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.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.