TY  - BOOK
AU  - Yang, Bisheng
AU  - Awrangjeb, Mohammad
AU  - Hu, Xiangyun
AU  - Tian, Jiaojiao
AB  - 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
DO  - 10.3390/books978-3-03928-383-5
ID  - OAPEN ID: 44862
KW  - object recognition
KW  - n/a
KW  - very high resolution
KW  - image fusion
KW  - regularization
KW  - simple linear iterative clustering (SLIC)
KW  - digital building height
KW  - building
KW  - DTM extraction
KW  - 3D reconstruction
KW  - imagery
KW  - GIS data
KW  - high-resolution satellite images
KW  - building edges detection
KW  - high resolution optical images
KW  - point clouds
KW  - building extraction
KW  - land-use
KW  - morphological attribute filter
KW  - deep convolutional neural network
KW  - boundary extraction
KW  - high spatial resolution remotely sensed imagery
KW  - remote sensing
KW  - fully convolutional network
KW  - 3-D
KW  - semantic segmentation
KW  - morphological profile
KW  - modelling
KW  - roof segmentation
KW  - boundary regulated network
KW  - 3D urban expansion
KW  - feature fusion
KW  - developing city
KW  - very high resolution imagery
KW  - building detection
KW  - occlusion
KW  - change detection
KW  - building index
KW  - Massachusetts buildings dataset
KW  - elevation map
KW  - high spatial resolution remote sensing imagery
KW  - data fusion
KW  - generative adversarial network
KW  - unmanned aerial vehicle (UAV)
KW  - high-resolution aerial images
KW  - ultra-hierarchical sampling
KW  - U-Net
KW  - binary decision network
KW  - straight-line segment matching
KW  - outline extraction
KW  - building boundary extraction
KW  - deep learning
KW  - aerial images
KW  - mobile laser scanning
KW  - feature extraction
KW  - multiscale Siamese convolutional networks (MSCNs)
KW  - urban building extraction
KW  - high-resolution aerial imagery
KW  - mathematical morphology
KW  - indoor modelling
KW  - Gabor filter
KW  - active contour model
KW  - attention mechanism
KW  - convolutional neural network
KW  - LiDAR
KW  - accuracy analysis
KW  - point cloud
KW  - feature-level-fusion
KW  - building reconstruction
KW  - richer convolution features
KW  - open data
KW  - VHR remote sensing imagery
KW  - Inria aerial image labeling dataset
KW  - LiDAR point cloud
KW  - method comparison
KW  - 5G signal simulation
KW  - reconstruction
KW  - building regularization technique
KW  - web-net
L1  - https://mdpi.com/books/pdfview/book/2139
LA  - English
LK  - https://directory.doabooks.org/handle/20.500.12854/58168
PB  - MDPI - Multidisciplinary Digital Publishing Institute
PY  - 2020
SN  - 9783039283835
SN  - 9783039283828
TI  - Remote Sensing based Building Extractionnull
ER  - 
