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

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.