Zhehao Ren

TL;DR: For example, in this article, the authors present a set of urban land use maps at the national and global scales that are derived from the same or consistent data sources with similar or compatible classification systems and mapping methods.

TL;DR: Wenjia Cai*, Chi Zhang*, Hoi Ping Suen*, Siqi Ai, Yuqi Bai, Junzhe Bao, Bin Chen, Liangliang Cheng, Xueqin Cui, Hancheng Dai, Qian Di, Wenxuan Dong, Deijing Dou, Weicheng Fan, Xing Fan, Tong Gao, Yang Geng, Dabo Guan, Yafei Guo, Yixin Hu, Junyi Hua.

TL;DR: Li et al. as discussed by the authors used a gap-filling method to generate full-coverage AOD by fusing AOD data from satellite (Himawari-8 and MODIS) and weather forecast model (CAMS), and additional meteorological and geographical variables.

TL;DR: In this article, the authors proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geospatial big data, which integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socioeconomic attributes, social media, demography, and topography.

TL;DR: It is argued that thinking globally, categorizing locations in a hub-and-spoke intervention network, and acting locally, applying interventions at high-risk areas, is a functional strategy to avert the tremendous burden that would otherwise be placed on public health and society.