AnySat：适用于任何分辨率、比例和模态的地球观测模型

AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities

December 18, 2024

作者: Guillaume Astruc, Nicolas Gonthier, Clement Mallet, Loic Landrieu

cs.AI

摘要

地理空间模型必须适应地球观测数据的多样性，包括分辨率、尺度和模态。然而，现有方法期望固定的输入配置，这限制了它们的实际适用性。我们提出了AnySat，这是一种基于联合嵌入预测架构（JEPA）和分辨率自适应空间编码器的多模态模型，使我们能够以自监督方式在高度异构数据上训练单一模型。为了展示这一统一方法的优势，我们编制了GeoPlex，这是一个包含5个多模态数据集和11个不同传感器的集合。然后，我们同时在这些多样化的数据集上训练一个强大的单一模型。经过微调后，我们在GeoPlex数据集和其他4个额外数据集上的5个环境监测任务（土地覆盖映射、树木种类识别、作物类型分类、变化检测和洪水分割）中取得了更好或接近最先进的结果。代码和模型可在https://github.com/gastruc/AnySat 上找到。

English

Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurations, which limits their practical applicability. We propose AnySat, a multimodal model based on joint embedding predictive architecture (JEPA) and resolution-adaptive spatial encoders, allowing us to train a single model on highly heterogeneous data in a self-supervised manner. To demonstrate the advantages of this unified approach, we compile GeoPlex, a collection of 5 multimodal datasets with varying characteristics and 11 distinct sensors. We then train a single powerful model on these diverse datasets simultaneously. Once fine-tuned, we achieve better or near state-of-the-art results on the datasets of GeoPlex and 4 additional ones for 5 environment monitoring tasks: land cover mapping, tree species identification, crop type classification, change detection, and flood segmentation. The code and models are available at https://github.com/gastruc/AnySat.