EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis
This dataset addresses the need for AI-ready resources in Earth sciences, supporting applications in climate change and resource management, but it is incremental as it focuses on data creation rather than new methods.
The authors tackled the problem of labor-intensive surficial geologic mapping by introducing EarthScape, a novel multimodal dataset that integrates aerial imagery, terrain features, and annotations for seven geologic classes, establishing baseline benchmarks to demonstrate its utility.
Surficial geologic mapping is essential for understanding Earth surface processes, addressing modern challenges such as climate change and national security, and supporting common applications in engineering and resource management. However, traditional mapping methods are labor-intensive, limiting spatial coverage and introducing potential biases. To address these limitations, we introduce EarthScape, a novel, AI-ready multimodal dataset specifically designed for surficial geologic mapping and Earth surface analysis. EarthScape integrates high-resolution aerial RGB and near-infrared (NIR) imagery, digital elevation models (DEM), multi-scale DEM-derived terrain features, and hydrologic and infrastructure vector data. The dataset provides detailed annotations for seven distinct surficial geologic classes encompassing various geological processes. We present a comprehensive data processing pipeline using open-sourced raw data and establish baseline benchmarks using different spatial modalities to demonstrate the utility of EarthScape. As a living dataset with a vision for expansion, EarthScape bridges the gap between computer vision and Earth sciences, offering a valuable resource for advancing research in multimodal learning, geospatial analysis, and geological mapping. Our code is available at https://github.com/masseygeo/earthscape.