Mario Soilán

Automatic Segmentation and Shape-Based Classification of Retro-Reflective Traffic Signs from Mobile LiDAR Data

Recently, many studies have demonstrated the valid contribution of mobile laser scanning to road safety improvements, thus intense efforts have been made to implement automatic data processing using laser scanning data, with special emphasis on road object recognition. This study is focused on the detection and classification of retro-reflective vertical traffic signs according to their function (danger, give way, prohibition/obligation, and indication) from mobile laser scanning data by considering geometric and radiometric information, but without relying on trajectory data. The global strategy for segmentation involves the application of an optimized intensity threshold in order to segment the points that correspond to traffic sign panels. Next, contour recognition is performed for each sign using a linear regression model based on a raster image, which is generated for each cluster of points. The shape evaluation is motivated by the correspondence between contour shape and function of the traffic. The completeness of results for detection (92.11%) and classification (83.91%) demonstrates that this implementation is promising for the automatic detection and inventory analysis of traffic signs in road mapping applications. The efficiency rates are acceptable in urban areas, but our tests indicate that the detection and classification rates are more robust in road environments.

Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems

Abstract This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on their retro-reflective material. Then, classification is carried out over the point cloud projection on RGB images applying a Deep Neural Network which comprises convolutional and spatial transformer layers. This network is evaluated in three European traffic sign datasets. On the GTSRB, it outperforms previous state-of-the-art published works and achieves top-1 rank with an accuracy of 99.71%. Furthermore, a Spanish traffic sign recognition dataset is released.

Automatic extraction of road features in urban environments using dense ALS data

Spanish Ministry of Economy and Competitiveness through the project HERMES:S3D – Healthy and Efficient Routes in Massive Open-data based Smart Cities (Ref.: TIN201346801-C4-4-R); Human Resources program FPI(Grant BES-2014-067736); Fundacion Barrie (Grant holder – Ayudas a la Movilidad Internacional de Jovenes Investigadores de Programas de Doctorado Sistema Universitario de Galicia 2016).

Safety assessment on pedestrian crossing environments using MLS data

In the framework of infrastructure analysis and maintenance in an urban environment, it is important to address the safety of every road user. This paper presents a methodology for the evaluation of several safety indicators on pedestrian crossing environments using geometric and radiometric information extracted from 3D point clouds collected by a Mobile Mapping System (MMS). The methodology is divided in four main modules which analyze the accessibility of the crossing area, the presence of traffic lights and traffic signs, and the visibility between a driver and a pedestrian on the proximities of a pedestrian crossing. The outputs of the analysis are exported to a Geographic Information System (GIS) where they are visualized and can be further processed in the context of city management. The methodology has been tested on approximately 30 pedestrian crossings in cluttered urban environments of two different cities. Results show that MMS are a valid mean to assess the safety of a specific urban environment, regarding its geometric conditions. Remarkable results are presented on traffic light classification, with a global F-score close to 95%.

Automatic Parametrization and Shadow Analysis of Roofs in Urban Areas from ALS Point Clouds with Solar Energy Purposes

A basic feature of modern and smart cities is their energetic sustainability, using clean and renewable energies and, therefore, reducing the carbon emissions, especially in large cities. Solar energy is one of the most important renewable energy sources, being more significant in sunny climate areas such as the South of Europe. However, the installation of solar panels should be carried out carefully, being necessary to collect information about building roofs, regarding its surface and orientation. This paper proposes a methodology aiming to automatically parametrize building roofs employing point cloud data from an Aerial Laser Scanner (ALS) source. This parametrization consists of extracting not only the area and orientation of the roofs in an urban environment, but also of studying the shading of the roofs, given a date and time of the day. This methodology has been validated using 3D point cloud data of the city of Santiago de Compostela (Spain), achieving roof area measurement errors in the range of ±3%, showing that even low-density ALS data can be useful in order to carry out further analysis with energetic perspective.