Dr. Yao Yao is a Professor and Doctoral Supervisor. He previously served as a Specially Appointed Professor at Hitotsubashi University, Japan, and as a Researcher and Visiting Scholar at the University of Tokyo. He is a Senior Member and Committee Member of the Big Data Committee of the Chinese Society for Urban Studies, and has served as a Visiting Scholar in the Data Technology and Products Division of Alibaba Group.

Since 2016, Dr. Yao has been dedicated to research in Spatiotemporal Big Data and Geospatial Artificial Intelligence (GeoAI). He has received numerous prestigious academic honors, including the ACM SIGSPATIAL China Rising Star Award (awarded to only one or two Chinese researchers worldwide each year), the Clarivate Highly Cited Researcher recognition (2023–2025), inclusion in Stanford University's World's Top 2% Scientistsannual rankings (2022–2026) and career-long ranking, and recognition as a Global Frontier Technology Young Scientist in 2022.

Dr. Yao has published over 100 high-quality research papers in the fields of multi-source spatiotemporal big data mining and urban computing, accumulating more than 12,000 citations. Among these publications, 13 have been recognized as ESI Highly Cited Papers or Hot Papers, and his H-index is 50. He has served as Principal Investigator for numerous research projects, including General Program and Young Scientists Fund projects supported by the National Natural Science Foundation of China (NSFC), several projects under China's National Key Research and Development Program, and industry-funded projects supported by leading technology companies such as Alibaba, Tencent, and DiDi. He holds more than 20 patents and software copyrights.

In 2022, Dr. Yao was recognized as the most highly cited author worldwide over the preceding five years for non-review research articles in the International Journal of Geographical Information Science (IJGIS), a leading journal in the field of Geographic Information Science. He pioneered theoretical frameworks and methodologies for geographic entity representation based on geographic intelligence models.

His research publications and related resources are available through the UrbanComp website: https://www.urbancomp.net/.

Dr. Yao has published three academic works, including two specialized textbooks:

1. Geospatial Big Data Analysis: Spatiotemporal Data, Spatial Modeling, and Analytical Frameworks (Volume I)
   ISBN: 978-7-307-25736-8
   Publisher: Wuhan University Press

2. Geospatial Big Data Analysis: Semantic Modeling, GeoAI, and Application Cases (Volume II)
   ISBN: 978-7-307-25794-8
   Publisher: Wuhan University Press

3. AI-Empowered Smart Cities (Volumes I & II)
   ISBN: 978-7-5239-1354-3
   Publisher: China Electric Power Press

Dr. Yao Yao is currently an editorial board member of authoritative journals in the field of Geographic Information Systems, including Computers Environment and Urban Systems (CEUS), Urban Climate, Journal of Spatiotemporal Information Science, and The Innovation. He is a reviewer for 50 international authoritative SCI/SSCI journals in the fields of geographic information, remote sensing, computer science, and data science, including Nature Human Behaviour, Nature Communications, International Journal of Geographical Information Science (IJGIS), Annals of the AAG, Transactions in GIS (TGIS), Landscape and Urban Planning, and Cities. As a senior member of the Urban Science Research Association of the Ministry of Housing and Urban-Rural Development, Dr. Yao Yao has collaborated, researched, and exchanged ideas with teams from Wuhan University, Sun Yat-sen University, Shenzhen University, Stanford University in the USA, the University of Tokyo in Japan, Alibaba DAMO Academy, and MIT in the USA on location-based services, big data technology, and fine urban simulation and application.

Students with backgrounds in 3S (GNSS/RS/GIS), computer science, and data science are welcome to join the High Performance Spatial Intelligent Computing Laboratory (HPSCIL@CUG) and Dr. Yao Yao's graduate student team!

Research website：

https://www.researchgate.net/profile/Yao_Yao42/

https://www.urbancomp.net/  

Publications (* is the corresponding author): 

• I will not update publication list here. 

