mlsanityData to 14 Oct 2026

EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis

arXiv 2503.15625 ↗First posted 19 Mar 2025cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 77 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

29 of 77 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  5. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  7. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  8. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
  9. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  10. 2021Multiscale Vision Transformerscited by 897
  11. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 408
  12. 2017Functional Map of the Worldcited by 338
17 more
  1. 2020Keystroke Dynamics as Part of Lifeloggingcited by 334
  2. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 277
  3. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 234
  4. 2018SpaceNet: A Remote Sensing Dataset and Challenge Seriescited by 179
  5. 2018xView: Objects in Context in Overhead Imagerycited by 146
  6. 2024Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observationcited by 104
  7. 2019SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusioncited by 95
  8. 2024Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagerycited by 83
  9. 2023Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentationcited by 76
  10. 2024OmniSat: Self-Supervised Modality Fusion for Earth Observationcited by 63
  11. 2024Bridging Remote Sensors with Multisensor Geospatial Foundation Modelscited by 46
  12. 2024CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approachcited by 38
  13. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  14. 2025Panopticon: Advancing Any-Sensor Foundation Models for Earth Observationcited by 34
  15. 2022Multimodal contrastive learning for remote sensing taskscited by 18
  16. 2024QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1cited by 8
  17. 2024ViTCN: Vision Transformer Contrastive Network For Reasoningcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Modelscited by 90
  2. 2026EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agentscited by 1
  3. 2026Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?cited by 0
  4. 2026SynCo: Learning Cross-Modal Synergy by Contrasting Interaction Residualscited by 0