With the in-depth penetration of Unmanned Aerial Vehicle (UAV) technology in fields such as geographic information surveying and mapping, the urban low-altitude economy has ushered in a critical opportunity for rapid development. However, surveying and mapping UAVs are confronted with the core technical bottleneck of "accurately determining the safety of flight routes", while issues such as airspace congestion and collision risks have become increasingly prominent. How to enable UAVs to perceive the complex environment in which they operate has thus emerged as a key challenge. Based on remote sensing images, 3D geographic data, and other relevant datasets, this study comprehensively applies multi-source data fusion technology to construct the theoretical framework and technical system of the "Urban Low-Altitude Surveying UAV Flight Safety Zones". It classifies the core causal factors affecting flight safety into three major categories: building height, electromagnetic interference, and controlled areas. Through a series of key technical processes, a standardized 3D spatial grid system is established, and a computable "risk perception" model is developed. Taking Shanghai Municipality as the empirical research area, empirical analysis is conducted using remote sensing images, ultimately generating a low-altitude flight safety field grid dataset covering the entire administrative region of Shanghai. Verification results indicate that this achievement can effectively identify the spatial distribution characteristics of potential safety hazards and height constraints, significantly reduce the collision probability between UAVs and obstacles, and provide standardized technical support for the flight safety of low-altitude surveying UAVs.