构建全球多模态遥感数据集，支持大规模预训练。
TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data
- 整合光学、雷达、高程等8类遥感数据，覆盖全球900万样本。
- 在多个任务上验证，基于TerraMesh预训练模型性能显著提升。
- 适合遥感、地理信息、环境监测领域研究者使用。
地球观测领域的大型基础模型可通过利用海量无标签数据学习通用且高效的表征。然而，现有公开数据集在规模、地理覆盖范围或传感器多样性方面仍受限。本文提出TerraMesh，一个全球多样、多模态的遥感数据集，包含光学、合成孔径雷达、高程和土地覆盖等多源数据，并以分析就绪数据（Analysis-Ready Data）格式提供。TerraMesh包含超过900万样本，涵盖8个时空对齐的模态，支持大规模预训练。我们提供了详细的数据处理流程、全面的统计信息，以及实证证据，证明在TerraMesh上预训练可显著提升模型性能。该数据集已发布于 https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh。
原文摘要 · Abstract (English)
Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public datasets are often limited in scale, geographic coverage, or sensor variety. We introduce TerraMesh, a new globally diverse, multimodal dataset combining optical, synthetic aperture radar, elevation, and land-cover modalities in an Analysis-Ready Data format. TerraMesh includes over 9~million samples with eight spatiotemporal aligned modalities, enabling large-scale pre-training. We provide detailed data processing steps, comprehensive statistics, and empirical evidence demonstrating improved model performance when pre-trained on TerraMesh. The dataset is hosted at https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。