DOI: 10.5061/dryad.mcvdnckb3
title: Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification

description: 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.
description: # 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
 generated, and can be created using either QGIS or GEE. All remote sensing
 imagery used for MBI is freely available in GEE. ### Files To
 access MBI for all years:  #### File: 'MBI\_2015-2024.zip'
 **Description:**  A zipped folder containing 30 seperate .tif files for
 the MBI product spanning the Snake River Plain from 2015 to 2024. Each
 raster has 6 classes with this notation: 1. Residential, 2.
 Infrastructure, 3. Paved, 4. Agriculture, 5. Vegetation, and 6.
 Range/Scrub. For each year of the MBI product, there are three seperate
 .tif files (-1.tif, -2.tif, and -3.tif) due to the size of the region.
 These are named RFYY where RF stands for Random Forest which was the
 algorithm used to create the MBI product and "YY" corresponds to
 the last two numbers of a given year:        
 'RF"YY"-1.tif'   (e.g., RF19-1.tif is one of three MBI
 .tif files for the year 2019)         'RF"YY"-2.tif'  
       'RF"YY"-3.tif' To access MBI for only selected
 years: #### File: 'MBI\_20"YY".zip'
 **Description:** 10 seperate zipped folders, one for each year the MBI
 product is available (2015 to 2024). "YY" responds to the last
 two numbers of a given year. Each zipped folder contains three .tif files
 that have the raster MBI product across the Snake River Plain for that
 given year, for example:     'MBI_2021.zip' contains only the
 rasters for the year 2021 and contains three .tif files:          
 'RF21-1.tif           'RF21-2.tif'          
 'RF21-3.tif' ## Sharing/Accessing Information **The ready-to-use
 MBI product can also be accessed here:** 
 [https://drive.google.com/drive/folders/1faKaLfSwEiYhrunrSKAPdpGMEpogGR7V?usp=drive_link](https://drive.google.com/drive/folders/1faKaLfSwEiYhrunrSKAPdpGMEpogGR7V?usp=drive_link)  **Read more about these data here:**  * Dolman et al. (2025) Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification. *Remote Sensing Applications: Society and Environment*, 37, 101472, [https://doi.org/10.1016/j.rsase.2025.101472](https://doi.org/10.1016/j.rsase.2025.101472)  * ArcStoryMaps [https://storymaps.arcgis.com/stories/9a3e71e2b1a7469a8cb997f8a2ee231a](https://storymaps.arcgis.com/stories/9a3e71e2b1a7469a8cb997f8a2ee231a) The MBI products were derived from the following sources: * Sentinel-1 radar: [https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD)  * Sentinel-2 multispectral imagery: [https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED) * Spectral indices calculated from multispectral bands * Topographic variables from National Elevation Dataset ([https://www.usgs.gov/publications/national-elevation-dataset](https://www.usgs.gov/publications/national-elevation-dataset)) * Distance to existing features, specifically roads from US Census TIGER/Line - Roads for each year ([https://catalog.data.gov/dataset/census-tiger-line-roads1](https://catalog.data.gov/dataset/census-tiger-line-roads1)), railways from US Census TIGER/Line Nation Rails Shapefile for each year ([https://catalog.data.gov/dataset/tiger-line-shapefile-2019-nation-u-s-rails-national-shapefile](https://catalog.data.gov/dataset/tiger-line-shapefile-2019-nation-u-s-rails-national-shapefile)), and rivers from National Hydrography Dataset ([https://www.usgs.gov/national-hydrography/national-hydrography-dataset](https://www.usgs.gov/national-hydrography/national-hydrography-dataset)) ## Code/software A Google Earth engine accout is required for a user to reproduce these products. **Here is a [summary guide](https://docs.google.com/document/d/16sDu1Y5wnd9n_bQdA-HgCRBepgjH8Hsvd9Uw-pTentY/edit?usp=sharing ) to using GEE, accessing and running MBI scripts in GEE, and making changes to these scripts.** **A link to the Mapped built infrastructure product Google Earth Engine (GEE) repository:** **[https://code.earthengine.google.com/?accept_repo=users/megdolman/SIAC_BuiltInfrastructureMapping](https://code.earthengine.google.com/?accept_repo=users/megdolman/SIAC_BuiltInfrastructureMapping)** The repository for creating the MBI product is seperated into 4 folders, each with seperate scripts:  1. Train a. CreateTrainingStacks:* creates atmospherically corrected bottom-of-atmosphere image stacks from which to sample to create training data* b. SampleTrainingStacks: *samples random points across the region of interest. Pixels are then extracted at these random points to generate a pixel training dataset. For each class (six classes) and every year (four years of training polygons), 15,000 random points are generated* 2. Classify c. ClassifyImageStacks: *imports the training pixels, generates annual image stacks, applies the Random Forest classifier to classify the image stacks, produces the annual mapped products of built infrastructure.* 3. Validate d. StratifySampleForValidation: *applies a stratified sampling method to generate random points for algorithm validation* e. GenerateValidationData: *adds geometry points at each point generated in the stratified random sample, for every year* 4. Analysis f. AnalyseTimeSeries: *visualizes each year of the MBI product, from 2015 to 2024 (as of December 2024) and calculates the area of each class, for every year* g. CompareProducts: *visually compare the four LULC products in Dolman et al. (2025), as well as calculate area of certain classes. The other products compared are [Dynamic World,](https://dynamicworld.app/) [National Land Use and Land Cover,](https://www.usgs.gov/centers/eros/science/annual-national-land-cover-database) and [Microsoft Building Footprints](https://github.com/microsoft/USBuildingFootprints).*