datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TerraMesh
TerraMesh
A planetary‑scale, multimodal analysis‑ready dataset for Earth‑Observation foundation models: TerraMesh merges data from Sentinel‑1 SAR, Sentinel‑2 optical, Copernicus DEM, NDVI, and land‑cover sources into more than 9 million co‑registered patches ready for large‑scale representation learning.
You find more information about the data sampling and preprocessing in our paper: TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data.
Samples from the TerraMesh… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh.geospatialLandslide4sense
Landslide4Sense
Dataset Description
This dataset is originally introduced in GitHub repo Landslide4Sense-2022.
The Landslide4Sense dataset has three splits, training/validation/test, consisting of 3799, 245, and 800 image patches, respectively. Each image patch is a composite of 14 bands that include:
Multispectral data from Sentinel-2: B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12.
Slope data from ALOS PALSAR: B13.
Digital elevation model (DEM) from ALOS… See the full description on the dataset page: https://huggingface.co/datasets/ibm-nasa-geospatial/Landslide4sense.Llama3-SSL4EO-S12-v1.1-captions
Llama3-SSL4EO-S12-Captions
The captions are aligned with the SSL4EO-S12 v1.1 dataset and were automatically generated using the Llama3-LLaVA-Next-8B model.
Please find more information regarding the generation and evaluation in the Llama3-MS-CLIP paper.
Code: https://github.com/IBM/MS-CLIP
Data Structure
We provide the captions in two versions: As a single compressed Parquet file per split and as CSV files with 256 captions each that match the Zarr Zip files of the… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/Llama3-SSL4EO-S12-v1.1-captions.CoordBench
CoordBench
A unified benchmark suite for evaluating location encoders such as SatCLIP, GeoCLIP, Climplicit, and MIND.
The dataset contains 40 normalized source tables from 13 source families. The paper's evaluation suite
uses 52 datasets and 78 prediction targets drawn from this mirror. The source files previously lived across GitHub,
figshare, GCS, Socrata, Zenodo, and Google Drive.
Intended use
Use the normalized tables to compare coordinate-to-embedding models.… See the full description on the dataset page: https://huggingface.co/datasets/taylor-geospatial/CoordBench.hls_burn_scarsThis dataset contains Harmonized Landsat and Sentinel-2 imagery of burn scars and the associated masks for the years 2018-2021 over the contiguous United States. There are 804 512x512 scenes. Its primary purpose is for training geospatial machine learning models.geospatially_enriched_ndvi
Geospatially Enriched NDVI (16-Day Terra/MODIS)
This dataset transforms raw 16-day MODIS NDVI grids into a per-pixel time series enriched with hierarchical administrative boundaries. It covers every 0.1°×0.1° land pixel worldwide from 2000 onward and is partitioned for efficient bulk download and selective access.
Dataset Contents
Partitioned Parquet filesStored under:
ndvi/
├── year=YYYY/
│ ├── country=Netherlands/
│ │ └── data_0.parquet
│ └── country=India/
│ │ └──… See the full description on the dataset page: https://huggingface.co/datasets/svenmeijboom/geospatially_enriched_ndvi.ImpactMesh-Flood
ImpactMesh-Flood
ImpactMesh is a large-scale multimodal, multitemporal dataset for flood and wildfire mapping, released by IBM, DLR, and the ESA Φ-lab.
It integrates Sentinel-1 SAR, Sentinel-2 optical, Copernicus DEM, and high-quality annotations from Copernicus EMS.
The technical report is released soon. You find the wildfire subset here: https://huggingface.co/datasets/ibm-esa-geospatial/ImpactMesh-Fire.
Features
Multimodal: SAR, optical, DEM… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/ImpactMesh-Flood.multi-temporal-crop-classification
Dataset Card for Multi-Temporal Crop Classification
Dataset Summary
This dataset contains temporal Harmonized Landsat-Sentinel imagery of diverse land cover and crop type classes across the Contiguous United States for the year 2022. The target labels are derived from USDA's Crop Data Layer (CDL). It's primary purpose is for training segmentation geospatial machine learning models.
Dataset Structure
TIFF Files
Each tiff file covers a… See the full description on the dataset page: https://huggingface.co/datasets/ibm-nasa-geospatial/multi-temporal-crop-classification.ImpactMesh-Fire
ImpactMesh-Fire
ImpactMesh is a large-scale multimodal, multitemporal dataset for flood and wildfire mapping, released by IBM, DLR, and the ESA Φ-lab.
It integrates Sentinel-1 SAR, Sentinel-2 optical, Copernicus DEM, and high-quality annotations from Copernicus EMS.
The technical report is released soon. You find the flood subset here: https://huggingface.co/datasets/ibm-esa-geospatial/ImpactMesh-Flood.
