Zobrazeno 1 - 10
of 222
pro vyhledávání: '"Multi-source remote sensing data"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract As the mainstream and trend of urban development in China, deeply exploring the spatiotemporal patterns and influencing mechanisms of ecosystem service value in the Yangtze River Delta urban agglomeration is of great significance for achievi
Externí odkaz:
https://doaj.org/article/e39edf6bb2694a48b27e0ef723c9dee0
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
Coastal wetlands, especially tidal marshes, play a crucial role in supporting ecosystems and slowing shoreline erosion. Accurate and cost-effective identification and classification of various marsh types, such as high and low marshes, are important
Externí odkaz:
https://doaj.org/article/5b14975723984b7594031aaab41bd9d8
Publikováno v:
Big Earth Data, Pp 1-27 (2024)
With the increasing frequency of floods, in-depth flood event analyses are essential for effective disaster relief and prevention. Satellite-based flood event datasets have become the primary data source for flood event analyses instead of limited di
Externí odkaz:
https://doaj.org/article/a0a3215ae61f413c95133b94bf13cdeb
Autor:
Jian Lu, Hongkun Fu, Xuhui Tang, Zhao Liu, Jujian Huang, Wenlong Zou, Hui Chen, Yue Sun, Xiangyu Ning, Jian Li
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation
Externí odkaz:
https://doaj.org/article/b553cc3485c24f2a9f801d3181e13ba6
Publikováno v:
Remote Sensing, Vol 16, Iss 20, p 3825 (2024)
The field of multi-source remote sensing observation is becoming increasingly dynamic through the integration of various remote sensing data sources. However, existing deep learning methods face challenges in differentiating between internal and exte
Externí odkaz:
https://doaj.org/article/8838732959dd47c8bf485bbde815e773
Publikováno v:
International Journal of Digital Earth, Vol 16, Iss 1, Pp 2522-2554 (2023)
Deep learning algorithms show good prospects for remote sensing flood monitoring. They mostly rely on huge amounts of labeled data. However, there is a lack of available labeled data in actual needs. In this paper, we propose a high-resolution multi-
Externí odkaz:
https://doaj.org/article/b9bbd9a97ebb422e9f6aee429e9bba14
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3120 (2024)
The integration of multi-source remote sensing data, bolstered by advancements in deep learning, has emerged as a pivotal strategy for enhancing land use and land cover (LULC) classification accuracy. However, current methods often fail to consider t
Externí odkaz:
https://doaj.org/article/8a8f18be9d574041a91ba69ce72a6b13
Publikováno v:
Dizhi lixue xuebao, Vol 30, Iss 3, Pp 519-534 (2023)
Objective This study focuses on the Gagarin region on the far side of the Moon, aiming to reveal the geological characteristics, distribution features, and genesis of typical areas on the lunar far side. Additionally, it seeks to explore the regional
Externí odkaz:
https://doaj.org/article/ec589ac38eb245628f172ce6423f3871
Autor:
Yang Qichi, Wang Lihui, Huang Jinliang, Liu Linzhi, Li Xiaodong, Xiao Fei, Du Yun, Yan Xue, Ling Feng
Publikováno v:
GIScience & Remote Sensing, Vol 60, Iss 1 (2023)
Alpine land cover (ALC) is facing many challenges with climatic change, biodiversity reduction and other cascading ecosystem damage triggered by natural and anthropogenic interference. Although several global land cover products and thematic maps are
Externí odkaz:
https://doaj.org/article/0a4779377c28466f8647d2ea2916934e
Publikováno v:
Remote Sensing, Vol 16, Iss 2, p 271 (2024)
Accurate information concerning the spatial distribution of invasive alien species’ habitats is essential for invasive species prevention and management, and ecological sustainability. Currently, nationwide identification of suitable habitats for t
Externí odkaz:
https://doaj.org/article/c141a9dd62e941f09bc5af5e6fcd4443