Foto von Peng Luo

Peng Luo

Peng is a Ph.D. candidate at the Chair of Cartography, Technical University of Munich, and former visiting researcher at the University of Oxford. He earned his master's in Remote Sensing from Peking University in 2020. 

Peng’s research is focused on spatially explicit modeling of big data in urban analytics, including issues related to spatial sparsity and distribution bias. He has been deploying deep learning methods to enhance the predictability of both explicit and implicit socio-economic attributes in urban areas, and exploring human-nature interactions in urban spaces with the goal of advancing sustainable urban development.

Research Interests

  • Spatial association
  • GeoAI
  • Computational urban science

Supervision and Lecture

Master Thesis

  • Mapping Population Distribution at Building Level Considering Multi Data Sources (Zhuo Yang, 2022, Cartography) 
  • Predicting the building age using Mapillary street-view images (Tao Wu, 2023, ESPACE)
  • Species Habitat Mapping Based on Semantic Segmentation of Multiband Images:
    A Case Study of the Sandpiper Family in Taiwan (Lingjun Wang, 2023, ESPACE)
  • A Comprehensive Study on Bike Sharing Mobility in the City of Munich: Utilizing Community Detection Method (Yagiz Kara, 2023, Cartography)

Seminar Cartography (MC)

  • Identifying spatial determinants of Covid-19 in Germany (Xinyue Yang, 2022)
  • Spatiotemporal analysis and community detection of dockless e-scooter sharing (Chengyu Song, 2022)


  • Geoinformation (exercise, Master Course)- Technical University of Munich
  • Advanced Remote Sensing and Geospatial Techniques (GEOG 8350,Master Course)- University of Geogria (Guest lecturer)


Journal Publication (Google Scholar)

Leading author (* corresponding author/ project lead)

[1] Luo, P., Song, Y., Zhu, D., Cheng J. and Meng L. A Generalized Spatial Heterogeneity Model for Interpolation.2022. International Journal of Geographical Information Science, 37(3), 634-659 (Top 10 read at 2023)

[2] Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y. and Meng, L., 2022. Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing, 185, pp.111-128 

[3] Luo, P., Song, Y. and Wu, P., 2021. Spatial disparities in trade-offs: economic and environmental impacts of road infrastructure on continental level. GIScience and Remote Sensing, 58(5), pp.756-775. 

[4] Li, Y.#Luo, P.#(co-first), Song, Y., Zhang, L., Qu, Y and Hou, Z., 2023. A locally explained heterogeneity model for examining wetland disparity. International Journal of Digital Earth, 13(2), p.4533-4552.

[5] Luo, P., Zhang, X., Cheng, J. and Sun, Q., 2019. Modeling population density using a new index derived from
multi-sensor image data. Remote Sensing, 11(22), p.2620.

[6] Yao, Y., Dong, A., Liu, Z., Jiang, Y., Guo, Z., Cheng, J., Guan, Q. and Luo, P.*., 2023. Extracting the pickpocketing information implied in the built environment by treating it as the anomalies. Cities, 143, p.104575. 

[7] Yao Y., Lei S., Guo., Li Y., Ren S., Liu Z., Guan Q.,Luo, P.*. Fast urban logistics optimization based on hybrid
sparrow search algorithm. International Journal of Geographical Information Science.1-29 

[8]  Yao, Y., Yan, X., Luo, P.*, Liang, Y.*, Ren, S., Hu, Y., Han, J. and Guan, Q., 2022. Classifying land-use patterns by integrating time-series electricity data and high-spatial resolution remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 106, p.102664.

[9]  Yao, Y., Guo, Z., Dou, C., Jia, M., Hong, Y., Guan, Q.* and Luo, P.*, 2023. Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism. Computers, Environment and Urban Systems, 104, p.102009. 

[10]  Yao, Y., Feng, C., Xie, J., Yan, X., Guan, Q., Han, J., Zhang, J., Ren, S., Liang, Y., Luo, P*. A site selection framework for urban power substation at micro-scale using spatial optimization strategy and geospatial big data. Transactions in GIS. (Cover paper)

Other publication

[1] Dong, A., Zhang, Y., Guo, Z., Luo, P., Yao, Y., He, J., Zhu, Q., Jiang, Y., Xiong, K. and Guan, Q., 2024. Predicting the locations of missing persons in China by using NGO data and deep learning techniques. International Journal of Digital Earth17(1), p.2304076.

[2] Zhang Z, Song Y, Luo, P.. GeoComplexity explains spatial errors. International Journal of Geographical Information Science (Most read at 2023)

[3] Zhang, Z., Song, Y., Luo, P. et al. Spatial disparities of factors affecting air pollutant emissions in industrial regions on continental level. International journal of applied earth observation and geoinformation.117 (2023): 103221. 

