Building footprtints from 1970s Hexagon spy satellite images for four global urban growth hotspots

<p>This dataset features building footprints derived from 1970s very-high resolution KH-9 Hexagon spy satellite imagery using a Mask R-CNN deep learning object detection approach for four sites: San Diego County (USA), Madison (USA), Harare (Zimbabwe), and Hyderabad (India). It also contains contemporary building footprint data from Microsoft’s building footprint layer (https://github.com/microsoft/GlobalMLBuildingFootprints) as a reference.</p> <p><strong>Corresponding publication</strong></p> <p>Franz Schug^*; Neda K. Kasraee, Akash Anand, MacKenzy T. Growth-Price, Mihai D. Nita, Afag Rizayeva, Volker C. Radeloff. Quantifying multi-decadal urban growth using Hexagon spy satellite imagery and deep learning building detection across four global cities (in review). Landscape and Urban Planning.</p> <p><strong>Temporal extent</strong></p> <p>The data contains data representative for ca. 1975 and ca. 2020.</p> <p><strong>Data, data format, and units</strong></p> <p>KH-9 Hexagon data (“hex_images”) are provided as geotiff files with a spatial resolution of ~ 0.6 – 1 m. The coordinate reference systems (CRS) are local UTM projections (San Diego County: Zone 11N, EPSG 32611, Madison: Zone 16N, EPSG 32616, Harare: Zone 36S, EPSG 32736, Hyderabad: Zone 44N, EPSG 32644).</p> <p>Microsoft building footprint data (“ms_buildings”) are provided as vector shape files. The data were clipped from Microsoft’s global building footprint layer (https://github.com/microsoft/GlobalMLBuildingFootprints) and are provided as a reference for ca. 2020. CRS correspond to the CRS of Hexagon data. The data are also provided in a rasterized version with 2-m and 300-m spatial resolution (nearest neighbor resampling).</p> <p>Study site extents (“study_sites”) are provided as vector shape files.</p> <p>Training chips (“training_chips”) contain image chips and labels used for training the Mask R-CNN models. The metadata format is “Mask RCNN Masks”. The data include no-feature tiles.</p> <p>The models (“models”) are trained Mask R-CNN models ready to be used in ESRI ArcGIS Pro version 3.2.1. Please refer to the publication for details about model parameterization.</p> <p>The detected buildings (“detected_buildings”) are provided as vector shape files and represent building footprints from ca. 1975 Hexagon imagery using the provided Mask R-CNN models. The data represent the final results, that means, after merging models with different chip sizes and post-processing (see manuscript). The data are also provided in a rasterized version with 2-m and 300-m spatial resolution (nearest neighbor resampling).</p> <p><strong>Processing environment</strong></p> <p>This research has been conducted using Python for ESRI ArcGIS Pro version 3.2.1 and the TensorFlow package. We conducted our analysis on a server with an NVIDIA A100 Tensor Core GPU (40GB, PCIe), a Dual AMD EPYC 7513 CPU with 2.6GHz and 128 threads in total, and 1 TB RAM (RDIMM, 3200MT/s Dual Rank).</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit the website of SILVIS lab, University of Wisconsin-Madison (https://silvis.forest.wisc.edu/) to learn more.</p> <p>Please check the corresponding github repository for additional data and code: https://github.com/franzschug/hexagon_bld_footprints</p> <p><strong>Acknowledgments</strong></p> <p>This study was supported by the NASA Land Cover and Land Use Change Program under agreement 80NSSC21K0310, the NASA IDS program under agreement 80NSSC24K0303, and the USDA McIntire Stennis Program. </p>