Minho Kim
Read more About Me and my CV.

Minho Kim

I’m a PhD candidate in Environmental Planning at UC Berkeley advised by Professors Marta C. Gonzalez and John Radke. I am a member of HumNet Lab. My research focuses on building data-driven methods to model and predict natural hazard risks on our built and natural environment. To this end, I use GeoAI methods, computational modeling, and spatial networks to develop decision support tools that guide risk-informed decisions, improve wildfire risk management, and build resilience in complex socio-ecological systems like the Wildland Urban Interface.

I received my BS and MS degrees in Civil and Environmental Engineering at Seoul National University, where I was advised by Professor Yongil Kim and was a member of the SPINS Lab.

Recent News

Oct 2025
[Talk] I was invited to present on wildfires and climate change at Georgia Tech
July 2025
[Award] I was awarded a scholarship by the Association of Environmental Planners San Francisco Bay Area Chapter!
June 2025
[Paper] Our paper on "Community-scale microclimate simulation and object-based urban tree classification" was published in Landscape and Urban Planning.
June 2025
[Paper] Our paper on "Fire Spread Simulations Using Cell2Fire on Synthetic and Real Landscapes" was accepted to Scientific Reports.
May 2025
[Research] Our proposal on "Multi-scale mitigation of wildfire risk vulnerabilities in the natural and built environment" was awarded the 2025 Lau Grant.
July 2024
[Talk] I was invited to present on WUI risk modeling and fire suppression strategies at the Catalan Fire Service (Spain).
July 2024
[Talk] I was invited to present on fire spread modeling and fire suppression strategies at Wageningen University

Highlighted Research

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Redefining defensible spaces using spatial responsibility of wildfire mitigation

Developed novel spatial metrics (shared & owed responsibility) to mitigate at individual homeowner scale and built spatial networks to simulate mitigation scenarios in the wildland urban interface. Paper

Natural Hazard Network Science Urban Wildfire
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Fire potential polygons and suppression networks

Hydrology-inspired risk management method to create decision support networks for more effctive, proactive suppression strategies. Paper

Natural Hazard Network Science Wildfire
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Fire spread simulations in synthetic and real landscapes

Comprehensive analysis of a cellular automata fire spread simulator on synthetic and real landscapes in Canada, US, and Chile with optimization to improve simulation accuracy. Paper

Natural Hazard Wildfire
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Mapping high resolution local climate zones using multi-scale attention models

Developed a novel multi-scale, multi-layer attention model to classify high resolution local climate zones in South Korea using multispectral satellite imagery, building information, and terrain data. Paper

Remote Sensing Urban Machine Learning
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Mapping local climate zones with deep learning

Developed a multi-scale module in a feedforward neural network to map local climate zones in Seoul, South Korea (Presented at NeurIPS 2020: AI4Earth) Paper Presentation

Remote Sensing Urban Machine Learning
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Forecasting solar power using deep learning and geostationary satellite data

Short-term forecast (1 to 3-day ahead) forecast of photovoltaic power using deep learning with COMS and Himawari-8 geostationary satellite images. Paper

Remote Sensing Urban Machine Learning