[4] Cheng, T., Zhao, Y., Song, Y., Ma, L., Zhang, Z., Luo, P., Gao, P., Zhang, M. and Zhao, C., 2023. Towards resilience effectiveness: Assessing its patterns and determinants to identify optimal geographic zones. Journal of Cleaner Production429, p.139596.

[5] Cheng, J., Zhang, X., Chen, X., Ren, M., Huang, J. and Luo, P., 2022. Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data. ISPRS International Journal of Geo-Information , 11(9), p.478.

[6] Cheng, J., Zhang, X., Luo, P., Huang, J., Huang J. An unsupervised approach for semantic place annotation of
trajectories based on the prior probability. Information Sciences, 607, 1311-1327 

[7] Yang, W., Zhang, X. and Luo, P., 2021. Transferability of convolutional neural network models for identifying damaged buildings due to earthquake. Remote Sensing, 13(3), p.504.

[8] Cheng, J., Zhang, X., Sun, M., Luo, P. and Yang, W., 2020. Random forest model for the estimation of fractional
vegetation coverage based on a UAV-ground co-sampling strategy. Acta Scientiarum Naturalium Universitatis Pekinensis. 56(1), pp.143-154



[1] Luo, P., Song,Y., Meng, L,. Revisting the role of distance for spatial prediction. Abstracts of the ICA, 6, p.148.

[2] Luo, P., Li Y. Explainable spatial heterogeneity model for association analysis. The 30th International Conference of Geoinformatics 2023

[3] Luo, P., Zhu D. Overlapping Community detection based on unsupervised GCN. ACM SIGSPATIAL 2022 GeoAI

[4] Luo, P. and Song, Y., 2021. A spatial second-order non-stationary interpolation method for large area
mapping.Abstracts of the ICA, 3, p.187.

[5] Luo, P. and Zhang, X., 2019.Gridded population density estimation based on multi-source remote sensing data and POI data. Joint EOEC-GiT4NDM 2019 Conference


  1. Luo, P. Sensing the unseen nature of cities. Senseable City Lab, MIT. (2023.10). Invited.
  2. Luo, P. Uncover the overlapping nature of cities. Center for Spatial Information Science, The University of Tokyo. (2023.10). Invited.
  3. Luo, P. Location defined and redefined: the spectrum of spatial dependence. International conferences for early career researchers and PhD students on urban studies. (2023.09). Invited.
  4. Luo, P. Revisiting the role of distance for spatial prediction. The 30th International Cartographic Conference (ICC), Capetown, South Africa. (2023.08)
  5. Luo, P. A generalized association model explains geo-nonlinear interaction. The 30th International Conference on Geoinformatics. London, UK. (2023.07)
  6. Luo, P. Generalized spatial association modeling for intelligent spatial understanding. Environmental Change Institute, University of Oxford. (2023.05). Invited.
  7. Luo, P. (in Chinese) 面向智能空间理解的空间关系建模,北京大学第四届遥感地信青年论坛, 北京。(2023.04)。
  8. Luo, P. (in Chinese) 空间关系建模与预测,空间统计课题组(王劲峰教授),资源与环境信息系统国家重点实验室, (2023.02)。特邀报告。
  9. Luo, P. (in Chinese) 广义空间异质性建模, 地理孪生与地理智能研讨会,资源与环境信息系统国家重点实验室, (2022.12)。特邀报告。
  10. Luo, P. Sensing overlapping geospatial communities from human movements using graph affiliation generation models. The 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems,
  11. Seattle, USA. (2022.11)
  12. Luo, P. Optimization for large scale logistics using the hybrid sparrow search algorithm.The 17th International Conference on Location Based Services, Munich, Germany. (2022.09)
  13. Luo, P. A spatial second-order non-stationary interpolation method for large area mapping. The 30th International Cartographic Conference (ICC). Florence, Italy. (2021.11)
  14. Luo, P. A tree- based spatial stratified model. Curtin University. (2021.04). Invited.
  15. Luo, P. Gridded population density estimation based on multi-source remote sensing data and POI data. EOEC 2019 and GiT4NDM, Chengdu, China. (2019.07)



  • Urban Climate (2023- Present)
  • Accident Analysis and Prevention (2023- Present)
  • Geo-spatial Information Science (2023- Present)
  • Transaction in GIS (2023- Present)
  • International Journal of Digital Earth (2023- Present)
  • Sustainable Cities and Society (2022- Present)
  • International Journal of Applied Earth Observation and Geoinformation (2022- Present)
  • Aerosol and Air Quality Research (2022- Present)
  • Scientific Reports (2021- Present)
  • Buildings(2021- Present)
  • PLOS ONE (2021- Present)
  • ISPRS International Journal of Geo-Information (2021- Present)
  • Forests (2021- Present)
  • Remote Sensing (2021- Present)
  • IEEE Sensors Journal (2021- Present)
  • Sustainability (2021- Present)