用于美国城市长期可持续发展的卫星图像数据集
A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities
AI总结：
针对美国城市缺乏多维度可持续发展目标（SDG）监测数据集的问题，本文利用深度学习模型开发了一个覆盖100个城市、2014至2023年的卫星图像数据集，包含5个SDG和25个指标，以支持长期多尺度的城市SDG研究。
摘要：
城市在实现可持续发展目标（SDGs）以促进经济增长和满足社会需求方面发挥着重要作用。特别是卫星图像，是研究城市可持续发展的潜在数据源。然而，目前缺乏一个覆盖美国多个城市、多个年份、多个尺度以及多个SDG监测指标的综合数据集。为了支持美国城市SDG研究，我们利用深度学习模型开发了一个卫星图像数据集，涵盖5个SDG和25个可持续发展指标。该数据集覆盖了2014至2023年间美国人口最多的100个城市及其相应的人口普查街区组。具体而言，我们收集卫星图像，并使用最先进的物体检测和语义分割模型识别物体，以观察城市的鸟瞰图。我们进一步收集人口、夜间灯光、调查和建成环境数据，以描绘关于贫困、健康、教育、不平等和居住环境的SDGs。我们期望该数据集能帮助城市政策制定者和研究人员推进SDG相关研究，特别是应用卫星图像监测城市中长期和多尺度的SDGs。
英文摘要：
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.
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