4 papers
CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regression
Fei Jiang, Jiyang Xia, Junjie Yu +6
Quantifying the impacts of air pollution on health and climate relies on key atmospheric particle properties such as toxicity and hygroscopicity. However, these properties typicall…
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach
Jinzhou Cao, Xiangxu Wang, Jiashi Chen +5
Fine-grained economic mapping through urban representation learning has emerged as a crucial tool for evidence-based economic decisions. While existing methods primarily rely on su…
Scalable Analysis of Urban Scaling Laws: Leveraging Cloud Computing to Analyze 21,280 Global Cities
Zhenhui Li, Hongwei Zhang, Kan Wu
Cities play a pivotal role in human development and sustainability, yet studying them presents significant challenges due to the vast scale and complexity of spatial-temporal data.…
What can LLM tell us about cities?
Zhuoheng Li, Yaochen Wang, Zhixue Song +4
This study explores the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale. We employ two methods: directly querying the…