Founder · Principal Investigator
Mingze Chen
Ph.D. Candidate, University of British Columbia · Visiting, Senseable City Lab, MIT
Mingze Chen is the founder of Nature AI Lab. His research focuses on the interdisciplinary application of big data and machine learning in urban nature analytics. He completed the MArch in Urban Design at University College London (UCL), UK. His interests span AI-driven human behaviour monitoring, multi-scale urban vitality and environmental justice, and computational design and data visualization.
Research Themes
AI-driven Human Behaviour Monitoring and Analytics in Urban and Natural Environments
Applying sensor- and vision-based technologies — smart sensors, computer vision, vision-language models, and crowdsourced imagery — to monitor human volumes, trajectories, and activities in urban and natural environments, and to link them to health and wellbeing.
Multi-scale Urban Vitality and Environmental Justice
Using big data (street view imagery, social media, GPS, POI, socio-economic data) and machine learning across scales to evaluate urban vitality, greenery distribution, and environmental justice.
Computational Design, Mapping, and Visualization
Developing Rhino-Grasshopper / Python / GIS / web-based workflows for parametric design, mapping, and readable data visualization, and reviewing emerging technologies and AI in urban design and planning.
Peer-Reviewed Articles
Theme I · AI-driven Human Behaviour Monitoring & Analytics
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SCI/SSCIChen, Mingze., Liu, Yuxuan., Liu, Fan., Chadha, Trishla., Park, Keunhyun. Measuring pedestrian-level street greenery visibility through space syntax and crowdsourced imagery: A case study in London, UK. Urban Forestry & Urban Greening 2025.DOI ↗
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SCIZhang, Xiamengwei., Chen, Mingze*., Huang, Yongming. Who gets to use the street? Evaluate the utilization and inclusiveness using crowdsourced videos and vision-language models. Sustainable Cities and Society 2025.DOI ↗
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SCI/SSCIChen, Mingze*., Yuan, Fan. A systematic review of measurement tools and senior engagement in urban nature: Health benefits and behavioral patterns analysis. Health & Place 2025.DOI ↗
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SCILuo, Tianze., Chen, Mingze*. Advancements in supervised machine learning for outdoor thermal comfort: A comprehensive systematic review of scales, applications, and data types. Energy and Buildings 2025.DOI ↗
Theme II · Multi-scale Urban Vitality & Environmental Justice
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SCI/SSCIChen, Mingze., Cai, Yuxuan., Guo, Shuying., Sun, Ruilin., Song, Yang., Shen, Xiwei. Evaluating implied urban nature vitality in San Francisco: An interdisciplinary approach combining census data, street view images, and social media analysis. Urban Forestry & Urban Greening 2024.DOI ↗
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SSCIYang, Jiaxi., Chen, Mingze*. Assessing the impact of urban amenities on people with disabilities in London: A multiscale geographically weighted regression analysis. Habitat International 2025.DOI ↗
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SCI/SSCIHu, Yequan., Chen, Mingze*., Cai, Yuxuan. Comparative analysis of greenery inequalities in New York and London: Social-economic and spatial dimensions. Urban Forestry & Urban Greening 2025.DOI ↗
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SCI/SSCIZhang, Kaiqi., Chen, Mingze*. Multi-method analysis of urban green space accessibility: Influences of land use, greenery types, and individual characteristics factors. Urban Forestry & Urban Greening 2024.DOI ↗
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SSCIHuang, Yongming., Du, Jiani., Chen, Mingze*., Lin, Yuxuan., Huang, Shaopo., Cai, Yuxuan. Evaluating the spatial-temporal impact of urban nature on urban vitality in Vancouver: A social media and GPS data approach. Land Use Policy 2025.DOI ↗
Theme III · Computational Design, Mapping & Visualization
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SCIHe, Wei., Chen, Mingze*. Advancing urban life: A systematic review of emerging technologies and AI in urban design and planning. Buildings 2024.DOI ↗
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SCI/SSCIGe, Mengting., Kong, Jie., Yang, Qiuyi., Chen, Mingze*., Wang, Wenji. Examine an Intelligence Education Framework of Landscape Architecture (EFLA) based on a Network Model of Technology in Landscape Architecture (NMTLA). Sustainability 2023.DOI ↗
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Chen, Mingze., Zhang, Yang., Yang, Yubing., Fang, Zhiguo. Application of data visualization in urban design based on Grasshopper. Landscape Architecture Academic Journal 2022, 39(5), 44–51. (in Chinese)DOI ↗
Conference Presentations
- Chen, Mingze., Park, Keunhyun. Low-cost Bluetooth sensing for monitoring visitor volumes and movements in urban nature. 2026 CELA — Oral Presentation (Cincinnati, OH)
- Chen, Mingze., Park, Keunhyun. Urban human–nature interaction: A deep learning approach to global social media imagery. 2025 ACSP — Oral Presentation (Minneapolis, MN)
- Chen, Mingze., Park, Keunhyun. Understanding human–nature interaction through AI and crowdsourced imagery: A case study of Vancouver. 2025 BCSLA — Oral Presentation (Vancouver, BC)
- Chen, Mingze., Park, Keunhyun. Usability of low-cost smart sensors for monitoring visitor volumes and movements in urban parks. 2025 CELA — Oral Presentation (Portland, OR)
- Chen, Mingze., Huang, Yongming., Zheng, Yuqiao., Du, Jiani., Park, Keunhyun. Defining urban vitality using text-based, image-based, and GPS data. 2024 ACSP — Oral Presentation (Seattle, WA)
- Chen, Mingze., Park, Keunhyun. Sensor-based and vision-based technologies in monitoring human behaviours in natural areas: A scoping review. 2024 CELA — Oral Presentation (St. Louis, MI)
Book Chapters
- Magical Foodscape: A Guidebook for Re-planning the Cities Based on the Culture, Food and the Built Environment. (pp. 010–055). Advanced Books, 1, e129204. Section Editors. DOI ↗
- Agro-Matrix. In M.Arch. UD 2021–2022 (pp. 148–159). Bartlett School of Architecture, UCL.