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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2507.09732 |
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| _version_ | 1866917044160561152 |
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| author | Si-Moussi, Sara Hennekens, Stephan Mucher, Sander Los, Stan Cartier, Yoann Jiménez-Alfaro, Borja Attorre, Fabio Svenning, Jens-Christian Thuiller, Wilfried |
| author_facet | Si-Moussi, Sara Hennekens, Stephan Mucher, Sander Los, Stan Cartier, Yoann Jiménez-Alfaro, Borja Attorre, Fabio Svenning, Jens-Christian Thuiller, Wilfried |
| contents | Habitats integrate the abiotic conditions, vegetation composition and structure that support biodiversity and sustain nature's contributions to people. Most habitats face mounting pressures from human activities, which requires accurate, high-resolution habitat mapping for effective conservation and restoration. Yet, current habitat maps often fall short in thematic or spatial resolution because they must (1) model several mutually exclusive habitat types that co-occur across landscapes and (2) cope with severe class imbalance that complicates exhaustive multi-class training. Here, we evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat mapping across large geographical extents at fine spatial and thematic resolution. Using vegetation plots from the European Vegetation Archive, we modelled the distribution of Level 3 EUNIS habitat types across Europe and assessed multiple modelling strategies against independent validation datasets. Strategies that exploited the hierarchical nature of habitat classifications resolved classification ambiguities, especially in fragmented habitats. Integrating satellite-borne multispectral and radar imagery, particularly through Earth Observation (EO) Foundation models (EO-FMs), enhanced within-formation discrimination and overall performance. Finally, ensemble machine learning that corrects class imbalance boosted predictive accuracy even further. Our methodological framework is transferable beyond Europe and adaptable to other classification systems. Future research should advance temporal modelling of habitat dynamics, extend to habitat segmentation and quality assessment, and exploit next-generation EO data paired with higher-quality in situ observations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09732 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continental-scale habitat distribution modelling with multimodal earth observation foundation models Si-Moussi, Sara Hennekens, Stephan Mucher, Sander Los, Stan Cartier, Yoann Jiménez-Alfaro, Borja Attorre, Fabio Svenning, Jens-Christian Thuiller, Wilfried Machine Learning Populations and Evolution Applications 68T05, 62H35 I.5.4; I.2.6 Habitats integrate the abiotic conditions, vegetation composition and structure that support biodiversity and sustain nature's contributions to people. Most habitats face mounting pressures from human activities, which requires accurate, high-resolution habitat mapping for effective conservation and restoration. Yet, current habitat maps often fall short in thematic or spatial resolution because they must (1) model several mutually exclusive habitat types that co-occur across landscapes and (2) cope with severe class imbalance that complicates exhaustive multi-class training. Here, we evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat mapping across large geographical extents at fine spatial and thematic resolution. Using vegetation plots from the European Vegetation Archive, we modelled the distribution of Level 3 EUNIS habitat types across Europe and assessed multiple modelling strategies against independent validation datasets. Strategies that exploited the hierarchical nature of habitat classifications resolved classification ambiguities, especially in fragmented habitats. Integrating satellite-borne multispectral and radar imagery, particularly through Earth Observation (EO) Foundation models (EO-FMs), enhanced within-formation discrimination and overall performance. Finally, ensemble machine learning that corrects class imbalance boosted predictive accuracy even further. Our methodological framework is transferable beyond Europe and adaptable to other classification systems. Future research should advance temporal modelling of habitat dynamics, extend to habitat segmentation and quality assessment, and exploit next-generation EO data paired with higher-quality in situ observations. |
| title | Continental-scale habitat distribution modelling with multimodal earth observation foundation models |
| topic | Machine Learning Populations and Evolution Applications 68T05, 62H35 I.5.4; I.2.6 |
| url | https://arxiv.org/abs/2507.09732 |