构建多模态地质制图数据集，助力地表分析与模型泛化
EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis
- 整合高程、影像与矢量数据，建立可复现的地质制图流水线
- 地形特征预测力最强，跨区域评估中原始光谱/高程数据性能下降明显
- 适合做多模态融合、领域自适应与地表建模的研究者使用
表面地质（SG）地图对理解地表过程和支撑基础设施规划至关重要，但现有工作流程劳动密集且难以扩展。我们提出 EarthScape，一个面向 AI 的多模态数据集，集成数字高程模型、航空影像、多尺度地形特征以及水文与基础设施矢量数据，构建统一、可复现的制图流程。报告了单模态、多尺度及多模态配置下的基线基准测试结果。实验表明，地形特征提供最可靠的预测信号，而原始光谱与高程输入在跨区域评估中性能显著下降。EarthScape 提供地理上紧凑但模态丰富的基准，适用于多模态融合、领域自适应与地表建模研究。数据集可通过 https://uknowledge.uky.edu/kgs_data/16/ 直接下载，代码见 https://github.com/masseygeo/earthscape。
原文摘要 · Abstract (English)
Surficial geologic (SG) maps are essential for understanding surface processes and supporting infrastructure planning, but current workflows are labor-intensive and difficult to scale. We introduce EarthScape, an AI-ready multimodal dataset for SG mapping that integrates digital elevation models, aerial imagery, multi-scale terrain features, and hydrologic and infrastructure vector data within a unified, reproducible pipeline. We report baseline benchmarks across single-modality, multi-scale, and multimodal configurations. Our experiments show that terrain features provide the most reliable predictive signal, while raw spectral and elevation inputs degrade substantially under cross-region evaluation. EarthScape offers a geographically compact, but modality-rich benchmark for multimodal fusion, domain adaptation, and surface modeling. EarthScape is available for direct download at https://uknowledge.uky.edu/kgs_data/16/, and code is available at https://github.com/masseygeo/earthscape.
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