Core Viewpoint - The presentation by Dr. Liang Haojian focuses on optimizing urban emergency fire facility allocation using a hierarchical deep reinforcement learning (DRL) approach, highlighting the advantages and potential of deep learning in geographic spatial optimization [1][4][17]. Geographic Spatial Optimization - Geographic spatial optimization combines mathematical combinatorial optimization with geographic information science, addressing practical issues such as spatial layout and resource allocation in urban development [4][5]. - Traditional methods for solving spatial optimization problems include exact algorithms, approximate algorithms, and heuristic algorithms, each with its limitations [4][5]. Deep Learning in Geographic Spatial Optimization - The exploration of neural spatial optimization (NeurSPO) aims to utilize deep learning to solve spatial optimization problems, motivated by the need for faster heuristic methods and the automatic design of new algorithms [6]. - Two main constructs of NeurSPO are deep construction and deep improvement, focusing on stepwise solution construction and local search enhancements, respectively [6]. SpoNet Model - The SpoNet model integrates dynamic coverage information and attention mechanisms to address location selection challenges, allowing the model to focus on specific input sequences during decoding [7][11]. - In a case study involving emergency facilities in Beijing's Chaoyang District, the model selected 20 out of 132 candidate facilities to maximize coverage [11]. AIAM Model - The adaptive interaction attention model (AIAM) is designed to solve the p-median problem by incorporating user-facility interaction, enhancing local search capabilities [12][16]. - The model demonstrated feasibility by retaining 15 hospitals from 80 candidates to minimize total distance to residents [16]. Hierarchical DRL for Fire Facility Allocation - The hierarchical DRL approach addresses the challenges of urban emergency fire facility allocation by improving fire risk prediction accuracy, optimizing resource allocation, and enhancing response timeliness [17][21]. - The model incorporates multi-dimensional spatiotemporal feature extraction, uncertainty considerations, and a hierarchical strategy for facility layout optimization [18][21][22]. Future Outlook - The research team plans to enhance geographic spatial optimization by integrating geographic computing mechanisms, expanding to large-scale emergency response issues, and designing more efficient DRL frameworks [23][24][25]. - The proposed hierarchical DRL method aims to address inefficiencies in traditional fire facility layouts and improve emergency management through innovative solutions [25].