Geospatial
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.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.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.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.