Breaking new ground in mapping human settlements from space -The Global Urban Footprint-
Thomas Esch, Wieke Heldens, Andreas Hirne, Manfred Keil, Mattia, Marconcini, Achim Roth, Julian Zeidler, Stefan Dech, Emanuele Strano

TL;DR
The paper introduces the Global Urban Footprint, a high-resolution, globally comprehensive map of human settlements derived from radar satellite data, enabling advanced analysis of urbanization patterns.
Contribution
It presents a fully automated processing framework to generate a detailed, high-resolution global settlement map, improving rural settlement representation and accuracy over existing datasets.
Findings
Achieved an overall accuracy of about 85%.
Provided a global settlement map at 0.4 arcsec resolution.
Demonstrated the map's utility for urbanization and vulnerability studies.
Abstract
Today 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70 percent will be living in cities. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec () that provides - for the first time - a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework - the Urban Footprint Processor (UFP) - that was used to analyze a global coverage of more…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRemote Sensing and Land Use · Impact of Light on Environment and Health · Remote-Sensing Image Classification
