TL;DR
The paper introduces the Multi-Temporal Urban Development SpaceNet dataset, a comprehensive satellite imagery dataset with detailed building labels over time, enabling advanced urban change detection and tracking for various societal applications.
Contribution
It provides a large-scale, high-fidelity, time series satellite dataset with unique building identifiers, facilitating novel methods for urban change analysis.
Findings
Successful tracking of individual buildings over time.
Demonstrated urban change detection using moderate-resolution imagery.
Introduced the SCOT metric for evaluating change and object tracking.
Abstract
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…
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