Mapping biodiversity at very-high resolution in Europe

This paper describes a cascading multimodal pipeline for high-resolution biodiversity mapping across Europe, integrating species distribution modeling, biodiversity indicators, and habitat classification. The proposed pipeline first predicts species compositions using a deep-SDM, a multimodal model trained on remote sensing, climate time series, and species occurrence data at 50×50m resolution. These predictions are then used to generate biodiversity indicator maps and classify habitats with Pl@ntBERT, a transformer-based LLM designed for species-to-habitat mapping. With this approach, continental-scale species distribution maps, biodiversity indicator maps, and habitat maps are produced, providing fine-grained ecological insights. Unlike traditional methods, this framework enables joint modeling of interspecies dependencies, bias-aware training with heterogeneous presence-absence data, and large-scale inference from multi-source remote sensing inputs.

Recommended citation: Leblanc, C., Picek, L., Palard, R., Deneu, B., Servajean, M., Bonnet, P., & Joly, A. (2025). Mapping biodiversity at very-high resolution in Europe. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 2349-2358).
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