Unmanned Aerial Vehicles (UAVs) are increasingly operating in complex urban environments, requiring robust path planning methods that balance safety and efficiency. This work presents a general-purpose methodology for generating safe UAV flight routes by transforming spatial risk heatmaps into geospatial graphs. Using superpixel segmentation, the heatmaps are discretised into risk-weighted regions that form graph nodes, with edges representing spatial proximity and weighted by Euclidean distance. Classical pathfinding algorithms — Dijkstra, A*, and Ant Colony Optimisation — are applied to compute optimal or near-optimal paths that balance both distance and risk exposure. Experiments conducted over a large urban area in São Paulo, Brazil, demonstrate that graph-based routing effectively incorporates environmental risk data, with Dijkstra and A* offering superior computational performance suitable for real-time applications. The approach is modular, scalable, and adaptable to various heatmap types, providing a flexible foundation for UAV traffic management and autonomous navigation in dense airspace.