The Traj2Vec model to quantify residents' spatial trajectories and estimate the proportions of urban land-use types. - Jinbao Zhang 001; Xia Li 001; Yao Yao 004; Ye Hong; Jialyu He; Zhangwei Jiang; Jianchao Sun (Int. J. Geogr. Inf. Sci., 2021)

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning. - Haoming Zhuang; Xiaoping Liu 001; Yuchao Yan; Jinpei Ou; Jialyu He; Changjiang Wu (Remote. Sens., 2021)

Accurate Estimation of the Proportion of Mixed Land Use at the Street-Block Level by Integrating High Spatial Resolution Images and Geospatial Big Data. - Jialyu He; Xia Li 001; Penghua Liu; Xinxin Wu; Jinbao Zhang 001; Dachuan Zhang; Xiaojuan Liu; Yao Yao 004 (IEEE Trans. Geosci. Remote. Sens., 2021)

Integrating a deep forest algorithm with vector-based cellular automata for urban land change simulation. - Haoming Zhuang; Xiaoping Liu 001; Yuchao Yan; Dachuan Zhang; Jialyu He; Jinqiang He; Xinchang Zhang 002; Honghui Zhang; Manchun Li (Trans. GIS, 2022)

Predicting the locations of missing persons in China by using NGO data and deep learning techniques. - Anning Dong; Yatao Zhang; Zijin Guo; Peng Luo 001; Yao Yao 004; Jialyu He; Qia Zhu; Ying Jiang; Kailu Xiong; Qingfeng Guan 001 (Int. J. Digit. Earth, 2024)

DynaGraph: Towards Dynamic Graph Learning for Multi-scale Traffic Generation with Spatial-temporal Agent Framework. - Jinhui Ouyang; Yijie Zhu; Hanhui Deng; Jialyu He; Di Wu 002 (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2025)