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

Soilán, Mario; Truong-Hong, Linh; Riveiro, Belén; Laefer, Debra F.

doi:10.1016/j.jag.2017.09.010

Cited by 38 publications

(25 citation statements)

References 40 publications

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“…Both the top and bottom lines were extracted from the proposed method, allowing for the curb tridimensional modeling, unlike the methods described in References [14][15][16], where only the bottom line was extracted.…”

Section: Dataset Results Comparisonmentioning

confidence: 99%

“…The street boundaries result from a segmentation algorithm based on morphological operations. Soilán et al [16] use a section-wise approach to obtain a curb map, which is computed through an unsupervised learning algorithm. Despite the results' robustness claimed by several authors [14][15][16], the resulting curbs are represented only by one line, with a total absence of information regarding the curb height, which disqualifies its integration in Triangular Irregular Network (TIN) models.…”

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confidence: 99%

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Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

Gézero

Antunes

2019

IJGI

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In last decades, Mobile Light Detection And Ranging (LiDAR) systems were revealed to be an efficient and reliable method to collect dense and precise point clouds. The challenge now faced by researchers is the automatic object extraction from those point clouds, such as the curb break lines, which are essential to road rehabilitation projects and autonomous driving. Throughout this work, an efficient method to extract road curb break lines from mobile LiDAR point clouds is presented. The proposed method was based on the system working principles instead of an algorithmic application over the cloud as a mass of points. The point cloud was decomposed in the original sensor scan profiles. Then, a GPS epoch versus trajectory distance was used to eliminate most non-ground points. Finally, through a vertical monotone chain decomposition, candidate point arrays were created and the curb break lines are formed. The proposed method was shown to be able to avoid the occlusion effect caused by undergrowth. The method allows for distinguishing between right and left curbs and works on curved curbs. Both top and bottom tridimensional break lines were extracted. When compared with a reference manual method, in the tested dataset, the proposed method allowed for a decrease in the curb break lines extraction time from 25 min to less than 30 s. The extraction method provided completeness and correctness rates above 95% and 97%, respectively, and a quality value higher than 93%.Typically, when obtained using photogrammetric methods or a field survey, the break line designs are time consuming.Mobile Light Detection And Ranging (LiDAR) is a widely disseminated technology that allows for gathering dense and precise point clouds. Initially installed on aerial platforms, the static and mobile terrestrial systems rapidly emerged, becoming a crucial source of georeferenced data. However, LiDAR is a non-selective technique, i.e., the georeferenced point clouds represent the sensor's surrounding physical reality at an acquisition moment, indiscriminately, with no classification of terrain, vegetation, buildings, or any other object within the system range. The automatic classification and object extraction from those point clouds became a noteworthy challenge for researchers.Since the initial LiDAR systems, numerous techniques for automatic break lines extraction have been presented. Brugelmann [6] shortly describes some methods for break lines extraction from airborne point clouds, testing an approach proposed by Forstner [7]. In that approach, all pixels with a significant noise homogeneity measure are denoted as potential edge pixels. The quadratic variation, used as a homogeneity measure, indicates the extent of the curvature. Although the resulting lines have a lower quality when compared with the photogrammetrically measured break lines, the author showed that automatic break line extraction from airborne laser data is feasible.Other approach based on two intersecting planes as fitted functions can extract some 3D lines, but they nee...

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Section: Dataset Results Comparisonmentioning

confidence: 99%

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confidence: 99%

Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

Gézero

Antunes

2019

IJGI

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“…This way, a non-salient point cloud can be defined. Let S = (P, i) be a function that selects a subset i of points (being i a list of indices) within a point cloud P, as defined originally in [15]. Thus, given the indices of non-salient points i ns , the point cloud that will be processed in the following steps will be P ns = S(P, i ns ).…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Although TLS data is much denser than ALS and therefore the potential of this data source to capture small features with high resolution is higher, a terrestrial scan cannot collect geometric information about the building roofs as they will be always occluded. Therefore, Aerial data has to be employed, whose densities typically vary between 1-30 points per m 2 to higher densities such as the ALS dataset presented in [13], which averages 200 points per m 2 by maximizing data coverage on building facades, flying at a low altitude and orientating flight paths at 45 • with the major axes of the city streets, making possible a precise segmentation of building facades and roofs [14,15]. Other applications of ALS data are the extraction of the road network centerlines [16,17] or terrain recognition [18,19].…”

Section: Introductionmentioning

confidence: 99%

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

Soilán

Riveiro

Liñares

et al. 2018

IJGI

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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.

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“…In addition, for ensuring dynamics and accuracy, rapid city development requires frequent road updating, and the growing development of aerial technologies also provides an efficient, low-cost and reliable solution to receive dynamical road information. Besides aerial images, there are also other kinds of remote sensing data can be used for road extraction, such as hyperspectral images (HSI) [1,2], synthetic aperture radar (SAR) data [3][4][5], airborne laser scanning (ALS) data [6][7][8] and mobile laser scanner (MLS) data [9][10][11]. In this paper, we only focus on aerial images.…”

Section: Introductionmentioning

confidence: 99%

Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

Zhang

Han

et al. 2019

Remote Sensing

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Aerial photographs and satellite images are one of the resources used for earth observation. In practice, automated detection of roads on aerial images is of significant values for the application such as car navigation, law enforcement, and fire services. In this paper, we present a novel road extraction method from aerial images based on an improved generative adversarial network, which is an end-to-end framework only requiring a few samples for training. Experimental results on the Massachusetts Roads Dataset show that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision and F1-score.

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

Cited by 38 publications

References 40 publications

Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

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

Aerial Image Road Extraction Based on an Improved Generative Adversarial Network

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