山洪灾害具有突发性、局地性和空间分异显著等特征，其易发性制图是识别潜在高风险区域、支撑防灾减灾与国土空间规划的重要基础。传统山洪易发性制图（Flash Flood Susceptibility Mapping，FFSM）多基于静态地形因子与经验统计模型，在极端降雨事件频发及下垫面快速演变时，面临空间泛化能力弱、非线性空间关系刻画不足等局限。结合当前山洪风险精细化管理的迫切需求，基于风险感知视角提出“空间认知-空间感知-智能制图”3 阶递进式研究架构，深度解析FFSM由经验建模向混合智能演进的理论脉络。首先，界定易发性的核心内涵，山洪易发性本质上是介于环境背景与灾害事件之间的潜在空间响应状态，其制图核心在于评估流域在相似触发条件下的空间相对倾向；其次，通过构建“内在孕灾环境”与“外在致灾触发”的空间认知二元结构，辨明山洪风险制图中可观测的地理空间要素，以拓展空天地多源遥感观测在FFSM因子表征中的感知维度。研究同时发现，易发性制图方法的演进映射了空间感知维度的提升，地理空间人工智能（Geospatial Artificial Intelligence，GeoAI）正推动着制图研究从表层“因子驱动”向深层“非线性空间 关联”转型。探讨并提出了融合GeoAI 隐式表达能力与物理过程约束的混合智能制图框架，指出在数据驱动与物理守恒定律间建立协同机制，是提升复杂情景下制图稳健性与可解释性的关键路径。本研究旨在推动山洪易发性制图由经验导向向认知驱动与精准智治转型，为山地城市精细化防灾减灾提供科学支撑。

Abstract： Flash floods pose severe threats due to their abruptness and spatial heterogeneity. Traditional Flash Flood Susceptibility Mapping (FFSM) relies on static factors and empirical models, which lack spatial generalization and physical constraints under frequent extreme precipitation events and rapidly changing environments. To address the urgent need for fine-grained flash flood risk management, this study proposes a three-stage progressive research framework—comprising “spatial cognition, spatial perception, and intelligent mapping”—from the perspective of risk perception. It provides an in-depth analysis of the theoretical trajectory of FFSM’s evolution from empirical modeling to hybrid intelligence. A comprehensive analysis yields the following insights: First, flash flood susceptibility is fundamentally a potential spatial response state mediating between the environmental background and the disaster event; the core of its mapping lies in evaluating the relative spatial propensity of a watershed under similar triggering conditions. Second, by constructing a binary spatial cognition structure consisting of an “intrinsic environment” and “extrinsic triggers”, this study clarifies the observable geospatial elements in flash flood risk mapping. This structure broadens the application dimensions of space-air-ground integrated multi-source remote sensing observations in the representation of FFSM factors. Furthermore, the evolution of susceptibility mapping methods reflects an elevation in the dimensions of spatial perception. Geospatial Artificial Intelligence (GeoAI) is profoundly driving the transition of mapping research from surface-level “factor-driven” paradigms to the exploration of deep “nonlinear spatial associations”. Finally, this study systematically explores a hybrid intelligent mapping framework that integrates the implicit representational capabilities of GeoAI with physical process constraints. It highlights that establishing a synergistic mechanism between data-driven approaches and physical conservation laws is a crucial pathway for enhancing the robustness and interpretability of mapping under complex scenarios. Ultimately, this study aims to propel the paradigm shift in flash flood susceptibility mapping from experience-oriented to cognition-driven and precise intelligent governance, thereby providing robust scientific support for fine-grained disaster prevention and mitigation in mountainous cities.