Features
Multimodal: SAR, optical, DEM
Multitemporal:… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/ImpactMesh-Fire.hurricane
Data Format Description for Hurricane Evaluation on Prithvi WxC
Overview
To evaluate the performance of Prithvi WxC on hurricanes, the surface and pressure data from the MERRA-2 dataset, comprising 160 variables used in training, is required. The complete evaluation dataset includes 75 different initial conditions for hurricanes that formed in the Atlantic Ocean between 2017 and 2023.
The scientific objective is to assess the zero-shot performance of Prithvi WxC in… See the full description on the dataset page: https://huggingface.co/datasets/ibm-nasa-geospatial/hurricane.MINDSET
MINDSET
MINDSET is the pretraining dataset for MIND, a coordinate-only location encoder distilled from static location encoder teachers and annual AlphaEarth Foundations (AEF) embeddings.
We release the embeddings at the 12.1M training coordinates. The dataset contains 12,099,072 land coordinates in WGS84. Coordinates are dense around cities and not uniformly sampled over land.
The files are in GeoParquet format and can be joined on point_id:
file
grain
rows
columns… See the full description on the dataset page: https://huggingface.co/datasets/taylor-geospatial/MINDSET.BioMassters
Dataset Card for BioMassters for Global Prithvi
Dataset Description
Copied from BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series https://nascetti-a.github.io/BioMasster/
Original Dataset: https://huggingface.co/datasets/nascetti-a/BioMassters
Point of Contact For Updated Dataset: Denys Godwin (dgodwin@clarku.edu)
Dataset Summary
This dataset contains Sentinel-1 SAR and Sentinel-2 MSI imagery of Finnish… See the full description on the dataset page: https://huggingface.co/datasets/ibm-nasa-geospatial/BioMassters.hls_merra2_gppFlux
Dataset Summary:
This dataset consists of Harmonized Landsat and Sentinel-2 multispectral reflectance imagery and MERRA-2 observations centered around eddy covariance flux towers and the corresponding Gross Primary Productivity (GPP) data at the towers. Its purpose is to serve as a finetuning dataset for geospatial foundation models for the task of regressing GPP flux observations from HLS and MERRA-2 data.
Dataset Structure:
The dataset consists of:
(1) HLS 6-band Tiff… See the full description on the dataset page: https://huggingface.co/datasets/ibm-nasa-geospatial/hls_merra2_gppFlux.TerraMesh-Masks
TerraMesh-Masks
TerraMesh-Masks is a dataset for open-vocabulary segmentation of satellite imagery. This dataset provides binary segmentation masks with captions that extend the samples from TerraMesh.
We also provide an human-verfied evaluation benchmark, called TerraMesh-Masks-Eval.
Examples from the training subset:
Usage
Download the data loading code from GitHub and install requirements with pip install -r requirements.txt. For development, you can… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh-Masks.pad-pid-geospatial
Geospatial data use in World Bank PADs and PIDs
The data behind How often do World Bank projects use geospatial data?.
Snapshot 938866a81eab, annotations to 2026-09-25 21:57 UTC. The page shows the same snapshot id.
Headline
Out of: those that use data
Out of: all
Projects
1,165 of 1,932 (60.3%)
1,165 of 2,261 (51.5%)
Documents
1,637 of 3,544 (46.2%)
1,637 of 4,494 (36.4%)
A project uses geospatial data when at least one data mention in the… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/pad-pid-geospatial.gravity-wave-parameterizationData format description for the nonlocal gravity wave parameterization dataset
Data Source
The dataset contains input and output training pairs computed using ECMWF's ERA5. The dataset was computed for the years 2010, 2012, 2014, and 2015. One month (from the validation set) is provided here for testing.
Variables Description
Dimensional variables: 64 latitudes (LAT) and 128 longitudes (LON)
features: background atmospheric state, fixed surface variables lat, lon, surface elevation and u… See the full description on the dataset page: https://huggingface.co/datasets/ibm-nasa-geospatial/gravity-wave-parameterization.ftw-planet
Note: Due to licensing restrictions, the imagery has been removed from the dataset. However the original PlanetScope item IDs and timestamps are made available in index.parquet. Please see the source code for how to download the dataset.
Fields of the Planet (FTP)
Paired PlanetScope SR scenes, two seasonal windows per patch (planting and
harvest), co-registered with Fields of The World field-boundary labels,
across 24 countries and 25 labeled regions.
66,584 patches across 24… See the full description on the dataset page: https://huggingface.co/datasets/taylor-geospatial/ftw-planet.hierarchical-geospatial-reasoningTerraMesh-Masks-Eval
TerraMesh-Masks-Eval
TerraMesh-Masks-Eval is a human-verified benchmark dataset to evaluate open-vocabulary segmentation models on satellite imagery. This dataset provides binary segmentation masks with captions togehther with input samples from TerraMesh.
