LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909772940312576 |
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| author | Sastry, Srikumar Xing, Xin Dhakal, Aayush Khanal, Subash Ahmad, Adeel Jacobs, Nathan |
| author_facet | Sastry, Srikumar Xing, Xin Dhakal, Aayush Khanal, Subash Ahmad, Adeel Jacobs, Nathan |
| contents | We focus on species distribution modeling using global-scale presence-only data, leveraging geographical and environmental features to map species ranges, as in previous studies. However, we innovate by integrating taxonomic classification into our approach. Specifically, we propose using a large language model to extract a latent representation of the taxonomic classification from a textual prompt. This allows us to map the range of any taxonomic rank, including unseen species, without additional supervision. We also present a new proximity-aware evaluation metric, suitable for evaluating species distribution models, which addresses critical shortcomings of traditional metrics. We evaluated our model for species range prediction, zero-shot prediction, and geo-feature regression and found that it outperforms several state-of-the-art models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08334 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LD-SDM: Language-Driven Hierarchical Species Distribution Modeling Sastry, Srikumar Xing, Xin Dhakal, Aayush Khanal, Subash Ahmad, Adeel Jacobs, Nathan Computer Vision and Pattern Recognition We focus on species distribution modeling using global-scale presence-only data, leveraging geographical and environmental features to map species ranges, as in previous studies. However, we innovate by integrating taxonomic classification into our approach. Specifically, we propose using a large language model to extract a latent representation of the taxonomic classification from a textual prompt. This allows us to map the range of any taxonomic rank, including unseen species, without additional supervision. We also present a new proximity-aware evaluation metric, suitable for evaluating species distribution models, which addresses critical shortcomings of traditional metrics. We evaluated our model for species range prediction, zero-shot prediction, and geo-feature regression and found that it outperforms several state-of-the-art models. |
| title | LD-SDM: Language-Driven Hierarchical Species Distribution Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.08334 |