Imbalance-aware Presence-only Loss Function for Species Distribution Modeling
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929273555648512 |
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| author | Zbinden, Robin van Tiel, Nina Rußwurm, Marc Tuia, Devis |
| author_facet | Zbinden, Robin van Tiel, Nina Rußwurm, Marc Tuia, Devis |
| contents | In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07472 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Imbalance-aware Presence-only Loss Function for Species Distribution Modeling Zbinden, Robin van Tiel, Nina Rußwurm, Marc Tuia, Devis Machine Learning In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations. |
| title | Imbalance-aware Presence-only Loss Function for Species Distribution Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.07472 |