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- W4301393721 abstract "Accurate building inventories are essential for city planning and disaster risk management. Traditionally generated via census or selected small surveys, these suffer from data quality and/or resolution. High-resolution satellite imagery with object segmentation provides an effective alternative, readily capturing large extents. This study develops a highly automated building extraction methodology for location-based building exposure data from high (0.5 m) resolution satellite stereo imagery. The development relied on Taipei test areas covering 13.5 km 2 before application to the megacity of Jakarta. Of the captured Taipei buildings, 48.8% are at one-to-one extraction, improving to 71.9% for larger buildings with total floor area >8000 m 2 , and to 99% when tightly-spaced building clusters are further included. Mean absolute error in extracted footprint area is 16% for these larger buildings. The extraction parameters are tuned for Jakarta buildings using small test areas before covering Jakarta's 643 km 2 with over 1.247 million buildings extracted." @default.
- W4301393721 created "2022-10-05" @default.
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- W4301393721 date "2022-10-01" @default.
- W4301393721 modified "2023-10-18" @default.
- W4301393721 title "Efficient Building Inventory Extraction from Satellite Imagery for Megacities" @default.
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- W4301393721 doi "https://doi.org/10.14358/pers.21-00053r2" @default.
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