The Multi-Temporal Urban Development SpaceNet Dataset
This dataset addresses the critical need for quantifying population statistics and assessing urbanization for human development and disaster response applications, particularly for countries lacking effective Civil Registration systems.
The Multi-Temporal Urban Development SpaceNet (MUDS) dataset, also known as SpaceNet 7, was created to address the lack of effective Civil Registration systems in over 100 countries. It provides 24 monthly satellite images covering over 100 unique geographies, totaling more than 40,000 km2 of imagery with over 11 million building footprint annotations, each with a unique identifier for temporal tracking. The dataset enables tracking of building construction and demolition over time, directly assessing urbanization.
Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that over 100 countries currently lack effective Civil Registration systems. To help address this deficit and develop novel computer vision methods for time series data, we present the Multi-Temporal Urban Development SpaceNet (MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics, which includes 24 images (one per month) covering >100 unique geographies, and comprises >40,000 km2 of imagery and exhaustive polygon labels of building footprints therein, totaling over 11M individual annotations. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. Label fidelity exceeds image resolution; this "omniscient labeling" is a unique feature of the dataset, and enables surprisingly precise algorithmic models to be crafted. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization. Performance is measured with the newly developed SpaceNet Change and Object Tracking (SCOT) metric, which quantifies both object tracking as well as change detection. We demonstrate that despite the moderate resolution of the data, we are able to track individual building identifiers over time. This task has broad implications for disaster preparedness, the environment, infrastructure development, and epidemic prevention.