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of 47 917
pro vyhledávání: '"Urban region"'
With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its
Externí odkaz:
http://arxiv.org/abs/2411.15214
Electric vehicle charging demand prediction is important for vacant charging pile recommendation and charging infrastructure planning, thus facilitating vehicle electrification and green energy development. The performance of previous spatio-temporal
Externí odkaz:
http://arxiv.org/abs/2410.18766
Autor:
Jin, Jiahui, Song, Yifan, Kan, Dong, Zhu, Haojia, Sun, Xiangguo, Li, Zhicheng, Sun, Xigang, Zhang, Jinghui
Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Previous work of
Externí odkaz:
http://arxiv.org/abs/2408.05920
Autor:
Xu, Zhuo, Zhou, Xiao
The explosion of massive urban data recently has provided us with a valuable opportunity to gain deeper insights into urban regions and the daily lives of residents. Urban region representation learning emerges as a crucial realm for fulfilling this
Externí odkaz:
http://arxiv.org/abs/2407.02074
Akademický článek
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Urban region profiling is influential for smart cities and sustainable development. However, extracting fine-grained semantics and generating robust urban region embeddings from noisy and incomplete urban data is challenging. In response, we present
Externí odkaz:
http://arxiv.org/abs/2402.01163
Urban region profiling aims to learn a low-dimensional representation of a given urban area while preserving its characteristics, such as demographics, infrastructure, and economic activities, for urban planning and development. However, prevalent pr
Externí odkaz:
http://arxiv.org/abs/2403.16831
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
Mining latent information from human trajectories for understanding our cities has been persistent endeavors in urban studies and spatial information science. Many previous studies relied on manually crafted features and followed a supervised learnin
Externí odkaz:
https://doaj.org/article/fd2731e9cc41498db9917c5127d6880f
Recently, learning urban region representations utilizing multi-modal data (information views) has become increasingly popular, for deep understanding of the distributions of various socioeconomic features in cities. However, previous methods usually
Externí odkaz:
http://arxiv.org/abs/2312.09681
Autor:
Yan, Yibo, Wen, Haomin, Zhong, Siru, Chen, Wei, Chen, Haodong, Wen, Qingsong, Zimmermann, Roger, Liang, Yuxuan
Urban region profiling from web-sourced data is of utmost importance for urban planning and sustainable development. We are witnessing a rising trend of LLMs for various fields, especially dealing with multi-modal data research such as vision-languag
Externí odkaz:
http://arxiv.org/abs/2310.18340