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Publications

For a full list of publications, please refer to Google Scholar: Google Scholar Profile

2026

  1. Li, H., Wang, F., Zhang, R., Qin, A., Javan, E., Reddy, R., Harper, L., Hung, P. and Kang, Y., 2026. Measuring maternal healthcare accessibility in Florida by a data-driven extension of V2SFCA. Health & Place, 98, p.103608. [DOI] [PDF]
  2. Kang, Y. and Wang, C., 2026. Envisioning generative artificial intelligence in cartography: mapmaking, map use, and ethics. International Journal of Cartography, pp.1-27. [DOI] [PDF]
  3. Kang, Y., Chen, J., Liu, L., Sharma, K., Mazzarello, M., Mora, S., Duarte, F. and Ratti, C., 2026. Decoding human safety perception with eye-tracking systems, street view images, and explainable AI. Computers, Environment and Urban Systems, 123, p.102356. [DOI] [PDF]

2025

  1. Guo, S., Jang, K.M., Duarte, F., Kang, Y. and Ratti, C., 2025. Urban visual uniqueness: A landmark-free framework to quantify city’s identity and distinctiveness from everyday scenes. Computers, Environment and Urban Systems, 122, p.102351. [DOI] [PDF]
  2. Abraham, J., Kang, Y., Ceccato, V., Näsman, P., Duarte, F., Gao, S., Ljungqvist, L., Zhang, F. and Ratti, C., 2025. Crime and visually perceived safety of the built environment: A deep learning approach. Annals of the American Association of Geographers, 115(7), pp.1613-1633. [DOI] [PDF]
  3. Ceccato, V., Kang, Y., Abraham, J., Näsman, P., Duarte, F., Gao, S., Ljungqvist, L., Zhang, F. and Ratti, C., 2025. What makes a place safe? Assessing AI-generated safety perception scores using Stockholm’s street view images. The British Journal of Criminology. [DOI] [PDF]
  4. Wang, C., Kang, Y., Gong, Z., Zhao, P., Feng, Y., Zhang, W. and Li, G., 2025. CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation. International Journal of Geographical Information Science, 39(9), pp.1904-1937. [DOI] [PDF]
  5. Chen, Y., Nelson, J.K., Zhou, B., Zhou, R.Z., Ye, S., Liu, H., Gu, Z., Kar, A., Kwon, H., Chen, P., Sun, M. and Kang, Y., 2025. Where are GIScience faculty hired from? Analyzing faculty mobility and research themes through hiring networks. Cartography and Geographic Information Science. [DOI] [PDF]
  6. Qiao, S., Fang, X., Wang, J., Zhang, R., Li, X. and Kang, Y., 2025. Generative AI for thematic analysis in a maternal health study: coding semistructured interviews using large language models. Applied Psychology: Health and Well-Being, 17(3). [DOI] [PDF]
  7. Sun, Y., Peng, W., Zheng, Q. and Kang, Y., 2025. Quantitative assessment of autonomous boats in harmful algal control: unveiling effectiveness and uncertainty. Big Earth Data. [DOI] [PDF]
  8. Qin, A., Kang, Y., Li, H., Wang, S., Zhou, B., Wang, F. and Hung, P., 2025. Leveraging reinforcement learning for maternity care resource reallocation: A case study in Florida. Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, pp.1106-1109. [DOI] [PDF]
  9. Kang, Y., Wang, C., Feng, Y., Touya, G. and Kim, J., 2025. Artificial intelligence for cartography and maps. In GeoAI and Human Geography: The Dawn of a New Spatial Intelligence Era, pp.219-237. [DOI] [PDF]
  10. Wang, J., Tan, H., Liao, B., Jiang, A., Fei, T., Huang, Q., Tu, Z., Ye, S. and Kang, Y., 2025. SounDiT: Geo-contextual soundscape-to-landscape generation. arXiv preprint. [DOI] [PDF]
  11. Wang, Y., Xing, S., Can, C., Li, R., Hua, H., Tian, K., Mo, Z., Gao, X., Wu, K., Zhou, S. and Kang, Y., 2025. Generative AI for autonomous driving: Frontiers and opportunities. arXiv preprint. [DOI] [PDF]
  12. Hou, C., Zhang, F., Kang, Y., Fan, Z. and Li, S., 2025. Transferring population group knowledge from multimodal large language model to small model: using urban safety perception evaluation as case study. Research Square preprint. [DOI] [PDF]
  13. Kang, Y., 2025. Human-centered geospatial data science. arXiv preprint. [DOI] [PDF]
  14. Hou, C., Zhang, F., Li, Y., Li, H., Mai, G., Kang, Y., Yao, L., Yu, W., Yao, Y., Gao, S. and Chen, M., 2025. Urban sensing in the era of large language models. The Innovation, 6(1). [DOI] [PDF]
  15. Huang, Y., Sanatani, R.P., Liu, C., Kang, Y., Zhang, F., Liu, Y., Duarte, F. and Ratti, C., 2025. No “true” greenery: Deciphering the bias of satellite and street view imagery in urban greenery measurement. Building and Environment, 269, p.112395. [DOI] [PDF]
  16. Lefosse, D.C., Duarte, F., Sanatani, R.P., Kang, Y., van Timmeren, A. and Ratti, C., 2025. Feeling Nature: Measuring perceptions of biophilia across global biomes using visual AI. npj Urban Sustainability, 5(1), p.4. [DOI] [PDF]
  17. Qiu, Y., Wu, M., Huang, Q. and Kang, Y., 2025. Do You Know Your Neighborhood? Integrating Street View Images and Multi-task Learning for Fine-Grained Multi-Class Neighborhood Wealthiness Perception Prediction. Cities, 158, p.105703. [DOI] [PDF]
  18. Knoblauch, S., Muthusamy, R.K., Moritz, M., Kang, Y., Li, H., Lautenbach, S., Pereira, R.H., Biljecki, F., Gonzalez, M.C., Barbosa, R. and Hirata, D.V., 2025. Crime-associated inequality in geographical access to education: Insights from the municipality of Rio de Janeiro. Cities, 160, p.105818. [DOI] [PDF]
  19. Hou, C., Zhang, F., Kang, Y., Gao, S., Li, Y., Duarte, F. and Li, S., 2025. Transferred Bias Uncovers the Balance Between the Development of Physical and Socioeconomic Environments in Cities. Annals of the American Association of Geographers. [DOI] [PDF]
  20. Gu, Z., Li, W., Zhou, B., Wang, Y., Chen, Y., Ye, S., Wang, K., Gu, H. and Kang, Y., 2025. GISphere Knowledge Graph for geography education: Recommending graduate Geographic Information System/Science programs. Transactions in GIS, 29(1), p.e13283. [DOI] [PDF]

2024

  1. Zhuang, Y., Kang, Y., Fei, T., Bian, M. and Du, Y., 2024. From hearing to seeing: Linking auditory and visual place perceptions with soundscape-to-image generative artificial intelligence. Computers, Environment and Urban Systems, 110, p.102122. [DOI] [PDF]
  2. Jang, K.M., Chen, J., Kang, Y., Kim, J., Lee, J., Duarte, F. and Ratti, C., 2024. Place identity: a generative AI’s perspective. Humanities and Social Sciences Communications, 11(1). [DOI] [PDF]
  3. Ito, K., Kang, Y., Zhang, Y., Zhang, F. and Biljecki, F., 2024. Understanding urban perception with visual data: A systematic review. Cities, 152, p.105169. [DOI] [PDF]