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.

翻译：地理空间基础模型（GFMs）是一种快速兴起的范式，适用于多种地理空间任务，如生态制图。然而，对于高价值应用场景，GFMs的效用尚未得到充分探索。本研究旨在探讨将GFMs应用于生态用途的效用、挑战与机遇。为此，我们在三个应用案例中对多个预训练AI模型（即Prithvi-E0-2.0和TerraMind）进行微调，并与基线ResNet-101模型进行比较。首先，我们展示了TerraMind的土地利用与土地覆盖生成能力。最后，我们探索了GFMs在森林功能性状制图和泥炭地检测中的效用。在所有实验中，GFMs均优于基线ResNet模型。总体而言，TerraMind略优于Prithvi。然而，在引入额外模态数据时，TerraMind显著优于基线ResNet和Prithvi模型。尽管如此，仍需考虑输入数据与预训练模态之间的差异。我们注意到，这些模型将受益于更高分辨率和更准确的标签，特别是在需要绘制像素级动态变化的应用场景中。