Zobrazeno 1 - 10
of 429
pro vyhledávání: '"Xiao Pengfeng"'
Autor:
Li, Zhenshi, Muhtar, Dilxat, Gu, Feng, Zhang, Xueliang, Xiao, Pengfeng, He, Guangjun, Zhu, Xiaoxiang
Automatically and rapidly understanding Earth's surface is fundamental to our grasp of the living environment and informed decision-making. This underscores the need for a unified system with comprehensive capabilities in analyzing Earth's surface to
Externí odkaz:
http://arxiv.org/abs/2411.09301
In the context of global climate change and frequent extreme weather events, forecasting future geospatial vegetation states under these conditions is of significant importance. The vegetation change process is influenced by the complex interplay bet
Externí odkaz:
http://arxiv.org/abs/2407.12592
Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transformer-based models possess glob
Externí odkaz:
http://arxiv.org/abs/2404.02668
The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains. In the remote sensing (RS) field, however, the divers
Externí odkaz:
http://arxiv.org/abs/2402.02544
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-16, 2023, Art no. 4508016
Change detection is a critical task in earth observation applications. Recently, deep learning-based methods have shown promising performance and are quickly adopted in change detection. However, the widely used multiple encoder and single decoder (M
Externí odkaz:
http://arxiv.org/abs/2311.11302
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023, Art no. 5607817
Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing R
Externí odkaz:
http://arxiv.org/abs/2304.09670
Autor:
Qiu, Yinguo, Huang, Jiacong, Luo, Juhua, Xiao, Qitao, Shen, Ming, Xiao, Pengfeng, Peng, Zhaoliang, Jiao, Yaqin, Duan, Hongtao
Publikováno v:
In Environmental Research 1 January 2025 264 Part 1
Autor:
Fan, Haotian, Gu, Wangcheng, Zhou, Dongrui, Ge, Song, Xiao, Pengfeng, Jiang, Ping, Fei, Zhongjie
Publikováno v:
In Sustainable Materials and Technologies September 2024 41
Autor:
Li, Mengjie, Li, Na, Dong, Yangyang, Zhang, Honglin, Bai, Zhimao, Zhang, Rui, Fei, Zhongjie, Zhu, Wenyong, Xiao, Pengfeng, Sun, Xiao, Zhou, Dongrui
Publikováno v:
In World Allergy Organization Journal April 2024 17(4)
Publikováno v:
In Methods March 2024 223:75-82