The Multi-Temporal Urban Development SpaceNet Dataset

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 Ba...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-02
Main Authors: Adam Van Etten, Hogan, Daniel, Martinez-Manso, Jesus, Shermeyer, Jacob, Weir, Nicholas, Lewis, Ryan
Format: Article
Language:English
Subjects:
Annotations
Change detection
Computer vision
Datasets
Demolition
Disaster management
Emergency preparedness
Footprints
Image resolution
Labels
Mosaics
Population statistics
Satellite imagery
Sustainable development
System effectiveness
Time series
Tracking
Uniqueness
Urban development
Urbanization
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422