Lei Zou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Lei Zou has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Dingli Liu
School of Transportation, Changsha University of Science & Technology, Changsha 410114, China
2 shared publications - Diping Yuan
Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518057, China
2 shared publications - Guohua Wu
Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
2 shared publications - Weijun Liu
Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, China
2 shared publications - Diping Yuan
Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518057, China
1 shared publication - Dongfang Shang
Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518057, China
1 shared publication
Latest Publications
- Open Access
A Similarity-Constrained Sampling Framework for Early-Stage Forest Fire Susceptibility Mapping in Sample-Scarce Regions
Forest fire events in coastal eastern Guangdong are strongly associated with human activities and often occur as small-scale, short-lived early-stage fire events. Given uneven thermal monitoring infrastructure, a key challenge is leveraging data-rich local areas to identify high-susceptibility areas in data-sparse regions. Focusing on Shanwei City, this study used fire data from thermal imaging in the Shenzhen–Shanwei Special Cooperation Zone. By integrating biophysical covariates and anthropogenic factors, we developed a Randomized Iterative Sampling–Similarity Assessment–Model Selection (RISAMS) framework to optimize negative-sample selection and identify a robust classifier. Shapley Additive exPlanations (SHAP) was implemented in Python 3.13.5 using the SHAP package (version 0.48.0) and was used to interpret drivers of susceptibility. In the optimal iteration, mean covariate similarity between training samples and the Shanwei macro-environmental background reached 0.94, with a minimum of 0.72. The optimal Random Forest classifier achieved an AUC of 0.92 on the training set and 0.91 on the test set, with Cohen’s Kappa of 0.78 for both. High-susceptibility zones clustered along forest edges and wildland–urban interfaces in northern and central Shanwei. SHAP identified road density, elevation, and slope as the primary drivers, with a cumulative relative importance of 61.87%. This framework supports early-stage forest fire susceptibility mapping under uneven monitoring resources and improves early warning of early-stage fires in human-dominated humid coastal forests.
Open AccessA Similarity-Constrained Sampling Framework for Early-Stage Forest Fire Susceptibility Mapping in Sample-Scarce Regions
Forest fire events in coastal eastern Guangdong are strongly associated with human activities and often occur as small-scale, short-lived early-stage fire events. Given uneven thermal monitoring infrastructure, a key challenge is leveraging data-rich local areas to identify high-susceptibility areas in data-sparse regions. Focusing on Shanwei City, this study used fire data from thermal imaging in the Shenzhen–Shanwei Special Cooperation Zone. By integrating biophysical covariates and anthropogenic factors, we developed a Randomized Iterative Sampling–Similarity Assessment–Model Selection (RISAMS) framework to optimize negative-sample selection and identify a robust classifier. Shapley Additive exPlanations (SHAP) was implemented in Python 3.13.5 using the SHAP package (version 0.48.0) and was used to interpret drivers of susceptibility. In the optimal iteration, mean covariate similarity between training samples and the Shanwei macro-environmental background reached 0.94, with a minimum of 0.72. The optimal Random Forest classifier achieved an AUC of 0.92 on the training set and 0.91 on the test set, with Cohen’s Kappa of 0.78 for both. High-susceptibility zones clustered along forest edges and wildland–urban interfaces in northern and central Shanwei. SHAP identified road density, elevation, and slope as the primary drivers, with a cumulative relative importance of 61.87%. This framework supports early-stage forest fire susceptibility mapping under uneven monitoring resources and improves early warning of early-stage fires in human-dominated humid coastal forests.
