An AI-ready dataset for the Urban Heat Island (UHI) analysis in Guatemala City integrates multi-source geospatial and socioeconomic data to enable predictive modelling and decision support for climate resilience.

The dataset combines municipal records (land use, population density, road networks, building height and age, poverty indicators) with remote sensing products from MODIS, Sentinel, and Copernicus (land surface temperature, NDVI, NDWI, albedo, nightlights), and topographic data (DEM, slopes). It includes temporal projections of heatwave intensity, green infrastructure metrics, and vulnerability classifications validated with stakeholders.

Structured into multiple blocks, the dataset supports machine learning workflows for identifying UHI hotspots, simulating mitigation scenarios, and optimising nature-based solutions such as reforestation and urban greening.

For the released dataset, we are working hard to ensure full compliance with FAIR data principles, including versioning, provenance, licensing, and documentation. If you are interested in the dataset or potential collaboration, please get in touch with the AETHER team.