We also provide a training dataset, called TerraMesh-Masks.
Examples from the evaluation subset:
Usage
Download the data loading code from GitHub and install requirements with pip install -r requirements.txt.… See the full description on the dataset page: https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh-Masks-Eval.ghana-ag-geospatial
Ghana Agricultural Geospatial Layers
Part of the Ghana Agricultural Data Commons v0.2.0 public dataset release by Sinuosity Physical Intelligence Lab. Repository payloads include redistributed source data only where licensing and publication gates allow; other entries are registry, provenance, governance or builder metadata.
This dataset is part of the Ghana Agricultural Data Commons from Sinuosity Physical Intelligence Lab.
GhanaAgData is a systematic, publicly documented… See the full description on the dataset page: https://huggingface.co/datasets/sinuosity/ghana-ag-geospatial.brazil-wildfire-geospatial-dataset
Banco Histórico de Incêndios no Brasil — 2018–2025
Banco de dados com 1,43 milhão de focos de calor registrados no Brasil entre 2018 e 2025, enriquecidos com dados meteorológicos (ERA5), cobertura do solo (MapBiomas) e altitude (SRTM).
Construído a partir de fontes públicas oficiais para suporte a pesquisas científicas sobre incêndios florestais.
Tabelas disponíveis
Tabela
Arquivos
Linhas
Descrição
focos_analise
focos_analise/*.parquet
1.430.756
Tabela… See the full description on the dataset page: https://huggingface.co/datasets/mateus-pcosta/brazil-wildfire-geospatial-dataset.burn_intensity
Dataset Summary
This dataset contains burn scar intensity data and Harmonized Landsat and Sentinel-2 (HLS) images for burn scar analysis across various time frames: pre-burn, during-burn, and post-burn.
Each file provides spatial information on burn scar intensity and top-of-atmosphere (TOA) reflectance values.
The dataset includes:
BS_files_raw.csv: The complete set of burn scar intensity data without filtering.
BS_files_with_less_than_25_percent_zeros.csv: Filtered dataset with… See the full description on the dataset page: https://huggingface.co/datasets/ibm-nasa-geospatial/burn_intensity.DoD-Instruction-8130-01-Installation-of-Geospatial-Information-And-Services
🗺️ DoD Installation Geospatial Information and Services Question-Answer Dataset
Source: DoD Instruction 8130.01
Source Effective Date: April 9, 2015
Change Incorporated: Change 3, effective August 4, 2020
Source Organization: Office of the Under Secretary of Defense for Acquisition and Sustainment
Source Ownership: United States Department of Defense
📋 Overview
Dataset Summary
The DoD Installation Geospatial Information and Services… See the full description on the dataset page: https://huggingface.co/datasets/leeroy-jankins/DoD-Instruction-8130-01-Installation-of-Geospatial-Information-And-Services.neural-earth-fields-hackathon
Neural Earth Fields Hackathon
Land cover
CGLC-MODIS-LCZ-100m.cog.tif is a Cloud Optimized GeoTIFF version of A hybrid 100-m global land cover dataset with Local Climate Zones for WRF by Matthias Demuzere, Cenlin He, Alberto Martilli, and Andrea Zonato (2023).
The source dataset is licensed under CC BY 4.0.
The file contains one uint8 class ID per pixel on the source's nominal 100 m EPSG:4326 grid.
It uses ZSTD compression, 512 × 512 tiles, and mode-resampled… See the full description on the dataset page: https://huggingface.co/datasets/taylor-geospatial/neural-earth-fields-hackathon.geospatial_data_coordinatesshrishtiai-geospatial-dataflorida_geospatialabdullahkhan70_global-street-food-3-continent-geospatial-index
Global Street Food: 3 Continent Geospatial Index
3 Countries street food index: GPS, local prices, and hygiene ratings
Dataset Info
Source: Kaggle
Original Size: 0.11 MB
Kaggle Downloads: 26
Files: 3
Files
mexico_street_food_vendor.csv
pakistan_street_food_vendor.csv
thailand_street_food_vendor.csv
Mirrored from Kaggle
astana_traffic_geospatial_v1
Flowmatic Smart City Dataset
Flowmatic smart-city dataset with hourly UTC CSV partitions. 1 hour file(s), 820 total row(s). Files live under data/hourly/ and are indexed in data/hourly/manifest.json.
Pipeline run: cmqh5oog206mto42sdzqeve6wUpdated: 2026-06-16T21:30:29.003ZPartition scheme: hourly-utcManifest: data/hourly/manifest.json
Hourly CSV layout
Rows are grouped by eventTime into one CSV per UTC hour:
Directory: data/hourly/
File pattern: YYYY-MM-DDTHH.csv… See the full description on the dataset page: https://huggingface.co/datasets/pushthetempo/astana_traffic_geospatial_v1.