Pole-NN: Few-Shot Classification of Pole-Like Objects in Lidar Point Clouds

In the realm of autonomous systems and smart-city initiatives, accurately detecting and localizing pole-like objects (PLOs) such as electrical poles and traffic signs has become crucial. Despite their significance, the diverse nature of PLOs complicates their accurate recognition. Point cloud data and 3D deep learning models offer a promising approach to PLO localization under varied lighting, addressing issues faced by camera systems. However, the distinct characteristics of different street scenes worldwide require infeasibly extensive training data for satisfactory results because of the nature of deep learning. This prohibitively increases the cost of lidar data capture and annotation. This paper introduces a novel few-shot learning framework for the classification of outdoor point cloud objects, leveraging a minimalistic approach that requires only a single support sample for effective classification. Central to our methodology is the development of Pole-NN, a Non-parametric Network that efficiently distinguishes between various PLOs and other road assets without the need for extensive training datasets traditionally associated with deep learning models. Additionally, we present the Parkville-3D Dataset, an annotated point cloud dataset we have captured and labelled, which addresses the notable scarcity of fine-grained PLO datasets. Our experimental results demonstrate the potential of our approach to utilize the intrinsic spatial relationships within point cloud data, promoting a more efficient and resource-conscious strategy for PLO classification

Automatic detection to inventory road slopes using open LiDAR point clouds

The transport infrastructure of a country facilitates the development and growth of its economy and improves the quality of life of its inhabitants. Increasing its resilience to different types of risks to improve performance is becoming more important. In the current context of climate change, natural hazards are more severe and frequent. In this article, we focus on rockfall as a natural hazard for roads that occurs in small areas in the vicinity of natural or cut slopes, causing road safety problems by invading part of the road. This article aims to inventory the slopes along the road, identifying the area of the road which would be invaded in case of a rockfall. A methodology divided into two blocks is proposed. First, for slope detection and inventory, an algorithm is developed based on open LiDAR point clouds analysis. The second block consists of estimating the invaded road area if a rockfall occurs on each of the inventoried slopes, using a combination of RockGIS software and the Monte Carlo method. The methodology was applied in five case studies: three sections on motorways and two sections on national roads. The results obtained for slope detection show higher rates in the case studies analyzing motorways, with a precision of 100%, a recovery rate of greater than 93.4%, and an F1 score of greater than 0.96. The results in the invaded area of the road show that 11 slopes would cause a total cut of the motorway in one of the directions if a rockfall occurs. These results are useful for infrastructure managers to remotely obtain an inventory of road slopes and know which of them would affect road safety. Also, the results can serve as input for the Intelligent Transportation System and allow the exchange of information under the Building Information Model approach.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I00Ministerio de Ciencia, Innovación y Universidades | Ref. PRE2020-096222European Commission | Ref. H2020, n. 95533

Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data

The digital mapping of road environment is an important task for road infrastructure inventory and urban planning. Automatic extraction and classification of pole-like objects can remarkably reduce mapping cost and enhance work efficiency. Therefore, this paper proposes a voxel-based method that automatically extracts and classifies three-dimensional (3-D) pole-like objects by analyzing the spatial characteristics of objects. First, a voxel-based shape recognition is conducted to generate a set of pole-like object candidates. Second, according to their isolation and vertical continuity, the pole-like objects are detected and individualized using the proposed circular model with an adaptive radius and the vertical region growing algorithm. Finally, several semantic rules, consisting of shape features and spatial topological relationships, are derived for further classifying the extracted pole-like objects into four categories (i.e., lamp posts, utility poles, tree trunks, and others). The proposed method was evaluated using three datasets from mobile LiDAR point cloud data. The experimental results demonstrate that the proposed method efficiently extracted the pole-like objects from the three datasets, with extraction rates of 85.3%, 94.1%, and 92.3%. Moreover, the proposed method can achieve robust classification results, especially for classifying tree trunks.Optical and Laser Remote Sensin