EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912945956454400 |
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| author | Massey, Matthew Munia, Nusrat Imran, Abdullah-Al-Zubaer |
| author_facet | Massey, Matthew Munia, Nusrat Imran, Abdullah-Al-Zubaer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15625 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis Massey, Matthew Munia, Nusrat Imran, Abdullah-Al-Zubaer Computer Vision and Pattern Recognition 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. |
| title | EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15625 |