Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Yunwei Tang"'
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
The tasseled cap transformation (TCT) is a widely used technique for reducing remote sensing multispectral data into three tasseled cap (TC) components – brightness, greenness, and wetness – while retaining essential information for various appli
Externí odkaz:
https://doaj.org/article/3dbbfabeb2bb479596353254d02decaf
Autor:
Yichen Jiang, Yunwei Tang, Linhai Jing, Charles Galdies, Hui Li, Lin Yan, Haifeng Ding, Qiyuan Xie, Changyong Dou
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Aerosol Optical Depth (AOD) is vital for assessing air quality and climate. Nighttime lights (NTL) provide a new data source for urban AOD inversion, but current NTL data suffer from low spatial resolution, limiting detailed AOD studies in smaller ur
Externí odkaz:
https://doaj.org/article/5ecbfc3c2e6f47ba994ff642abf7372f
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Urban development under a changing climate pose threats to heritage conservation, necessitating vigilant monitoring of human activities and natural disasters. This study proposes a dynamic monitoring and risk assessment technology aimed at identifyin
Externí odkaz:
https://doaj.org/article/1ed0723dea6a4736b30ce629413d3ea5
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTClassification of individual tree species (ITS) is critical for fine-scale forest surveys. However, it is difficult to obtain the complete and high-precision data needed for ITS classification in large areas. Lower spatial resolution time-ser
Externí odkaz:
https://doaj.org/article/a98c28bd8dfc43588f9642126afc6198
Publikováno v:
GIScience & Remote Sensing, Vol 59, Iss 1, Pp 1344-1366 (2022)
Recent developments in deep learning (DL) techniques have provided a series of new methods for land cover classification. However, most DL-based methods do not consider the rich spatial association of land cover classes embedded in remote sensing ima
Externí odkaz:
https://doaj.org/article/4af8089de47140498f1af88220823fd9
Autor:
Fulong Chen, Wei Zhou, Yunwei Tang, Ru Li, Hui Lin, Timo Balz, Jin Luo, Pilong Shi, Meng Zhu, Chaoyang Fang
Publikováno v:
International Journal of Digital Earth, Vol 15, Iss 1, Pp 770-788 (2022)
As a World Cultural Heritage site with sacred landscape featuring an exceptional range of Buddhist art and architecture, much attention has been focused on the sustainable development of Bagan (Myanmar). Particularly, the monitoring of landscape surf
Externí odkaz:
https://doaj.org/article/8b8e705e654248f98701775402326a89
Autor:
Huadong Guo, Fulong Chen, Yunwei Tang, Yanbin Ding, Min Chen, Wei Zhou, Meng Zhu, Sheng Gao, Ruixia Yang, Wenwu Zheng, Chaoyang Fang, Hui Lin, Ana Pereira Roders, Francesca Cigna, Deodato Tapete, Bing Xu
Publikováno v:
The Innovation, Vol 4, Iss 5, Pp 100496- (2023)
The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms o
Externí odkaz:
https://doaj.org/article/edcfeb2f67c344db9e37ae7d9af84141
Publikováno v:
Remote Sensing, Vol 15, Iss 9, p 2301 (2023)
Accurate identification of individual tree species (ITS) is crucial to forest management. However, current ITS identification methods are mainly based on traditional image features or deep learning. Traditional image features are more interpretative,
Externí odkaz:
https://doaj.org/article/a0cad3f51e95492ea72e80fcc472c71d
Publikováno v:
Remote Sensing, Vol 14, Iss 22, p 5892 (2022)
Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building foo
Externí odkaz:
https://doaj.org/article/8812a09241904a24a12aa8d2fcc9f9b4
Publikováno v:
Remote Sensing, Vol 14, Iss 20, p 5124 (2022)
Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem,
Externí odkaz:
https://doaj.org/article/da1bdc8a6b0b419a9d0c802b6f00f4c0