- Open Access
- 9 March 2026
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 15 (3) , 115
Spatio-Temporal Analysis of Regional Fire Service Accessibility for Underground Parking Garages
Underground parking garages in high-density megacities are high-risk environments where strong confinement and large fire loads pose severe safety threats. In this study, an evaluation model is proposed based on the entropy weight method combined with dynamic traffic conditions to determine the regional fire service accessibility index . Taking Shenzhen, a megacity in China, as the study area, POI data were used to identify 510 fire stations as supply points and 3378 underground parking garages as demand points, yielding 165,522 samples across 49 evaluation scenarios. The results show that the overall average travel time, distance, and velocity are 388.17 s, 2217.95 m, and 5.84 m/s. fluctuates between 0.572 and 0.813, demonstrating clear time-of-day differences. The overall average for all 49 scenarios is 0.697, corresponding to Grade “C”, representing the general level of regional fire service accessibility. It is recommended that additional fire resources be deployed during peak hours and that fire station layouts in peripheral areas be optimized to improve fire safety in underground parking garages.- 9 March 2026
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 15 (3) , 115
Open AccessSpatio-Temporal Analysis of Regional Fire Service Accessibility for Underground Parking Garages
Underground parking garages in high-density megacities are high-risk environments where strong confinement and large fire loads pose severe safety threats. In this study, an evaluation model is proposed based on the entropy weight method combined with dynamic traffic conditions to determine the regional fire service accessibility index . Taking Shenzhen, a megacity in China, as the study area, POI data were used to identify 510 fire stations as supply points and 3378 underground parking garages as demand points, yielding 165,522 samples across 49 evaluation scenarios. The results show that the overall average travel time, distance, and velocity are 388.17 s, 2217.95 m, and 5.84 m/s. fluctuates between 0.572 and 0.813, demonstrating clear time-of-day differences. The overall average for all 49 scenarios is 0.697, corresponding to Grade “C”, representing the general level of regional fire service accessibility. It is recommended that additional fire resources be deployed during peak hours and that fire station layouts in peripheral areas be optimized to improve fire safety in underground parking garages. - Open Access
Block-Unit-Based Method for Delineating Fire Station Response Zones Using Real-Time Traffic Data
The effective delineation of fire station response zones is critical for urban public safety planning, yet traditional methods often fail to account for dynamic traffic conditions, leading to suboptimal resource allocation. This study proposes a novel block-unit-based method that incorporates real-time traffic data to delineate fire station response zones, improving the scientificity of response time estimation. The method was validated using data from Daxiang District, China, a typical urban–rural mixed region, encompassing 2230 block units, 4 fire stations, and 13,097 demand points. Analysis of 1,225,047 data samples revealed an average travel time of 960.7 s, highlighting significant accessibility challenges. The re-delineated response zones cover areas ranging from 1.07 to 156.24 km2, with significant variations. It is attributed to the concentration of fire stations in urban areas, insufficient coverage of vast rural regions, and the proximity of one station to a river and regional boundary. These findings underscore the spatial inequities in fire service provision and the need for a more balanced resource allocation strategy. Recommendations include establishing rural fire stations, improving urban traffic conditions, and relocating certain fire stations. This approach can enhance regional accessibility and provides a scientific basis for fire service planning.
Open AccessBlock-Unit-Based Method for Delineating Fire Station Response Zones Using Real-Time Traffic Data
The effective delineation of fire station response zones is critical for urban public safety planning, yet traditional methods often fail to account for dynamic traffic conditions, leading to suboptimal resource allocation. This study proposes a novel block-unit-based method that incorporates real-time traffic data to delineate fire station response zones, improving the scientificity of response time estimation. The method was validated using data from Daxiang District, China, a typical urban–rural mixed region, encompassing 2230 block units, 4 fire stations, and 13,097 demand points. Analysis of 1,225,047 data samples revealed an average travel time of 960.7 s, highlighting significant accessibility challenges. The re-delineated response zones cover areas ranging from 1.07 to 156.24 km2, with significant variations. It is attributed to the concentration of fire stations in urban areas, insufficient coverage of vast rural regions, and the proximity of one station to a river and regional boundary. These findings underscore the spatial inequities in fire service provision and the need for a more balanced resource allocation strategy. Recommendations include establishing rural fire stations, improving urban traffic conditions, and relocating certain fire stations. This approach can enhance regional accessibility and provides a scientific basis for fire service planning.