structure for SDMs that allows us to test the effect of scale in both single- and multi-scale settings. Furthermore, our model enables different scales to be considered for different modalities, using a late fusion approach. Results on the GeoLifeCLEF 2023 benchmark indicate that considering multimodal data and learning multi-scale representations leads to more accurate models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Scale and Multimodal Species Distribution Modeling van Tiel, Nina Zbinden, Robin Dalsasso, Emanuele Kellenberger, Benjamin Pellissier, Loïc Tuia, Devis Machine Learning Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (environmental rasters, satellite images) as model predictors, allowing the model to consider the spatial context around each species' observations. However, the appropriate spatial extent of the images is not straightforward to determine and may affect the performance of the model, as scale is recognized as an important factor in SDMs. We develop a modular structure for SDMs that allows us to test the effect of scale in both single- and multi-scale settings. Furthermore, our model enables different scales to be considered for different modalities, using a late fusion approach. Results on the GeoLifeCLEF 2023 benchmark indicate that considering multimodal data and learning multi-scale representations leads to more accurate models. |
| title | Multi-Scale and Multimodal Species Distribution Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.04016 |