A Multi-Modal Satellite Imagery Dataset for Public Health Analysis in Colombia

We introduce a cost-effective public health analysis solution for low- and
middle-income countries--the Multi-Modal Satellite Imagery Dataset in
Colombia. By leveraging high-quality, spatiotemporally aligned satellite
images and corresponding metadata, the dataset integrates economic,
demographic, meteorological, and epidemiological data. Employing a single
forwards and a forward-backward technique ensures clear satellite images with
minimal cloud cover for every epi-week, significantly enhancing overall data
quality. The extraction process utilizes the satellite extractor package
powered by the SentinelHub API, resulting in a comprehensive dataset of 12,636
satellite images from 81 municipalities in Colombia between 2016 and 2018,
along with relevant metadata. Beyond expediting public health data analysis
across diverse locations and timeframes, this versatile framework consistently
captures multimodal features. Its applications extend to various realms in
multimodal AI, encompassing deforestation monitoring, forecasting education
indices, water quality assessment, tracking extreme climatic events,
addressing epidemic illnesses, and optimizing precision agriculture.