2026-10-15T14:46:18Z https://api.figshare.com/v2/oai

oai:figshare.com:article/25847125 2024-05-20T14:35:07Z category_29161 category_25051 category_25027 portal_753 item_type_3 month_year_05_2024

Mask region-based convolutional neural network (R-CNN) models for extracting building footprints from remote sensing data Khumeleni Makungo (18574285) Deep learning Urban informatics Land use and environmental planning Segmentation High-resolution aerial imagery LiDAR Detection Mask region-based convolutional neural network (R-CNN) Deep learning Spatial resolution Normalized digital surface model (nDSM) SDG 9 Industry, innovation and infrastructure SDG 11 Sustainable cities and communities <p dir="ltr">These trained deep learning are trained to extract building footprints from high-resolution aerial imagery and LiDAR-derived nDSM with a spatial resolution of 20cm or less. These models were used to extract footprints in formal residential zones, industrial zones, and informal settlement zones within the City of Cape Town. It must be noted that the trained Mask R-CNN models are scalable to extract building footprints across different South African’ Metropolitans, as formal and informal zones co-exist in these areas and they have similar environmental settings.</p> 2024-05-20T14:19:42Z Dataset Dataset 10.25403/UPresearchdata.25847125.v2 https://figshare.com/articles/dataset/Mask_region-based_convolutional_neural_network_R-CNN_models_for_extracting_building_footprints_from_remote_sensing_data/25847125 CC BY 4.0