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230515 ||| eng |
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|a 9783036570655
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|a books978-3-0365-7065-5
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|a 9783036570648
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| 100 |
1 |
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|a Tian, Jiaojiao
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| 245 |
0 |
0 |
|a Remote Sensing Based Building Extraction II
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (276 p.)
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| 653 |
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|a fully convolutional neural networks
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| 653 |
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|a semantic edge detection
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| 653 |
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|a global information awareness
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| 653 |
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|a high-resolution image
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| 653 |
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|a n/a
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| 653 |
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|a U-Net network improvement
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| 653 |
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|a deep learning
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| 653 |
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|a iterative training
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| 653 |
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|a attention enhancement
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| 653 |
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|a dense matching
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| 653 |
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|a building photovoltaic
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| 653 |
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|a road extraction
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| 653 |
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|a instance segmentation
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| 653 |
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|a urban scale
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| 653 |
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|a interactive segmentation network
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| 653 |
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|a end-to-end
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| 653 |
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|a map vectorization
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| 653 |
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|a building model
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| 653 |
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|a building footprint
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| 653 |
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|a high-resolution remote-sensing image
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| 653 |
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|a Research & information: general / bicssc
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| 653 |
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|a LiDAR data
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| 653 |
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|a remote sensing images
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| 653 |
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|a hyperspectral image
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| 653 |
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|a building reconstruction
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| 653 |
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|a integer programming
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| 653 |
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|a convolutional neural networks
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| 653 |
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|a airborne Earth observation
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| 653 |
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|a pyramid architecture
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| 653 |
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|a airborne LiDAR
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| 653 |
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|a building extraction
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| 653 |
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|a LiDAR
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| 653 |
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|a synthetic aperture radar (SAR)
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| 653 |
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|a multi-source remote sensing image
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| 653 |
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|a half-space
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| 653 |
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|a point clouds
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| 653 |
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|a cross level information fusion
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| 653 |
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|a semantic segmentation
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| 653 |
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|a geometric feature
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| 653 |
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|a Geography / bicssc
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| 653 |
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|a roofscape
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| 653 |
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|a convolutional neural network
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| 653 |
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|a graph segmentation
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| 653 |
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|a reconstruction
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| 653 |
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|a object primitive
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| 653 |
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|a remote sensing building extraction
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| 653 |
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|a ultrahigh spatial resolution
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| 653 |
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|a spatial attention
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| 653 |
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|a light detection and ranging (LiDAR)
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| 653 |
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|a self-supervised learning
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| 653 |
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|a farmland range
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| 700 |
1 |
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|a Yan, Qin
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| 700 |
1 |
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|a Awrangjeb, Mohammad
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| 700 |
1 |
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|a Kallfelz-Sirmacek, Beril
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
0 |
|a 10.3390/books978-3-0365-7065-5
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/100123
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/7217
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 720
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| 082 |
0 |
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|a 000
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| 082 |
0 |
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|a 700
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| 520 |
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|a Building extraction from remote sensing data plays an important role in geospatial applications such as urban planning, disaster management, navigation, and updating geographic databases. The rapid development of image processing techniques and the accessibility of very-high-resolution multispectral, hyperspectral, LiDAR, and SAR remote sensing images have further boosted research on building-extraction-related topics. In particular, to meet the recent demand for advanced artificial intelligence models, many research institutes and associations have provided open source datasets and annotated training data, presenting new opportunities to develop advanced approaches for building extraction and monitoring. Hence, there are higher expectations of the efficiency, accuracy, and robustness of building extraction approaches. Additionally, they should meet the demand for processing large city-, national-, and global-scale datasets. Moreover, learning and dealing with imperfect training data remains a challenge, as does unexpected objects in urban scenes such as trees, clouds, and shadows. In addition to building masks, more research has arisen on the automatic generation of LoD2/3 building models from remote sensing data. This follow-up Special Issue of "Remote Sensing-based Building Extraction", has collected more research on cutting-edge approaches to essential urban processes such as 3D reconstruction, automatic building segmentation, and 3D roof modelling.
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