Predicting Future Built-Up Land Cover from a Yearly Time Series of Satellite-Derived Binary Urban Maps

Francis D. O’Neill (), Nicole M. Wayant and Sarah J. Becker
Additional contact information
Francis D. O’Neill: Geospatial Research Laboratory, Engineer Research & Development Center, U.S. Army Corps of Engineers, 7701 Telegraph Road, Alexandria, VA 22315-3864, USA
Nicole M. Wayant: Geospatial Research Laboratory, Engineer Research & Development Center, U.S. Army Corps of Engineers, 7701 Telegraph Road, Alexandria, VA 22315-3864, USA
Sarah J. Becker: Geospatial Research Laboratory, Engineer Research & Development Center, U.S. Army Corps of Engineers, 7701 Telegraph Road, Alexandria, VA 22315-3864, USA

Land, 2025, vol. 14, issue 8, 1-18

Abstract: We compare several methods for predicting future built-up land cover using only a short yearly time series of satellite-derived binary urban maps. Existing methods of built-up expansion forecasting often rely on ancillary datasets such as utility networks, distance to transportation nodes, and population density maps, along with remotely sensed aerial or satellite imagery. Such ancillary datasets are not always available and lack the temporal density of satellite imagery. Moreover, existing work often focuses on quantifying the expected volume of built-up expansion, rather than predicting where exactly that expansion will occur. To address these gaps, we evaluate six methods for the creation of prediction maps showing expected areas of future built-up expansion, using yearly built/not-built maps derived from Sentinel-2 imagery as inputs: Cellular Automata, logistic regression, Support Vector Machines, Random Forests, Convolutional Neural Networks (CNNs), and CNNs with the addition of long short-term memory (ConvLSTM). Of these six, we find CNNs to be the best-performing method, with an average Cohen’s kappa score of 0.73 across nine study sites in the continental United States.

Keywords: urban; machine learning; forecasting; built up; deep learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/8/1630/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/8/1630/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:8:p:1630-:d:1723305

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().