scholarly journals BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS

2015 ◽  
Vol II-3/W5 ◽  
pp. 393-400 ◽  
Author(s):  
M. Awrangjeb ◽  
C. S. Fraser ◽  
G. Lu
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Component Analysis ◽  
Detection Technique ◽  
Building Detection ◽  
Point Cloud Data ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Cloud Data ◽  
Detection Techniques

Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

scholarly journals AUTOMATED BUILDING DETECTION USING RANSAC FROM CLASSIFIED LIDAR POINT CLOUD DATA

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 115-121
Author(s):  
D. L. Bool ◽  
L. C. Mabaquiao ◽  
M. E. Tupas ◽  
J. L. Fabila
Keyword(s):  
Point Cloud ◽  
The Philippines ◽  
Building Detection ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Past ◽  
Sample Area ◽  
The Difference ◽  
Ransac Algorithm ◽  
Initial List

<p><strong>Abstract.</strong> For the past 10 years, the Philippines has seen and experienced the growing force of different natural disasters and because of this the Philippine governement started an initiative to use LiDAR technology in the forefront of disaster management to mitigate the effects of these natural phenomenons. The study aims to help the initiative by determining the shape, number and distribution and location of buildings within a given vicinity. The study implements a Python script to automate the detection of the different buildings within a given area using a RANSAC Algorithm to process the Classified LiDAR Dataset. Pre-processing is done by clipping the LiDAR data into a sample area. The program starts by using the a Python module to read .LAS files then implements the RANSAC algorithm to detect roof planes from a given set of parameters. The detected planes are intersected and combined by the program to define the roof of a building. Points lying on the detected building are removed from the initial list and the program runs again. A sample area in Pulilan, Bulacan was used. A total of 8 out of 9 buildings in the test area were detected by the program and the difference in area between the generated shapefile and the digitized shapefile were compared.</p>

Change detection in drill and blast tunnels from point cloud data

2018 ◽  
Vol 105 ◽  
pp. 172-181 ◽  
Author(s):  
Gabriel Walton ◽  
Mark S. Diederichs ◽  
Klaus Weinhardt ◽  
Dani Delaloye ◽  
Matthew J. Lato ◽  
...  
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Drill And Blast

scholarly journals Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

2019 ◽  
Vol 11 (6) ◽  
pp. 729 ◽  
Author(s):  
Shiyan Pang ◽  
Xiangyun Hu ◽  
Mi Zhang ◽  
Zhongliang Cai ◽  
Fengzhu Liu
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Laser Scanner ◽  
Graph Cuts ◽  
Aerial Images ◽  
Processing Unit ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Surface Models

Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.

scholarly journals FUSION OF LIDAR DATA AND MULTISPECTRAL IMAGERY FOR EFFECTIVE BUILDING DETECTION BASED ON GRAPH AND CONNECTED COMPONENT ANALYSIS

2015 ◽  
Vol XL-3/W2 ◽  
pp. 65-72 ◽  
Author(s):  
S. A. N. Gilani ◽  
M. Awrangjeb ◽  
G. Lu
Keyword(s):  
Component Analysis ◽  
Airborne Lidar ◽  
Connected Components ◽  
Lidar Data ◽  
Building Detection ◽  
Multispectral Imagery ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Dense Vegetation ◽  
Black Pixel

Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building’s occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m<sup>2</sup> and 10 m<sup>2</sup> in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m<sup>2</sup> in area.

scholarly journals CLASSIFIER-FREE DETECTION OF POWER LINE PYLONS FROM POINT CLOUD DATA

2017 ◽  
Vol IV-4/W4 ◽  
pp. 81-87 ◽  
Author(s):  
M. Awrangjeb ◽  
M. K. Islam
Keyword(s):  
Point Cloud ◽  
Power Line ◽  
Point Cloud Data ◽  
Connected Component ◽  
Cloud Data ◽  
Symmetry Properties ◽  
Dense Point ◽  
Subsequent Step ◽  
Important Means ◽  
Power Line Corridor

High density airborne point cloud data has become an important means for modelling and maintenance of a power line corridor. Since, the amount of data in a dense point cloud is huge even in a small area, an automatic detection of pylons in the corridor can be a prerequisite for efficient and effective extraction of wires in a subsequent step. However, the existing solutions mostly overlook this important requirement by processing the whole data into one go, which nonetheless will hinder their applications to large areas. This paper presents a new pylon detection technique from point cloud data. First, the input point cloud is divided into ground and nonground points. The non-ground points within a specific low height region are used to generate a pylon mask, where pylons are found stand-alone, not connected with any wires. The candidate pylons are obtained using a connected component analysis in the mask, followed by a removal of trees by comparing area, shape and symmetry properties of trees and pylons. Finally, the parallelism property of wires with the line connecting pair of candidate pylons is exploited to remove trees that have the same area and shape properties as pylons. Experimental results show that the proposed technique provides a high pylon detection rate in terms of completeness (100&amp;thinsp;%) and correctness (100&amp;thinsp;%).

scholarly journals Real-Time HD Map Change Detection for Crowdsourcing Update Based on Mid-to-High-End Sensors

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2477
Author(s):  
Pan Zhang ◽  
Mingming Zhang ◽  
Jingnan Liu
Keyword(s):  
Change Detection ◽  
Real Time ◽  
Point Cloud ◽  
Measurement Unit ◽  
Point Cloud Data ◽  
Integrated Navigation ◽  
High Definition ◽  
Cloud Data ◽  
Computing Platform ◽  
Semantic Point

Continuous maintenance and real-time update of high-definition (HD) maps is a big challenge. With the development of autonomous driving, more and more vehicles are equipped with a variety of advanced sensors and a powerful computing platform. Based on mid-to-high-end sensors including an industry camera, a high-end Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU), and an onboard computing platform, a real-time HD map change detection method for crowdsourcing update is proposed in this paper. First, a mature commercial integrated navigation product is directly used to achieve a self-positioning accuracy of 20 cm on average. Second, an improved network based on BiSeNet is utilized for real-time semantic segmentation. It achieves the result of 83.9% IOU (Intersection over Union) on Nvidia Pegasus at 31 FPS. Third, a visual Simultaneous Localization and Mapping (SLAM) associated with pixel type information is performed to obtain the semantic point cloud data of features such as lane dividers, road markings, and other static objects. Finally, the semantic point cloud data is vectorized after denoising and clustering, and the results are matched with a pre-constructed HD map to confirm map elements that have not changed and generate new elements when appearing. The experiment conducted in Beijing shows that the method proposed is effective for crowdsourcing update of HD maps.

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