Zobrazeno 1 - 10
of 536
pro vyhledávání: '"Shao Zhenfeng"'
Change detection in remote sensing images is essential for tracking environmental changes on the Earth's surface. Despite the success of vision transformers (ViTs) as backbones in numerous computer vision applications, they remain underutilized in ch
Externí odkaz:
http://arxiv.org/abs/2406.12847
Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way. The emergence of the 3rd wave information has made a proportion of maps are available to be generated ubiquitously, which would significantly e
Externí odkaz:
http://arxiv.org/abs/2312.08600
Publikováno v:
In Remote Sensing of Environment 1 September 2024 311
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation August 2024 132
Publikováno v:
In Ecological Indicators August 2024 165
In a complex urban scene, observation from a single sensor unavoidably leads to voids in observations, failing to describe urban objects in a comprehensive manner. In this paper, we propose a spatio-temporal-spectral-angular observation model to inte
Externí odkaz:
http://arxiv.org/abs/2109.00900
Autor:
Cai, Bowen, Baumgart, André, Haberl, Helmut, Wiedenhofer, Dominik, Fang, Shenghui, Shao, Zhenfeng
Publikováno v:
In Resources, Conservation & Recycling June 2024 205
In the analytic hierarchy process (AHP) based flood risk estimation models, it is widely acknowledged that different weighting criteria can lead to different results. In this study, we evaluated and discussed the sensitivity of flood risk estimation
Externí odkaz:
http://arxiv.org/abs/2107.13368
Floods are highly uncertain events, occurring in different regions, with varying prerequisites and intensities. A highly reliable flood disaster risk map can help reduce the impact of floods for flood management, disaster decreasing, and urbanization
Externí odkaz:
http://arxiv.org/abs/2107.02043
Autor:
Shao, Zhenfeng, Wang, Jiaming, Deng, Lianbing, Huang, Xiao, Lu, Tao, Luo, Fang, Zhang, Ruiqian, Lv, Xianwei, Dang, Chaoya, Ding, Qing, Wang, Zhiqiang
In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks. The designed GLSD database includes a total of 212,357 annotated instances from 152,576 images. Based on the col
Externí odkaz:
http://arxiv.org/abs/2106.02773