Hybrid deep learning and geometric algorithms for individual object detection in urban LiDAR point clouds

Accurate extraction of individual urban objects from aerial LiDAR point clouds presents a complex challenge due to the heterogeneity of object types, occlusions, and overlapping geometries. This research paper introduces a hybrid framework that combines deep learning and geometric algorithms for the detection and classification of both structured and unstructured urban objects within LiDAR point clouds, supported by auxiliary RGB and satellite data. The methodology incorporates a multistage pipeline: initial point cloud classification using neural architectures such as RandLANet and PointPillars, followed by semantic segmentation using YOLOv7, YOLOv11, and SegFormer, and refined object extraction using rule-based and geometric filtering (e.g. DBSCAN, RANSAC). A data set of over 3.1 billion classified points, derived from Estonian LiDAR and OSM data, was prepared and augmented to address the detection of small and overlapping objects. Experimental results demonstrate high accuracy for structured classes (e.g. buildings: 88.3%) and effective segmentation of unstructured objects (e.g. trees: 85.0%). Cascaded networks achieved up to 72.6% classification accuracy depending on neighbourhood size. The 9-channel data fusion (RGB + LiDAR features) significantly improved segmentation performance. Comparative evaluation shows that combining model specialization with rule-based metaclass grouping improves object detection granularity and classification consistency, providing a scalable approach to fine-grained object recognition in smart city and geospatial analysis applications.