• Plese visit our website at http://www.urbancomp.net 

[1] Wang, R., Lu, Y.*, Wu, X., Liu, Y., & Yao, Y.*, 2020. Relationship between eye-level greenness and cycling frequency around metro stations in Shenzhen, China: A big data approach. Sustainable Cities and Society, 102201. (SCI)

[2] Zhang, J., Li, X.*, Yao, Y.*, Hong, Y., He, J., Jiang, Z., & Sun, J. 2020. The Traj2Vec model to quantify residents’ spatial trajectories and estimate the proportions of urban land-use types. International Journal of Geographical Information Science, 1-19. (SCI/SSCI)

[3] Yao, Y.*, Qian, C., Hong, Y. etc. 2020. Delineating mixed urban “Jobs-Housing” patterns at a fine scale by using high spatial-resolution remote-sensing imagery. Complexity. DOI: 10.1155/2020/8018629. (SCI)

[4] Wang, R., Yang, B., Liu, P., Zhang, J., Liu, Y., Yao, Y.*, & Lu, Y. 2020. The longitudinal relationship between exposure to air pollution and depression in older adults. International Journal of Geriatric Psychiatry. DOI: 10.1002/gps.5277. (SCI/SSCI)

[5] Zhai, Y.#, Yao Y.#, Guan Q.*, Liang X., Li X., Pan Y., Yue H., Yuan Z., & Zhou J., 2020. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2020.1711915. (SCI/SSCI，# 共同第一作者)

[6] Yao, Y.*, Wu, D., Hong, Y., Chen, D., Liang, Z., Guan, Q., Liang, X., Dai, L. 2020. Analyzing the Effects of Rainfall on Urban Traffic-Congestion Bottlenecks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI: 10.1109/JSTARS.2020.2966591. (SCI)

[7] Chen, D., Zhang, Y., Yao, Y.*, Hong, Y., Guan, Q., Tu, W. 2019. Exploring the spatial differentiation of urbanization on two sides of the Hu Huanyong Line -- based on nighttime light data and cellular automata. Applied Geography. 112(2019): 102081. (SSCI) 

[8] Wang, R., Liu, Y., Lu, Y., Zhang, J., Liu, P., Yao, Y.*, Grekousis, G.*. 2019. Perceptions of built environment and health outcomes for older Chinese in Beijing: A big data approach with street view images and deep learning technique. Computers, Environment and Urban Systems. DOI: 10.1016/j.compenvurbsys.2019.101386. (SSCI)

[9] Yao, Y., Liang, Z., ..., Guan, Q.*. 2019. A human-machine adversarial scoring framework for urban perception assessment using street-view images. International Journal of Geographical Information Science. DOI: 10.1080/13658816.2019.1643024. (SCI/SSCI)

[10] Wang, R., Liu, Y.*, ..., Yao, Y.*. 2019. The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques. International Journal of Health Geographics. DOI: 10.1186/s12942-019-0182-z. (SCI/SSCI)

[11] Wang, R., Yuan, Y., ..., Yao, Y.*. 2019. Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents’ mental health. Health & Place. DOI: 10.1016/j.healthplace.2019.102186. (SCI/SSCI)

[12] Zhang, Y., Li, Q., Tu, W.*, Mai, K., Yao, Y., Chen, Y. 2019. Functional urban land use recognition integrating multi-source geospatial data and cross-correlations. Computers, Environment and Urban System. DOI: 10.1016/j.compenvurbsys.2019.101374. (SSCI)

[13] Wang, R., Helbich, M., Yao, Y., Zhang, J., Liu, P., Yuan, Y.*, & Liu, Y.*. 2019. Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures, Environmental Research. DOI: 10.1016/j.envres.2019.108535. (SCI/SSCI)

[14] Yao, Y., Liu, P., Hong, Y., Liang, Z., Wang, R., Guan, Q.*, & Chen, J. 2019. Fine-scale Intra- and intercity commercial store site recommendations using knowledge transfer. Transactions in GIS. DOI: 10.1111/TGIS.12553. (SSCI)

[15] Wang, R., Lu, Y., Zhang, J., Liu, P., Yao, Y.*, Liu, Y. 2019. The relationship between visual enclosure for neighbourhood street walkability and elders’ mental health in China: Using street view images. Journal of Transport & Health. DOI: 10.1016/j.jth.2019.02.009. (SCI)

[16] Hong, Y. & Yao, Y.* 2019. Hierarchical community detection and functional area identification with OSM roads and complex graph theory. International Journal of Geographical Information Science. DOI: 10.1080/13658816.2019.1584806. (SCI/SSCI)

[17] Marco, H.#, Yao, Y.#, Liu, Y., Zhang, J., Liu, P. & Wang, R.*. 2019. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Enviroment International.DOI: 10.1016/j.envint.2019.02.013. (SCI，# 共同第一作者)

[18] Wang, R., Liu, Y., Xue, D.*, Yao, Y., Liu, P., & Helbich, M. 2019. Cross-sectional associations between long-term exposure to particulate matter and depression in China: The mediating effects of sunlight, physical activity, and neighborly reciprocity. Journal of Affective Disorders. DOI: 10.1016/j.jad.2019.02.007. (SCI)

