We propose an integrated approach for automatic point cloud thinning and curb mapping in Uncrewed Aerial Vehicle - Structure from Motion (UAV-SfM) point clouds to enhance hydrological modeling in flood-prone urban areas. UAV flights were conducted to generate an initial orthoimage, which was used to train a convolutional neural network (CNN) segmentation model. The trained model was then applied to the UAV images to produce two binary mask sets: one for vegetation and one for streets and sidewalks. These masks were incorporated during photogrammetric 3D reconstruction to estimate camera geometry and generate a dense point cloud. Our results show that vegetation masks did not improve camera geometry estimation. However, by applying UAV masks, we achieved a 15% reduction in total processing time and decreased the number of points by a factor of 2.7. This targeted approach enabled curb detection by focusing on expected curb locations. Curb candidate points were proposed using geometric characteristics of the point cloud, including normal values, linearity, and verticality. Our rule-based method effectively mapped even subtle curb features, providing a rapid, cost-effective solution for large-area curb mapping. Further, we explored the potential of random forest for curb mapping, with promising results. Our approach can support urban flood modeling efforts and strengthen urban resilience for flood-prone communities.