Zobrazeno 1 - 10
of 351
pro vyhledávání: '"Du, Zhenhong"'
Autor:
Lin, Qingming, Hu, Rui, Li, Huaxia, Wu, Sensen, Li, Yadong, Fang, Kai, Feng, Hailin, Du, Zhenhong, Xu, Liuchang
Vector data is one of the two core data structures in geographic information science (GIS), essential for accurately storing and representing geospatial information. Shapefile, the most widely used vector data format, has become the industry standard
Externí odkaz:
http://arxiv.org/abs/2410.12376
Volunteer Geographic Information (VGI), with its rich variety, large volume, rapid updates, and diverse sources, has become a critical source of geospatial data. However, VGI data from platforms like OSM exhibit significant quality heterogeneity acro
Externí odkaz:
http://arxiv.org/abs/2409.17049
Autor:
Xu, Liuchang, Zhao, Shuo, Lin, Qingming, Chen, Luyao, Luo, Qianqian, Wu, Sensen, Ye, Xinyue, Feng, Hailin, Du, Zhenhong
The advent of large language models such as ChatGPT, Gemini, and others has underscored the importance of evaluating their diverse capabilities, ranging from natural language understanding to code generation. However, their performance on spatial tas
Externí odkaz:
http://arxiv.org/abs/2408.14438
Autor:
Ma, Xiaowen, Lian, Rongrong, Wu, Zhenkai, Guo, Hongbo, Ma, Mengting, Wu, Sensen, Du, Zhenhong, Song, Siyang, Zhang, Wei
Remote sensing images usually characterized by complex backgrounds, scale and orientation variations, and large intra-class variance. General semantic segmentation methods usually fail to fully investigate the above issues, and thus their performance
Externí odkaz:
http://arxiv.org/abs/2406.16502
The reconstruction of Earth's history faces significant challenges due to the nonunique interpretations often derived from rock records. The problem has long been recognized but there are no systematic solutions in practice. This study introduces an
Externí odkaz:
http://arxiv.org/abs/2407.09977
Publikováno v:
Zhejiang Daxue xuebao. Lixue ban, Vol 51, Iss 2, Pp 131-142 (2024)
High spatial resolution remote sensing images contain rich information, it is therefore very important to study their semantic segmentation. Traditional machine learning methods appear low accuracy and efficiency when used for segmenting high-resolut
Externí odkaz:
https://doaj.org/article/91a8827f7d9541ddbf34b7b47ff39e0f
Publikováno v:
In Water Research 1 October 2024 263
Publikováno v:
In Chemical Geology 20 September 2024 663
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation September 2024 133
Publikováno v:
In Journal of Quantitative Spectroscopy and Radiative Transfer September 2024 323