Evidence Hub

fMoW-ERA5: A Tri-Modal Satellite Image Dataset with Textual and Environmental Metadata for Diffusion-Based Generation

Nikolaos Stefanos Kostagiolas, Pantelis Georgiades

Zenodo (CERN European Organization for Nuclear Research), 2026

Abstract

This dataset accompanies the paper "Environment-Aware Satellite Image Generation with Diffusion Models" and provides one of the first publicly available tri-modal annotation files for satellite imagery, combining textual scene descriptions with both static geographic metadata and continuous numerical environmental metadata derived from the ERA5 climate reanalysis. The dataset is released as a JSON Lines annotation file (.jsonl) designed for use in conjunction with the fMoW-RGB dataset (Functional Map of the World, Christie et al. 2018, https://github.com/fMoW/dataset), which must be downloaded separately under its own license. Each record in this file corresponds to one fMoW image and contains the following fields: file_name: filename of the corresponding fMoW image text: scene caption of the form "a satellite image of a in during , " lon, lat: centroid coordinates of the depicted scene year, month, day: image capture date gsd: ground sampling distance (m/pixel) t2: 2 m temperature, 5-day average (K) [ERA5: 2t] tp: total precipitation, 5-day average (mm) [ERA5: tp] 10u: 10 m U wind component, 5-day average (m/s) [ERA5: 10u] 10v: 10 m V wind component, 5-day average (m/s) [ERA5: 10v] ssr: surface net solar radiation, 5-day sum (J/m²) [ERA5: ssr] tcc: total cloud cover, instantaneous (0–1) [ERA5: tcc] t2d: 2 m dewpoint temperature, 5-day average (K) [ERA5: 2d] ERA5 variables were retrieved from the Copernicus Climate Data Store (CDS) using the cdsapi Python library in NetCDF…

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free