# TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data

arXiv: https://arxiv.org/abs/2504.11172 · HTML: https://mlsanity.com/paper/2504.11172/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [Foundation Models for Generalist Geospatial Artificial Intelligence](https://mlsanity.com/paper/2310.18660/) (2023)
- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [4M: Massively Multimodal Masked Modeling](https://mlsanity.com/paper/2312.06647/) (2023)
- [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)
- [BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://mlsanity.com/paper/2105.07921/) (2021)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning](https://mlsanity.com/paper/2405.02771/) (2024)
- [AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities](https://mlsanity.com/paper/2412.14123/) (2024)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Bridging Remote Sensors with Multisensor Geospatial Foundation Models](https://mlsanity.com/paper/2404.01260/) (2024)
- [Major TOM: Expandable Datasets for Earth Observation](https://mlsanity.com/paper/2402.12095/) (2024)
- [So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification](https://mlsanity.com/paper/1912.12171/) (2019)
- [SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updated](https://mlsanity.com/paper/2503.00168/) (2025)

## Built on by (most cited first, 14 of 14)

- [The Moon's Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction](https://mlsanity.com/paper/2505.05644/) (2025)
- [AQUAH: Automatic Quantification and Unified Agent in Hydrology](https://mlsanity.com/paper/2508.02936/) (2025)
- [EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data](https://mlsanity.com/paper/2602.12177/) (2026)
- [TerraFlow: Multimodal, Multitemporal Representation Learning for Earth Observation](https://mlsanity.com/paper/2603.12762/) (2026)
- [SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining](https://mlsanity.com/paper/2605.21075/) (2026)
- [SARLO-80: Worldwide Slant SAR Language Optic Dataset 80cm](https://mlsanity.com/paper/2606.20523/) (2026)
- [Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning](https://mlsanity.com/paper/2510.10068/) (2025)
- [Grazing Detection using Deep Learning and Sentinel-2 Time Series Data](https://mlsanity.com/paper/2510.14493/) (2025)
- [Cryo-Bench: Benchmarking Foundation Models for Cryosphere Mapping](https://mlsanity.com/paper/2603.01576/) (2026)
- [Quantizing Space and Time: Fusing Time Series and Images for Earth Observation](https://mlsanity.com/paper/2510.23118/) (2025)
- [OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation](https://mlsanity.com/paper/2601.07392/) (2026)
- [Now We Know? A Systematic Comparison of TerraMind and THOR](https://mlsanity.com/paper/2607.18504/) (2026)
- [MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery](https://mlsanity.com/paper/2609.15676/) (2026)
- [GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation](https://mlsanity.com/paper/2609.37496/) (2026)

## Abstract (arXiv)

Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public datasets are often limited in scale, geographic coverage, or sensor variety. We introduce TerraMesh, a new globally diverse, multimodal dataset combining optical, synthetic aperture radar, elevation, and land-cover modalities in an Analysis-Ready Data format. TerraMesh includes over 9~million samples with eight spatiotemporal aligned modalities, enabling large-scale pre-training. We provide detailed data processing steps, comprehensive statistics, and empirical evidence demonstrating improved model performance when pre-trained on TerraMesh. The dataset is hosted at https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh.