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

TL;DR

This paper introduces a comprehensive satellite imagery dataset for 100 major U.S. cities from 2014 to 2023, enabling long-term, multi-scale monitoring of sustainable development goals using deep learning and diverse urban indicators.

Contribution

The paper presents a novel, multi-year satellite imagery dataset covering multiple SDGs and indicators for U.S. cities, integrating deep learning object detection with socioeconomic data.

Findings

Dataset covers 100 cities from 2014 to 2023.

Combines satellite imagery with socioeconomic indicators.

Supports SDG monitoring and urban policy research.

Abstract

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe 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 to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities.