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Palabras clave – provistas por la editorial

object recognition; n/a; very high resolution; image fusion; regularization; simple linear iterative clustering (SLIC); digital building height; building; DTM extraction; 3D reconstruction; imagery; GIS data; high-resolution satellite images; building edges detection; high resolution optical images; point clouds; building extraction; land-use; morphological attribute filter; deep convolutional neural network; boundary extraction; high spatial resolution remotely sensed imagery; remote sensing; fully convolutional network; 3-D; semantic segmentation; morphological profile; modelling; roof segmentation; boundary regulated network; 3D urban expansion; feature fusion; developing city; very high resolution imagery; building detection; occlusion; change detection; building index; Massachusetts buildings dataset; elevation map; high spatial resolution remote sensing imagery; data fusion; generative adversarial network; unmanned aerial vehicle (UAV); high-resolution aerial images; ultra-hierarchical sampling; U-Net; binary decision network; straight-line segment matching; outline extraction; building boundary extraction; deep learning; aerial images; mobile laser scanning; feature extraction; multiscale Siamese convolutional networks (MSCNs); urban building extraction; high-resolution aerial imagery; mathematical morphology; indoor modelling; Gabor filter; active contour model; attention mechanism; convolutional neural network; LiDAR; accuracy analysis; point cloud; feature-level-fusion; building reconstruction; richer convolution features; open data; VHR remote sensing imagery; Inria aerial image labeling dataset; LiDAR point cloud; method comparison; 5G signal simulation; reconstruction; building regularization technique; web-net