Road extraction in remote sensing data: A survey

GPR monitoring for road transport infrastructure: A systematic review and machine learning insights

Building Information Modeling (BIM) has a crucial role in smart road applications, not only limited to the design and construction stages, but also to traffic monitoring, autonomous vehicle navigation, road condition assessment, and real-time data delivery to drivers, among others Point clouds collected through LiDAR are a powerful solution to capture as-built conditions, notwithstanding the lack of commercial tools able to automatically reconstruct road geometry in a BIM environment This paper illustrates a two-step procedure in which roads are automatically detected and classified, providing GIS layers with basic road geometry that are turned into parametric BIM objects The proposed system is an integrated BIM-GIS with a structure based on multiple proposals, in which a single project file can handle different versions of the model using a variable level of detail The model is also refined by adding parametric elements for buildings and vegetation Input data for the integrated BIM-GIS can also be existing cartographic layers or outputs generated with algorithms able to handle LiDAR data This makes the generation of the BIM-GIS more flexible and not limited to the use of specific algorithms for point cloud processing

Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR

Road markings are of great significance to road inventory management, intelligent transportation systems, high-definition maps (HD Maps), and autonomous driving. Most existing methods focus on extracting and classifying the road markings from mobile laser scanning (MLS) point clouds. Nevertheless, the performance suffers from the wear and incompleteness of road markings. Converting the extracted road marking points into a consistent representation with a sparse set of parameters needs extensive study as well. This paper presents a two-stage coarse-to-fine object detection and localization approach for automatically extracting and modeling road markings from mobile laser scanning (MLS) point clouds, which is robust to variations in reflective intensity, various point density, and partial occlusion. The first step is to use a general object detection network to detect bounding boxes with semantic labels of road markings on feature maps, which consists of information about intensity, elevation, and distance to the scanner. Next, accurate positions, orientations, and scales of candidate road markings are determined in the raw point clouds coordinate system through a shape matching operator that leverages the standard geometric structure and radiometric appearance of road markings. Finally, a re-ranking operator combining the coarse detection confidence and fine localization score is used to acquire the final road marking models. Comprehensive experiments revealed that the proposed method achieved an overall performance of 92.3% in recall and 95.1% in precision for extracting 12 types of road markings from urban scene point cloud datasets, even with worn and incomplete road markings. The modeling performance was 0.504 using the mIoU metric.

A two-stage approach for road marking extraction and modeling using MLS point clouds

Robust cascaded frequency filters to recognize rebar in GPR data with complex signal interference

To meet the urgent demands in a wide range of geospatial applications, such as road management, intelligent transportation systems, road safety evaluation, and traffic accident analysis, automatic and accurate extraction of 3D roads and associated geometric parameters from point clouds is receiving wide attention In this paper, we propose an accurate 3D road boundary extraction and vectorization method to bridge the gap from unstructured mobile laser scanning (MLS) point clouds to the vector-based representation of road boundary Firstly, we propose a supervoxel generation method to extract candidate curbs with fine border preservation and high computation efficiencies Then the candidate curb supervoxels are recognized and clustered to produce continuous road boundary segments with a contracted distance clustering strategy Finally, the vectorized road boundary is represented by fitting, tracking, and completion from the extracted road boundary segments, resulting in road geometric parameters including boundary location, road widths, turning radius, and slopes The performance of the proposed method was evaluated on two large-scale datasets collected in urban and industrial areas Comprehensive experiments reveal that the proposed method is robust to various road shapes and point densities, in terms of precision of 950% and recall of 910%, respectively

Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

. GPRs (Ground Penetrating Radar) are widely adopted in underground space survey and mapping, because of their advantages of fast data acquisition, convenience, high imaging resolution and NDT (Non Destructive Testing) inspection. However, at present, the automation of the GPR data post-processing is low and the identification of underground objects needs expert interpretation. The heavy manual interpretation labor limits the GPR applications in large-scale urban scenarios. According to the latest research, it is still an unsolved problem to detect targets or defects in GPR data automatically and needs further exploration. In this paper, we propose a deep learning method for real-time detection of underground targets from GPR data. Seven typical targets in urban underground space are identified and labelled to construct the training dataset. The constructed dataset is consist of 489 labelled samples including rainwater wells, cables, metal/nonmetal pipes, sparse/dense steel reinforcement, voids. The training dataset is further augmented to produce more samples. DarkNet53 convolutional neural network (CNN) is trained using the constructed training dataset including realistic data and augmented data to extract features of the buried objects. And then the end-to-end YOLO detection framework is used to classify and locate the seven specific categories buried targets in the GPR data in real time. Experiments show that the automatic real-time detection method proposed in this paper can effectively detect the buried objects in the ground penetrating radar image in real time at Shenzhen test site (typical urban road scene).

https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W16/293/2019/isprs-archives-XLII-2-W16-293-2019.pdf

A deep learning approach for urban underground objects detection from vehicle-borne ground penetrating radar data in real-time

. Accurate three-dimensional road structures and models are of great significance to intelligent transportation applications, such as vehicle navigation, inventory evaluation, construction quality control, self-driving vehicles and so on. This paper proposes an efficient and robust method to automatically extract structured road curbs from mobile laser scanning (MLS) data. The proposed method mainly consists of three steps: efficient supervoxel generation, road curbs detection and driving free space estimation. First, supervoxels are generated by assigning ground points with similar geometrical characteristics into the same group. Second, supervoxels with higher local projection density and height difference are identified and clustered as initial road curbs, which are continuous vertical curb facets. The continuous facades consisting of lots of scanned points on the road shoulder can be modeled as multi-dimensional boundary models depending on the requirements of the application, such as vector lines with or without height, micro-facades, etc. Finally, driving free space is obtained due to the road limits can be defined by road boundary in most scenarios. The proposed method is tested on two complex datasets acquired by an Alpha3D mobile laser scanning system from the urban area of Shanghai, China. Experimental results show that the road boundaries and driving free space can be accurately and efficiently extracted, which also demonstrates the superiority of the proposed method.

/pdf/automatic-road-structure-detection-and-vectorization-using-23drwknkk9.pdf

Xiaoxin Mi

Papers

Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

A two-stage approach for road marking extraction and modeling using MLS point clouds

A deep learning approach for urban underground objects detection from vehicle-borne ground penetrating radar data in real-time

Automatic Road Structure Detection and Vectorization Using Mls Point Clouds