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

arXiv: https://arxiv.org/abs/2503.15625 · HTML: https://mlsanity.com/paper/2503.15625/

## 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 (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [Functional Map of the World](https://mlsanity.com/paper/1711.07846/) (2017)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation](https://mlsanity.com/paper/2403.15356/) (2024)
- [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](https://mlsanity.com/paper/1906.07789/) (2019)
- [Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2403.05419/) (2024)
- [Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2312.12470/) (2023)
- [OmniSat: Self-Supervised Modality Fusion for Earth Observation](https://mlsanity.com/paper/2404.08351/) (2024)
- [Bridging Remote Sensors with Multisensor Geospatial Foundation Models](https://mlsanity.com/paper/2404.01260/) (2024)
- [CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approach](https://mlsanity.com/paper/2406.10581/) (2024)
- [Visual Attention Methods in Deep Learning: An In-Depth Survey](https://mlsanity.com/paper/2204.07756/) (2022)
- [Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.10845/) (2025)
- [Multimodal contrastive learning for remote sensing tasks](https://mlsanity.com/paper/2209.02329/) (2022)
- [QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1](https://mlsanity.com/paper/2403.18116/) (2024)
- [ViTCN: Vision Transformer Contrastive Network For Reasoning](https://mlsanity.com/paper/2403.09962/) (2024)

## Built on by (most cited first, 4 of 4)

- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents](https://mlsanity.com/paper/2605.01250/) (2026)
- [Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?](https://mlsanity.com/paper/2607.13192/) (2026)
- [SynCo: Learning Cross-Modal Synergy by Contrasting Interaction Residuals](https://mlsanity.com/paper/2609.32846/) (2026)

## 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.