Reprint
Remote Sensing based Building Extraction
Edited by
March 2020
442 pages
- ISBN978-3-03928-382-8 (Paperback)
- ISBN978-3-03928-383-5 (PDF)
This book is a reprint of the Special Issue Remote Sensing based Building Extraction that was published in
Engineering
Environmental & Earth Sciences
Summary
Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
roof segmentation; outline extraction; convolutional neural network; boundary regulated network; very high resolution imagery; building boundary extraction; convolutional neural network; active contour model; high resolution optical images; LiDAR; richer convolution features; building edges detection; high spatial resolution remote sensing imagery; building; modelling; reconstruction; change detection; LiDAR; point cloud; 3-D; building extraction; deep learning; attention mechanism; very high resolution; imagery; building detection; aerial images; feature-level-fusion; straight-line segment matching; occlusion; building regularization technique; point clouds; boundary extraction; regularization; building reconstruction; digital building height; 3D urban expansion; land-use; DTM extraction; open data; developing city; accuracy analysis; building detection; building index; feature extraction; mathematical morphology; morphological attribute filter; morphological profile; building extraction; deep learning; semantic segmentation; data fusion; high-resolution satellite images; GIS data; high-resolution aerial images; deep learning; generative adversarial network; semantic segmentation; Inria aerial image labeling dataset; Massachusetts buildings dataset; building extraction; simple linear iterative clustering (SLIC); multiscale Siamese convolutional networks (MSCNs); binary decision network; unmanned aerial vehicle (UAV); image fusion; high spatial resolution remotely sensed imagery; object recognition; deep learning; method comparison; LiDAR point cloud; building extraction; elevation map; Gabor filter; feature fusion; semantic segmentation; urban building extraction; deep convolutional neural network; VHR remote sensing imagery; U-Net; remote sensing; deep learning; building extraction; web-net; ultra-hierarchical sampling; 3D reconstruction; indoor modelling; mobile laser scanning; point clouds; 5G signal simulation; building extraction; high-resolution aerial imagery; fully convolutional network; semantic segmentation; n/a