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Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification

Dolman, Megan R.1; Kolarik, Nicholas E.1; Caughlin, T. Trevor1; Brandt, Jodi S.1; Som Castellano, Rebecca L.1; Cattau, Megan E.1

Published Apr 10, 2025 on Dryad. https://doi.org/10.5061/dryad.mcvdnckb3

Data files

Apr 10, 2025 version files 972.37 MB

Abstract

Accurate land use land cover (LULC) maps that delineate built infrastructure are useful for numerous applications, from urban planning, humanitarian response, disaster management, to informing decision making for reducing human exposure to natural hazards, such as wildfire. Existing products lack sufficient spatial, temporal, and thematic resolution, omitting critical information needed to capture LULC trends accurately over time. Advancements in remote sensing imagery, open-source software and cloud computing offer opportunities to address these challenges. Using Google Earth Engine, we developed a novel built infrastructure detection method in semi-arid systems by applying a random forest classifier to a fusion of Sentinel-1 and Sentinel-2 time series. Our classifier performed well, differentiating three built environment types: residential, infrastructure, and paved, with overall accuracies ranging from 90 to 96%. Producer accuracies were highest for the infrastructure class (98–99%), followed by the residential class (91–96%). Sentinel-1 variables were important for differentiating built classes. We illustrated the utility of our mapped products by generating a time-series of change across southern Idaho spanning 2015 to 2024 and comparing this with publicly available products: National Land Cover Database (NLCD), Microsoft Building Footprints (MBF) and the global Dynamic World (DW). For 2024, our product estimated 5.88% of the study area as built, aligning closely with NLCD (6%) and DW (4.64%). Our mapped built infrastructure products offer enhancements over NLCD spatially and temporally, over DW thematically, and over MBF both temporally and thematically. We demonstrate the potential of fusing data sources to improve LULC mapping and present a case for regionally parameterized models that can more accurately capture built infrastructure change over time. We used open-source approaches for built infrastructure detection, aiming for broader adoption of this workflow across other ecosystems and environments to support decision-making.