Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Haoyi Xiu"'
Autor:
Haoyi Xiu, Xin Liu, Weimin Wang, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 116, Iss , Pp 103150- (2023)
Collapsed buildings should be detected immediately after earthquakes for humanitarian assistance and post-disaster recovery. Automatic collapsed building detection using deep learning has recently become increasingly popular because of its superior a
Externí odkaz:
https://doaj.org/article/4349a0bb11b3468ab5e4cdc16d6fb086
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 2181 (2023)
Most research on the extraction of earthquake-caused building damage using synthetic aperture radar (SAR) images used building damage certification assessments and the EMS-98-based evaluation as ground truth. However, these methods do not accurately
Externí odkaz:
https://doaj.org/article/b63c6bf929f64bf18c2672431437c6dd
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11630-11642 (2021)
Since 2017, many deep learning methods for 3-D point clouds observed by airborne LiDAR (airborne 3-D point clouds) have been proposed. Moreover, not only a deep learning method for airborne 3-D point clouds but also a deep learning method for points
Externí odkaz:
https://doaj.org/article/96f93527a30b4b37b508e9bbb88b7271
Publikováno v:
Remote Sensing, Vol 12, Iss 24, p 4057 (2020)
Collapsed buildings should be detected with the highest priority during earthquake emergency response, due to the associated fatality rates. Although deep learning-based damage detection using vertical aerial images can achieve high performance, as d
Externí odkaz:
https://doaj.org/article/955947d0ea344c79aaceed19c4bc22d7
Publikováno v:
Sensors, Vol 20, Iss 12, p 3568 (2020)
In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised lear
Externí odkaz:
https://doaj.org/article/3762c59fcbf745e5b110fe3289c40ece
Autor:
Haoyi Xiu, Xin Liu, Weimin Wang, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Publikováno v:
MultiMedia Modeling ISBN: 9783031270765
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::743dc7cff91a94e7fdca71c6d1722601
https://doi.org/10.1007/978-3-031-27077-2_21
https://doi.org/10.1007/978-3-031-27077-2_21
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11630-11642 (2021)
Since 2017, many deep learning methods for 3-D point clouds observed by airborne LiDAR (airborne 3-D point clouds) have been proposed. Moreover, not only a deep learning method for airborne 3-D point clouds but also a deep learning method for points
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85bdd5235b2bff3afc98e603ede1c35e
http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100868441
http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100868441
Publikováno v:
Earthquake Spectra. 38:310-330
After an earthquake occurs, field surveys are conducted by relevant authorities to assess the damage suffered by buildings. The field survey is essential as it ensures the safety of residents and provides the necessary information to local authoritie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93ad2a37a867243d552c7ff39798b574
https://doi.org/10.1177/87552930211035408
https://doi.org/10.1177/87552930211035408
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2021, Pp 169-174 (2021)
This study introduces a novel image to a 3D point-cloud translation method with a conditional generative adversarial network that creates a large-scale 3D point cloud. This can generate supervised point clouds observed via airborne LiDAR from aerial
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff81d018ebf16b7dc2a5240f5e529b23
https://doi.org/10.5194/isprs-annals-v-2-2021-169-2021
https://doi.org/10.5194/isprs-annals-v-2-2021-169-2021
Publikováno v:
IGARSS
Three-dimensional (3D) point clouds are becoming an important part of the geospatial domain. During research on 3D point clouds, deep-learning models have been widely used for the classification and segmentation of 3D point clouds observed by airborn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91c6421545cbc1b74626a167ec1df20a
https://doi.org/10.1109/igarss47720.2021.9554523
https://doi.org/10.1109/igarss47720.2021.9554523