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Cities play an important role in achieving sustainable development goals(SDGs) to promote economic growth and meet social needs. Especially satelliteimagery is a potential data source for studying sustainable urban development.However, a comprehensive dataset in the United States (U.S.) covering multiplecities, multiple years, multiple scales, and multiple indicators for SDGmonitoring is lacking. To support the research on SDGs in U.S. cities, wedevelop a satellite imagery dataset using deep learning models for five SDGscontaining 25 sustainable development indicators. The proposed dataset coversthe 100 most populated U.S. cities and corresponding Census Block Groups from2014 to 2023. Specifically, we collect satellite imagery and identify objectswith state-of-the-art object detection and semantic segmentation models toobserve cities' bird's-eye view. We further gather population, nighttime light,survey, and built environment data to depict SDGs regarding poverty, health,education, inequality, and living environment. We anticipate the dataset tohelp urban policymakers and researchers to advance SDGs-related studies,especially applying satellite imagery to monitor long-term and multi-scale SDGsin cities.

A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities