scholarly journals SIDEWALK DETECTION AND PAVEMENT CHARACTERISATION IN HISTORIC URBAN ENVIRONMENTS FROM POINT CLOUDS: PRELIMINARY RESULTS

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 243-249
Author(s):  
D. Treccani ◽  
L. Díaz-Vilariño ◽  
A. Adami
Keyword(s):  
International Organizations ◽  
Point Cloud ◽  
Point Clouds ◽  
Northern Italy ◽  
Urban Environments ◽  
Policy Makers ◽  
Urban Heritage ◽  
Unesco Site ◽  
Definition Of ◽  
Physical Accessibility

Abstract. The definition of physical accessibility in urban environments is a topic of recognized importance by policy makers and by international organizations. A first step to address the accessibility topic is the definition and characterisation of urban elements, like sidewalks, roads, and ramps. Sidewalk inventory plays a crucial role in this phase. In literature there are several ways to extract sidewalks from a point cloud, but they are all tailored on modern and standardized situations. For example the presence of a curb is assumed as the normality and the roads are supposed to have the same width along the path. When dealing with an Urban Heritage, some difficulties arise. In fact, in an historic urban environment ground irregularities should be taken in consideration: the paving is composed by different materials, curbs are not always present, and a Z difference between road and sidewalks is not so sure. In such cases existing methodologies cannot be applied. This paper present a method to semantically segment a point cloud, labelling sidewalks and roads. Sidewalks are also characterized by detecting their pavings. The method is tested on an Urban Heritage: the Unesco site of Sabbioneta, in northern Italy. The results are promising, sidewalks are detected with a precision of 80%, main errors are in corner areas. Paving characterisation is based on thresholds derived from some samples, and the method shows an high precision (more than 90%) in all the pavings considered.

scholarly journals SEMANTIC SEGMENTATION OF BUILDING ELEMENTS USING POINT CLOUD HASHING

2018 ◽  
Vol XLII-2 ◽  
pp. 241-250 ◽  
Author(s):  
M. Chizhova ◽  
A. Gurianov ◽  
M. Hess ◽  
T. Luhmann ◽  
A. Brunn ◽  
...  
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Probabilistic Networks ◽  
Component Design ◽  
Semantic Modelling ◽  
Segmentation Approach ◽  
Novel Method ◽  
Human Interpretation ◽  
Definition Of

For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).

scholarly journals AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 31-38
Author(s):  
A. Nurunnabi ◽  
F. N. Teferle ◽  
J. Li ◽  
R. C. Lindenbergh ◽  
A. Hunegnaw
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Ground Surface ◽  
Point Clouds ◽  
Urban Environments ◽  
Training Data ◽  
Ground Point ◽  
End To End

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.

scholarly journals Direct local building inundation depth determination in 3D point clouds generated from user-generated flood images

2017 ◽  
Author(s):  
Luisa Griesbaum ◽  
Sabrina Marx ◽  
Bernhard Höfle
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Point Clouds ◽  
Urban Environments ◽  
Extreme Weather Events ◽  
Inundation Depth ◽  
Area Of Interest ◽  
3D Point Clouds ◽  
Flood Level ◽  
Flood Elevation

Abstract. In recent years, the number of people affected by flooding caused by extreme weather events has increased considerably. In order to provide support in disaster recovery or to develop mitigation plans, accurate flood information is necessary. Particularly pluvial urban floods, characterized by high temporal and spatial variations, are not well documented. This study proposes a new, low-cost approach to determining local flood elevation and inundation depth of buildings based on user-generated flood images. It first applies close-range digital photogrammetry to generate a geo-referenced 3D point cloud. Second, based on estimated camera orientation parameters, the flood level captured in a single flood image is mapped to the previously derived point cloud. The local flood elevation and the building inundation depth can then be derived automatically from the point cloud. The proposed method is carried out once for each of 66 different flood images showing the same building façade. An overall accuracy of 0.05 m  with an uncertainty of ±0.13 m for the derived flood elevation within the area of interest and an accuracy of 0.13 m  ± 0.10 m for the determined building inundation depth is achieved. Our results demonstrate that the proposed method can provide reliable flood information on a local scale using user-generated flood images as input. The approach can thus allow inundation depth maps to be derived even in complex urban environments with relatively high accuracies.

scholarly journals Walkable Urban Environments: An Ergonomic Approach of Evaluation

2020 ◽  
Vol 12 (20) ◽  
pp. 8347
Author(s):  
Letizia Appolloni ◽  
Alberto Giretti ◽  
Maria Vittoria Corazza ◽  
Daniela D’Alessandro
Keyword(s):  
Urban Environment ◽  
Urban Policy ◽  
Central Italy ◽  
Well Being ◽  
Urban Environments ◽  
Model Structure ◽  
Policy Makers ◽  
Definition Of

Background. The salutogenicity of urban environments is significantly affected by their ergonomics, i.e., by the quality of the interactions between citizens and the elements of the built environment. Measuring and modelling urban ergonomics is thus a key issue to provide urban policy makers with planning solutions to increase the well-being, usability and safety of the urban environment. However, this is a difficult task due to the complexity of the interrelations between the urban environment and human activities. The paper contributes to the definition of a generalized model of urban ergonomics and salutogenicity, focusing on walkability, by discussing the relevant parameters from the large and variegated sets proposed in the literature, by discussing the emerging model structure from a data mining process, by considering the background of the relevant functional dependency already established in the literature, and by providing evidence of the solutions’ effectiveness. The methodology is developed for a case study in central Italy, with a focus on the mobility issue, which is a catalyst to generate more salutogenic and sustainable behaviors.

scholarly journals Application of A Hand-Held LiDAR Scanner for the Urban Cadastral Detail Survey in Digitized Cadastral Area of Taiwan Urban City

2021 ◽  
Vol 13 (24) ◽  
pp. 4981
Author(s):  
Shih-Hong Chio ◽  
Kai-Wen Hou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Calibration Method ◽  
Urban Environments ◽  
Control Points ◽  
Cadastral Maps ◽  
Urban City ◽  
3D Point Clouds ◽  
Check Points ◽  
Overlap Analysis

The cadastral detail data is used for overlap analysis with digitized graphic cadastral maps to solve the problem of inconsistencies between cadastral maps and the current land situation. This study investigated the feasibility of a handheld LiDAR scanner to collect 3D point clouds in an efficient way for a detail survey in urban environments with narrow and winding streets. Then, urban detail point clouds were collected by the handheld LiDAR scanner. After point cloud filtering and the ranging systematic error correction that was determined by a plane-based calibration method, the collected point clouds were transformed to the TWD97 cadastral coordinate system using control points. The land detail line data were artificially digitized and the results showed that about 97% error of the digitized detail positions was less than 15 cm compared to the check points surveyed by a total station. The results demonstrated the feasibility of using a handheld LiDAR scanner to perform an urban cadastral detail survey in digitized graphic areas. Therefore, the handheld LiDAR scanner could be used for the production of the detail lines for urban cadastral detail surveying for digitized cadastral areas in Taiwan.

scholarly journals Ground-distance segmentation of 3D LiDAR point cloud toward autonomous driving

2020 ◽  
Vol 9 ◽  
Author(s):  
Jian Wu ◽  
Qingxiong Yang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Ground Plane ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Cloud Data ◽  
Dense Point ◽  
3D Lidar

In this paper, we study the semantic segmentation of 3D LiDAR point cloud data in urban environments for autonomous driving, and a method utilizing the surface information of the ground plane was proposed. In practice, the resolution of a LiDAR sensor installed in a self-driving vehicle is relatively low and thus the acquired point cloud is indeed quite sparse. While recent work on dense point cloud segmentation has achieved promising results, the performance is relatively low when directly applied to sparse point clouds. This paper is focusing on semantic segmentation of the sparse point clouds obtained from 32-channel LiDAR sensor with deep neural networks. The main contribution is the integration of the ground information which is used to group ground points far away from each other. Qualitative and quantitative experiments on two large-scale point cloud datasets show that the proposed method outperforms the current state-of-the-art.

scholarly journals LOCALIZATION OF PATHOLOGY ON COMPLEX ARCHITECTURE BUILDING SURFACES

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 617-621 ◽  
Author(s):  
A. A. Sidiropoulos ◽  
K. N. Lakakis ◽  
V. K. Mouza
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Normal Vector ◽  
Northern Greece ◽  
Complex Building ◽  
Building Surfaces ◽  
Definition Of ◽  
Complex Architecture

The technology of 3D laser scanning is considered as one of the most common methods for heritage documentation. The point clouds that are being produced provide information of high detail, both geometric and thematic. There are various studies that examine techniques of the best exploitation of this information. In this study, an algorithm of pathology localization, such as cracks and fissures, on complex building surfaces is being tested. The algorithm makes use of the points’ position in the point cloud and tries to distinguish them in two groups-patterns; pathology and non-pathology. The extraction of the geometric information that is being used for recognizing the pattern of the points is being accomplished via Principal Component Analysis (PCA) in user-specified neighborhoods in the whole point cloud. The implementation of PCA leads to the definition of the normal vector at each point of the cloud. Two tests that operate separately examine both local and global geometric criteria among the points and conclude which of them should be categorized as pathology. The proposed algorithm was tested on parts of the Gazi Evrenos Baths masonry, which are located at the city of Giannitsa at Northern Greece.

scholarly journals Direct local building inundation depth determination in 3-D point clouds generated from user-generated flood images

2017 ◽  
Vol 17 (7) ◽  
pp. 1191-1201 ◽  
Author(s):  
Luisa Griesbaum ◽  
Sabrina Marx ◽  
Bernhard Höfle
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Point Clouds ◽  
Urban Environments ◽  
Extreme Weather Events ◽  
Inundation Depth ◽  
Area Of Interest ◽  
Flood Level ◽  
Camera Orientation ◽  
Flood Elevation

Abstract. In recent years, the number of people affected by flooding caused by extreme weather events has increased considerably. In order to provide support in disaster recovery or to develop mitigation plans, accurate flood information is necessary. Particularly pluvial urban floods, characterized by high temporal and spatial variations, are not well documented. This study proposes a new, low-cost approach to determining local flood elevation and inundation depth of buildings based on user-generated flood images. It first applies close-range digital photogrammetry to generate a geo-referenced 3-D point cloud. Second, based on estimated camera orientation parameters, the flood level captured in a single flood image is mapped to the previously derived point cloud. The local flood elevation and the building inundation depth can then be derived automatically from the point cloud. The proposed method is carried out once for each of 66 different flood images showing the same building façade. An overall accuracy of 0.05 m with an uncertainty of ±0.13 m for the derived flood elevation within the area of interest as well as an accuracy of 0.13 m ± 0.10 m for the determined building inundation depth is achieved. Our results demonstrate that the proposed method can provide reliable flood information on a local scale using user-generated flood images as input. The approach can thus allow inundation depth maps to be derived even in complex urban environments with relatively high accuracies.

scholarly journals IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS

2019 ◽  
Vol IV-4/W9 ◽  
pp. 35-42 ◽  
Author(s):  
Y. Dehbi ◽  
L. Lucks ◽  
J. Behmann ◽  
L. Klingbeil ◽  
L. Plümer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Support Vector ◽  
Mobile Mapping ◽  
General Applicability ◽  
Data Set ◽  
Building Models

Abstract. Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.

scholarly journals HIERARCHICAL PROXIMITY-BASED OVER-SEGMENTATION OF 3-D POINT CLOUDS FOR EFFICIENT GRAPH FEATURE DETECTION

2020 ◽  
Vol V-2-2020 ◽  
pp. 273-280
Author(s):  
Z. Shtain ◽  
S. Filin
Keyword(s):  
Point Cloud ◽  
Surface Characterization ◽  
Feature Detection ◽  
Point Clouds ◽  
Free Form ◽  
Natural Scenes ◽  
General Strategy ◽  
Computationally Efficient ◽  
Definition Of ◽  
Point Cloud Simplification

Abstract. Point cloud simplification is empowered by the definition of similarity metrics which we aim to identify homogeneous regions within the point-cloud. Nonetheless, the variety of shapes and clutter in natural scenes, along with the significant resolution variations, occlusions, and noise, contribute to inconsistencies in the geometric properties, thereby making the homogeneity measurement challenging. Thus, the objective of this paper is to develop a point-cloud simplification model by means of data segmentation and to extract information in a better-suited way. The literature shows that most approaches either apply volumetric data strategies and/or resort to simplified planar geometries, which relate to only part of the entities found within a natural scene. To provide a more general strategy, we propose a proximity-based approach that allows an efficient and reliable surface characterization with no limitation on the number or shape of the primitives which in turn, enables detecting free-form objects. To achieve this, a local, computationally efficient and scalable metric is developed, which captures resolution variation and allows for short processing time. Our proposed scheme is demonstrated on datasets featuring a variety of surface types and characteristics. Experiments show high precision rates while exhibiting robustness to the varying resolution, texture, and occlusions that exist within the sets.

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