Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Siqiong Chen"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 6302-6315 (2021)
Recent advances in deep-learning methods have shown extraordinary performance in road extraction from high resolution satellite imagery. However, most existing deep-learning network models yield discontinuous and incomplete results because of shadows
Externí odkaz:
https://doaj.org/article/86d889e60c9a493789d3f0fa7f1f5aec
Autor:
Huihui Cai, Siqiong Chen, Yongyang Xu, Zixuan Li, Xiangjin Ran, Xingping Wen, Yongsheng Li, Yanqing Men
Publikováno v:
Earth and Space Science, Vol 8, Iss 11, Pp n/a-n/a (2021)
Abstract Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intellig
Externí odkaz:
https://doaj.org/article/40d3868f96c14616b7d04b682724494e
Publikováno v:
Earth and Space Science, Vol 8, Iss 6, Pp n/a-n/a (2021)
Abstract Chinese word segmentation (CWS) is the foundational work of geological report text mining and has an important influence on various tasks, such as named entity recognition and relation extraction. In recent years, the accuracy of the domain
Externí odkaz:
https://doaj.org/article/00f1352c496a4881a391cab2aa586abd
Publikováno v:
Remote Sensing, Vol 13, Iss 13, p 2506 (2021)
Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The
Externí odkaz:
https://doaj.org/article/7d585ac823e64ef1a500af3100731f31
Publikováno v:
Journal of Geographical Systems. 24:143-169
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aff9d82898371a847773736c2a101c10
https://doi.org/10.1007/s10109-022-00375-9
https://doi.org/10.1007/s10109-022-00375-9
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 6302-6315 (2021)
Recent advances in deep-learning methods have shown extraordinary performance in road extraction from high resolution satellite imagery. However, most existing deep-learning network models yield discontinuous and incomplete results because of shadows
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47a24209ef58a33e6864db7ee034eb0e
https://doi.org/10.1109/jstars.2021.3083055
https://doi.org/10.1109/jstars.2021.3083055
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. :1-1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a41dd5e62d305041b57cda0161561f5a
https://doi.org/10.1109/tgrs.2023.3277699
https://doi.org/10.1109/tgrs.2023.3277699
Publikováno v:
Earth and Space Science, Vol 8, Iss 6, Pp n/a-n/a (2021)
Chinese word segmentation (CWS) is the foundational work of geological report text mining and has an important influence on various tasks, such as named entity recognition and relation extraction. In recent years, the accuracy of the domain‐general
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f29948de50b8676e8cd6707e1a103c41
https://doi.org/10.1029/2021ea001673
https://doi.org/10.1029/2021ea001673
Publikováno v:
Remote Sensing, Vol 13, Iss 2506, p 2506 (2021)
Remote Sensing
Volume 13
Issue 13
Pages: 2506
Remote Sensing
Volume 13
Issue 13
Pages: 2506
Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::098467eb6a3429759a13bd674a4c9afd
https://doi.org/10.3390/rs13132506
https://doi.org/10.3390/rs13132506
Publikováno v:
International Journal of Digital Earth. Jan2024, Vol. 17 Issue 1, p1-24. 24p.