Ecological mapping with geospatial foundation models

• URL: http://arxiv.org/abs/2602.10720v1
• Date: Wed, 11 Feb 2026 10:25:51 GMT
• Title: Ecological mapping with geospatial foundation models
• Authors: Craig Mahlasi, Gciniwe S. Baloyi, Zaheed Gaffoor, Levente Klein, Anne Jones, Etienne Vos, Michal Muszynski, Geoffrey Dawson, Campbell Watson,
• Abstract summary: This study aims to explore the utility, challenges and opportunities associated with the application of GFMs for ecological uses.<n>We fine-tune several pretrained AI models, namely, Prithvi-E0-2.0 and TerraMind, across three use cases, and compare this with a baseline ResNet-101 model.<n>In all experiments, the GFMs outperform the baseline ResNet models.
• Score: 0.8919051099268218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geospatial foundation models (GFMs) are a fast-emerging paradigm for various geospatial tasks, such as ecological mapping. However, the utility of GFMs has not been fully explored for high-value use cases. This study aims to explore the utility, challenges and opportunities associated with the application of GFMs for ecological uses. In this regard, we fine-tune several pretrained AI models, namely, Prithvi-E0-2.0 and TerraMind, across three use cases, and compare this with a baseline ResNet-101 model. Firstly, we demonstrate TerraMind's LULC generation capabilities. Lastly, we explore the utility of the GFMs in forest functional trait mapping and peatlands detection. In all experiments, the GFMs outperform the baseline ResNet models. In general TerraMind marginally outperforms Prithvi. However, with additional modalities TerraMind significantly outperforms the baseline ResNet and Prithvi models. Nonetheless, consideration should be given to the divergence of input data from pretrained modalities. We note that these models would benefit from higher resolution and more accurate labels, especially for use cases where pixel-level dynamics need to be mapped.

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