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.

本报告推介一款针对中低收入国家的经济高效公共卫生分析解决方案——哥伦比亚多模态卫星影像数据集。该数据集通过利用高质量、时空对齐的卫星影像及其相应的元数据，整合了经济、人口、气象及流行病学数据。采用单一正向与正向-反向技术，确保了每个流行周均拥有尽可能少的云层覆盖的清晰卫星影像，显著提升了整体数据质量。提取过程利用了由SentinelHub API驱动的卫星提取器包，从而构建了一个包含2016年至2018年间哥伦比亚81个市镇的12,636张卫星影像的全面数据集，并附有相关元数据。此灵活框架不仅加速了不同地点和时间框架下的公共卫生数据分析，而且持续捕捉多模态特征。其应用范围广泛，涵盖多模态AI的多个领域，包括森林砍伐监测、教育指数预测、水质评估、极端气候事件追踪、流行病应对以及精准农业优化。