activity
20232025
most citedGeo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages

187 citations · 258 across the 5 of their papers we have counts for

collaborators

5 papers

physics.soc-ph2025

Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires

Jiyeon Kim, Yingjie Hu, Negar Elhami-Khorasani +2

Predicting the spread of wildfires is essential for effective fire management and risk assessment. With the fast advancements of artificial intelligence (AI), various deep learning…

cs.HC2025

Where are GIScience Faculty Hired from? Analyzing Faculty Mobility and Research Themes Through Hiring Networks

Yanbing Chen, Jonathan Nelson, Bing Zhou +9

Academia is profoundly influenced by faculty hiring networks, which serve as critical conduits for knowledge dissemination and the formation of collaborative research initiatives.…

cs.CY2024★ 30 cited

PyGRF: An improved Python Geographical Random Forest model and case studies in public health and natural disasters

Kai Sun, Ryan Zhenqi Zhou, Jiyeon Kim +1

Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretati…

cs.CY2023★ 41 cited

Conflating point of interest (POI) data: A systematic review of matching methods

Kai Sun, Yingjie Hu, Yue Ma +2

Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban mana…

cs.CY2023★ 187 cited

Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages

Yingjie Hu, Gengchen Mai, Chris Cundy +6

Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many…