Continental scale habitat modelling with artificial intelligence and multimodal earth observation

• URL: http://arxiv.org/abs/2507.09732v1
• Date: Sun, 13 Jul 2025 18:11:26 GMT
• Title: Continental scale habitat modelling with artificial intelligence and multimodal earth observation
• Authors: Sara Si-Moussi, Stephan Hennekens, Sander Mucher, Stan Los, Wilfried Thuiller,
• Abstract summary: Habitats integrate the abiotic conditions and biophysical structures that support biodiversity and sustain nature's contributions to people.<n>Current maps often fall short in thematic or spatial resolution because they must model several mutually exclusive habitat types.<n>Here, we evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat classification over large geographic extents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Habitats integrate the abiotic conditions and biophysical structures that support biodiversity and sustain nature's contributions to people. As these ecosystems face mounting pressure from human activities, accurate, high-resolution habitat maps are essential for effective conservation and restoration. Yet current 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 complicate multi-class training. Here, we evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat classification over large geographic extents at fine thematic resolution. Using vegetation plots from the European Vegetation Archive, we modelled Level 3 EUNIS habitats across Europe and assessed multiple modelling strategies against independent validation datasets. Strategies that exploited the hierarchical nature of habitat nomenclatures resolved classification ambiguities, especially in fragmented landscapes. Integrating multi-spectral (MSI) and synthetic aperture radar (SAR) imagery, particularly through Earth Observation Foundation models, enhanced within-formation discrimination and overall performance. Finally, ensemble machine learning that corrects class imbalance boosted accuracy further. Our methodological framework is transferable beyond Europe and adaptable to other classification systems. Future research should advance temporal modelling of dynamic habitats, extend to habitat segmentation and quality assessment, and exploit next-generation EO data paired with higher-quality in-situ observations.

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