[19] Wang, R., Chen, H.*, Liu, Y., Lu, Y., Yao, Y. 2019. Neighborhood social reciprocity and mental health among older adults in China: the mediating effects of physical activity, social interaction, and volunteering. BMC Public Health. DOI: 10.1186/s12889-019-7385-x. (SCI)

[20] Yue, H., Guan, Q.*, Pan, Y., Chen, L., Lv, J., & Yao, Y. 2019. Detecting clusters over intercity transportation networks using K-shortest paths and hierarchical clustering: a case study of mainland China. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2019.1566551. (SCI/SSCI)

[21] Yao, Y.*, Chen, D.*, Chen, L., Wang, H. & Guan, Q. 2018. A time series of urban extent in China using DSMP/OLS nighttime light data. PLoS ONE. DOI: 10.1371/journal.pone.0198189. (SCI)

[22] Lv, J., Ma, T., Dong, Z., Yao, Y.* & Yuan, Z. 2018 Temporal and Spatial Analyses of the Landscape Pattern of Wuhan City Based on Remote Sensing Images. ISPRS Int. J. Geo-Inf. 7, 340. (SCI)

[23] He, J., Li, X.*, Yao, Y.*, Hong, Y. & Zhang, J. 2018. Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. International Journal of Geographical Information Science. DOI: 10.1080/13658816.2018.1480783. (SCI)

[24] Liang, X, Liu, X.*, Li, X.*, Chen, Y.*, Tian, H. & Yao, Y. 2018. Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method. Landscape and Urban Planning. 177, 47-63. DOI: j.landurbplan.2018.04.016. (SCI)

[25] Yao, Y*, Hong, Y.*, Wu, D, Zhang, Y. & Guan, Q. 2018. Estimating effects of the "Communities Opening" policy on alleviating traffic congestion in China's big cities by integrating ant colony optimization and complex network analyses. Computers, Environment and Urban System. DOI: 10.1016/j.compenvurbsys.2018.03.005. (SSCI)

[26] Yao, Y.*, Zhang, J.*, Hong, Y., Liang, H., & He, J., 2018. Mapping fine-scale urban housing prices by fusing remote-sensing images and social media data. Transactions in GIS, DOI: 10.1111/tgis.12330. (SCI)

[27] Zhang, D., Liu, X., Wu, X., Yao, Y., Wu, X. & Chen, Y. 2018. Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng, China. GIScience & Remote Sensing, DOI: 10.1080/15481603.2018.1507074. (SCI)

[28] Yao, Y., Liu, X.*, Li, X.*, Liu, P, Hong, Y., Zhang, Y. & Mai, K., 2017. Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata. International Journal of Geographical Information Science, 31(12): 2452-2479. (SCI)

[29] Yao, Y., Li, X.*, Liu, X.*, Liu, P., Liang, Z., Zhang, J. & Mai, K., 2017. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science, 31(4), 825-848. (SCI)

[30] Yao, Y., Liu, X.*, Li, X., Zhang, J., Liang, Z., Mai, K. & Zhang, Y., 2017. Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. International Journal of Geographical Information Science, 31(6), 1220-1244. (SCI)

[31] Liu, X., He, J., Yao, Y.*, Zhang, J., Liang, H., Wang, H., & Hong, Y., 2017. Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science, 31(8): 1675-1696. (SCI)

[32] Chen, Y., Liu, X.*, Li, X., Liu, X., Yao, Y., Hu, G., Xu, X. & Pei, F., 2017. Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method. Landscape and Urban Planning, 160, 48-60. (SCI)

[33] He, Y., Ai, B.*, Yao, Y., & Zhong, F., 2015. Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 38, 164-174. (SCI)

...

Project:

1. Chair the National Natural Science Foundation of China (Grant No. 41801306)

2. Participate in the National Natural Science Foundation of China (Grant No. 41601420)

3. Site recommendation of offline-stores for Alibaba Group Visiting Fellow

4. Typical Application System (TAS) Development -- Affiliated to Venezuelan Remote Sensing Satellite (VRSS-1) Chief Technical Principal

5. African Water Resources Survey -- Affiliated to United Nations Environment Program (UNEP) Principal of the Advanced Remote Sensing Application Subsystem

6. Demand Analysis of GF Series Satellite’s Disaster Reduction Application System -- Affiliated to National 12th Five-Year Plan Chief Technical Principal

7. Chang'e-3 Lunar Probe (CE-3) -- Affiliated to National Deep Space Exploration Plan Principal of the Quality Assessment Subsystem of Satellite Remote Sensing Image