PDF(Pubmed)
Abstract：
In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health.
摘要：
在低收入和中等收入国家，与传统数据收集相关的巨大成本对促进公共卫生领域的决策构成了障碍。卫星图像提供了一个潜在的解决方案，但是图像提取和分析成本很高，需要专门的专业知识。我们介绍SatelliteBench,用于卫星图像提取和矢量嵌入生成的可扩展框架。我们还提出了一种新颖的多模态融合管道，该管道利用了一系列卫星图像和元数据。该框架进行了评估，生成了一个数据集，该数据集包含12,636张图像和嵌入，并附有全面的元数据，从2016年至2018年哥伦比亚的81个城市。然后在3个任务中评估数据集：包括登革热病例预测，贫困评估，和受教育的机会。性能展示了SatelliteBench的多功能性和实用性，提供可复制的，可访问和开放的工具，以加强公共卫生决策。
