Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Xingtong Ge"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 128, Iss , Pp 103709- (2024)
Precise traffic prediction is crucial in the domain of intelligent transportation. However, the task of accurately predicting traffic has struggled to keep pace with escalating application demands. One of the main reasons for this difficulty is the n
Externí odkaz:
https://doaj.org/article/910dd3d120b64c0aa5a615e1b8ef16e4
Publikováno v:
Fire, Vol 7, Iss 4, p 131 (2024)
This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating
Externí odkaz:
https://doaj.org/article/1550038a89584787aa9d7fcfbd0f01f6
Publikováno v:
Remote Sensing, Vol 15, Iss 20, p 4931 (2023)
Present approaches in PV (Photovoltaic) detection are known to be scalable to a larger area using machine learning classification and have improved accuracy on a regional scale with deep learning diagnostics. However, it may cause false detection, ti
Externí odkaz:
https://doaj.org/article/3f438bbff1f546a88d994c99f0614109
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4403 (2023)
Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative mann
Externí odkaz:
https://doaj.org/article/9672ab0490964c888994181239adad14
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 2126 (2023)
Landslides pose a significant threat to human lives and property, making the development of accurate and reliable landslide prediction methods essential. With the rapid advancement of multi-source remote sensing techniques and machine learning, remot
Externí odkaz:
https://doaj.org/article/cdb4eca9b81d442e81f66fc7951114e4
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 4391 (2022)
Forest fires destroy the ecological environment and cause large property loss. There is much research in the field of geographic information that revolves around forest fires. The traditional forest fire prediction methods hardly consider multi-sourc
Externí odkaz:
https://doaj.org/article/50ed2584687f4c6489dca3037b21214e
Publikováno v:
Remote Sensing, Vol 14, Iss 14, p 3496 (2022)
Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the
Externí odkaz:
https://doaj.org/article/167413375220443d83c46bbceea51b54
Publikováno v:
Remote Sensing, Vol 14, Iss 5, p 1214 (2022)
Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is har
Externí odkaz:
https://doaj.org/article/91984c592a4347c7b0b5e27c46d04ec0
Publikováno v:
2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2edfe0817cbe3eafdde88de6a55d6e81
https://doi.org/10.1109/qrs-c55045.2021.00071
https://doi.org/10.1109/qrs-c55045.2021.00071
Publikováno v:
IGARSS
Disaster emergency decision-making often involves a large amount of spatiotemporal information, and current emergency spatiotemporal analysis methods are often difficult to associate incidents, features and related knowledge at the same time. At pres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2a499fc93e51de8d2bf935d1c234d5a
https://doi.org/10.1109/igarss47720.2021.9553867
https://doi.org/10.1109/igarss47720.2021.9553867