Zobrazeno 1 - 10
of 223
pro vyhledávání: '"Masashi Matsuoka"'
Publikováno v:
Big Earth Data, Vol 8, Iss 3, Pp 467-493 (2024)
Conducting long measurements of infrastructure deformation is a critical engineering task. Conventional methods are both time-consuming and expensive, limiting their use for large-scale applications. The synergy of synthetic aperture radar (SAR) and
Externí odkaz:
https://doaj.org/article/e086b86e25514b94a52b8013ffec468f
Autor:
Hiroyuki Miura, Masashi Matsuoka, Juan C. Reyes, Nelson Pulido, Mitsufumi Hashimoto, Andrea C. Riaño, Alvaro Hurtado, Raul Rincon, Helber García, Carlos Lozano
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 12, Iss 12, p 471 (2023)
Early disaster responses in damaged areas after a large earthquake are indispensable for stakeholders to assess and grasp the impacts such as building and infrastructure damage and disrupted community functionality as soon as possible. This study int
Externí odkaz:
https://doaj.org/article/68c1f36fc2184edb8ecbdef2a7935429
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 5753-5765 (2022)
This article is a new assessment of damaged roads after the Kumamoto earthquake in southern Japan (2016) using remotely sensed synthetic aperture radar (SAR) data, field data and deep learning. Three SAR images from descending orbits of Sentinel-1 in
Externí odkaz:
https://doaj.org/article/8503465f18ee4e76938db3244d47a0b8
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 15, Iss 3, p 597 (2023)
Road markings are reflective features on roads that provide important information for safe and smooth driving. With the rise of autonomous vehicles (AV), it is necessary to represent them digitally, such as in high-definition (HD) maps generated by m
Externí odkaz:
https://doaj.org/article/315d957ab2734c6192351d46a61834c1
Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest
Autor:
Saygin Abdikan, Caglar Bayik, Aliihsan Sekertekin, Filiz Bektas Balcik, Sadra Karimzadeh, Masashi Matsuoka, Fusun Balik Sanli
Publikováno v:
Forests, Vol 13, Iss 2, p 347 (2022)
Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accura
Externí odkaz:
https://doaj.org/article/027429b247e748128f678d2aab70148a
Publikováno v:
Progress in Earth and Planetary Science, Vol 5, Iss 1, Pp 1-31 (2018)
Abstract Polygon-based terrain classification data were created globally using 280 m digital elevation models (DEMs) interpolated from the multi-error-removed improved-terrain DEM (MERIT DEM). First, area segmentation was performed globally with the
Externí odkaz:
https://doaj.org/article/ceec11ebbb894b0ab10d2c8929f0b070
Publikováno v:
Remote Sensing, Vol 13, Iss 21, p 4272 (2021)
This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analy
Externí odkaz:
https://doaj.org/article/62548aa19488436a97f7cf0a269e8261