Remote Sensing Based Building Extraction II
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...
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| Materialtyp: | Online |
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| Språk: | engelska |
| Utgiven: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Länkar: | ONIX_20230511_9783036570648_140 |
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| _version_ | 1869519519720931328 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-100123 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1001232024-03-28T03:31:35Z Remote Sensing Based Building Extraction II Tian, Jiaojiao Yan, Qin Awrangjeb, Mohammad Kallfelz-Sirmacek, Beril Demir, Nusret building extraction high-resolution remote-sensing image semantic edge detection semantic segmentation building footprint map vectorization convolutional neural network airborne LiDAR graph segmentation object primitive geometric feature road extraction high-resolution image hyperspectral image synthetic aperture radar (SAR) light detection and ranging (LiDAR) farmland range attention enhancement U-Net network improvement multi-source remote sensing image building model reconstruction half-space LiDAR data urban scale interactive segmentation network deep learning iterative training remote sensing images spatial attention global information awareness cross level information fusion dense matching convolutional neural networks end-to-end pyramid architecture building reconstruction LiDAR point clouds integer programming airborne Earth observation ultrahigh spatial resolution instance segmentation fully convolutional neural networks roofscape remote sensing building extraction building photovoltaic self-supervised learning n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography 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. 2023-05-11T17:21:54Z 2023-05-11T17:21:54Z 2023 book ONIX_20230511_9783036570648_140 9783036570648 9783036570655 https://directory.doabooks.org/handle/20.500.12854/100123 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7217 https://mdpi.com/books/pdfview/book/7217 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7065-5 10.3390/books978-3-0365-7065-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036570648 9783036570655 276 Basel open access |
| spellingShingle | building extraction high-resolution remote-sensing image semantic edge detection semantic segmentation building footprint map vectorization convolutional neural network airborne LiDAR graph segmentation object primitive geometric feature road extraction high-resolution image hyperspectral image synthetic aperture radar (SAR) light detection and ranging (LiDAR) farmland range attention enhancement U-Net network improvement multi-source remote sensing image building model reconstruction half-space LiDAR data urban scale interactive segmentation network deep learning iterative training remote sensing images spatial attention global information awareness cross level information fusion dense matching convolutional neural networks end-to-end pyramid architecture building reconstruction LiDAR point clouds integer programming airborne Earth observation ultrahigh spatial resolution instance segmentation fully convolutional neural networks roofscape remote sensing building extraction building photovoltaic self-supervised learning n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography Remote Sensing Based Building Extraction II |
| title | Remote Sensing Based Building Extraction II |
| title_full | Remote Sensing Based Building Extraction II |
| title_fullStr | Remote Sensing Based Building Extraction II |
| title_full_unstemmed | Remote Sensing Based Building Extraction II |
| title_short | Remote Sensing Based Building Extraction II |
| title_sort | remote sensing based building extraction ii |
| topic | building extraction high-resolution remote-sensing image semantic edge detection semantic segmentation building footprint map vectorization convolutional neural network airborne LiDAR graph segmentation object primitive geometric feature road extraction high-resolution image hyperspectral image synthetic aperture radar (SAR) light detection and ranging (LiDAR) farmland range attention enhancement U-Net network improvement multi-source remote sensing image building model reconstruction half-space LiDAR data urban scale interactive segmentation network deep learning iterative training remote sensing images spatial attention global information awareness cross level information fusion dense matching convolutional neural networks end-to-end pyramid architecture building reconstruction LiDAR point clouds integer programming airborne Earth observation ultrahigh spatial resolution instance segmentation fully convolutional neural networks roofscape remote sensing building extraction building photovoltaic self-supervised learning n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| topic_facet | building extraction high-resolution remote-sensing image semantic edge detection semantic segmentation building footprint map vectorization convolutional neural network airborne LiDAR graph segmentation object primitive geometric feature road extraction high-resolution image hyperspectral image synthetic aperture radar (SAR) light detection and ranging (LiDAR) farmland range attention enhancement U-Net network improvement multi-source remote sensing image building model reconstruction half-space LiDAR data urban scale interactive segmentation network deep learning iterative training remote sensing images spatial attention global information awareness cross level information fusion dense matching convolutional neural networks end-to-end pyramid architecture building reconstruction LiDAR point clouds integer programming airborne Earth observation ultrahigh spatial resolution instance segmentation fully convolutional neural networks roofscape remote sensing building extraction building photovoltaic self-supervised learning n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| url | ONIX_20230511_9783036570648_140 |