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%)..., , # Mapped built infrastructure (MBI)

## Description of the data and file structure

These data are annual maps of built infrastructure, with six classes, spanning the Snake River Plain ecoregion in southern Idaho. These products are ready-to-use, and can be imported into any geospatial software for analyses. These data were generated from a fusion of Sentinel-1 radar and Sentinel-2 multispectral imagery. The final MBI products are annual raster data types, that is pixelated, categorical data with 6 categories or classes; 1. Residential, 2. Infrastructure, 3. Paved, 4. Agriculture, 5. Vegetation, and 6. Range/Scrub.

If a user wants to generate these products themselves, or reproduce these products for a similar area, then Google Earth Engine and QGIS is required. The user must have an account with Google Earth Engine (GEE), load the MBI scripts into their repository, and run the code. For applying this model outside of the Snake River Plain Level III ecoregion, new training data must be...,