scholarly journals An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection

2018 ◽  
Vol 10 (10) ◽  
pp. 1512 ◽  
Author(s):  
Mohammad Awrangjeb ◽  
Syed Gilani ◽  
Fasahat Siddiqui
Keyword(s):  
Change Detection ◽  
Development Stage ◽  
Human Interaction ◽  
Reconstruction Technique ◽  
Test Model ◽  
Building Roof ◽  
Cloud Data ◽  
Detection Techniques ◽  
Model Sets ◽  
Building Models

Three-dimensional (3-D) reconstruction of building roofs can be an essential prerequisite for 3-D building change detection, which is important for detection of informal buildings or extensions and for update of 3-D map database. However, automatic 3-D roof reconstruction from the remote sensing data is still in its development stage for a number of reasons. For instance, there are difficulties in determining the neighbourhood relationships among the planes on a complex building roof, locating the step edges from point cloud data often requires additional information or may impose constraints, and missing roof planes attract human interaction and often produces high reconstruction errors. This research introduces a new 3-D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. It identifies any missing planes through an analysis using the 3-D plane intersection lines between adjacent planes. Then, it generates new planes to fill gaps of missing planes. Finally, it obtains complete building models through insertion of approximate wall planes and building floor. The reported research in this paper then uses the generated building models to detect 3-D changes in buildings. Plane connections between neighbouring planes are first defined to establish relationships between neighbouring planes. Then, each building in the reference and test model sets is represented using a graph data structure. Finally, the height intensity images, and if required the graph representations, of the reference and test models are directly compared to find and categorise 3-D changes into five groups: new, unchanged, demolished, modified and partially-modified planes. Experimental results on two Australian datasets show high object- and pixel-based accuracy in terms of completeness, correctness, and quality for both 3-D roof reconstruction and change detection techniques. The proposed change detection technique is robust to various changes including addition of a new veranda to or removal of an existing veranda from a building and increase of the height of a building.

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 INTEGRATION OF BUILDING KNOWLEDGE INTO BINARY SPACE PARTITIONING FOR THE RECONSTRUCTION OF REGULARIZED BUILDING MODELS

2015 ◽  
Vol II-3/W5 ◽  
pp. 541-548 ◽  
Author(s):  
A. Wichmann ◽  
J. Jung ◽  
G. Sohn ◽  
M. Kada ◽  
M. Ehlers
Keyword(s):  
Real World ◽  
Point Cloud Data ◽  
Space Partitioning ◽  
Building Roof ◽  
Cloud Data ◽  
Building Models ◽  
Topological Correctness ◽  
Binary Space Partitioning ◽  
Building Geometry ◽  
Data Driven Approach

Recent approaches for the automatic reconstruction of 3D building models from airborne point cloud data integrate prior knowledge of roof shapes with the intention to improve the regularization of the resulting models without lessening the flexibility to generate all real-world occurring roof shapes. In this paper, we present a method to integrate building knowledge into the data-driven approach that uses binary space partitioning (BSP) for modeling the 3D building geometry. A retrospective regularization of polygons that emerge from the BSP tree is not without difficulty because it has to deal with the 2D BSP subdivision itself and the plane definitions of the resulting partition regions to ensure topological correctness. This is aggravated by the use of hyperplanes during the binary subdivision that often splits planar roof regions into several parts that are stored in different subtrees of the BSP tree. We therefore introduce the use of hyperpolylines in the generation of the BSP tree to avoid unnecessary spatial subdivisions, so that the spatial integrity of planar roof regions is better maintained. The hyperpolylines are shown to result from basic building roof knowledge that is extracted based on roof topology graphs. An adjustment of the underlying point segments ensures that the positions of the extracted hyperpolylines result in regularized 2D partitions as well as topologically correct 3D building models. The validity and limitations of the approach are demonstrated on real-world examples.

scholarly journals Integration of Image Data for Refining Building Boundaries Derived from Point Clouds

2014 ◽  
Vol XL-3 ◽  
pp. 253-258 ◽  
Author(s):  
S. N. Perera ◽  
N. Hetti Arachchige ◽  
D. Schneider
Keyword(s):  
Laser Scanning ◽  
High Reliability ◽  
Image Data ◽  
Point Clouds ◽  
Aerial Photographs ◽  
Building Roof ◽  
Cloud Data ◽  
Building Models ◽  
3D Cadastre ◽  
Accurate Position

Geometrically and topologically correct 3D building models are required to satisfy with new demands such as 3D cadastre, map updating, and decision making. More attention on building reconstruction has been paid using Airborne Laser Scanning (ALS) point cloud data. The planimetric accuracy of roof outlines, including step-edges is questionable in building models derived from only point clouds. This paper presents a new approach for the detection of accurate building boundaries by merging point clouds acquired by ALS and aerial photographs. It comprises two major parts: reconstruction of initial roof models from point clouds only, and refinement of their boundaries. A shortest closed circle (graph) analysis method is employed to generate building models in the first step. Having the advantages of high reliability, this method provides reconstruction without prior knowledge of primitive building types even when complex height jumps and various types of building roof are available. The accurate position of boundaries of the initial models is determined by the integration of the edges extracted from aerial photographs. In this process, scene constraints defined based on the initial roof models are introduced as the initial roof models are representing explicit unambiguous geometries about the scene. Experiments were conducted using the ISPRS benchmark test data. Based on test results, we show that the proposed approach can reconstruct 3D building models with higher geometrical (planimetry and vertical) and topological accuracy.

scholarly journals Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Odile Close ◽  
Sophie Petit ◽  
Benjamin Beaumont ◽  
Eric Hallot
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Change Detection ◽  
Signal To Noise Ratio ◽  
Land Conversion ◽  
Sustainable Resources ◽  
Detection Techniques ◽  
Detection Analysis ◽  
Conversion Rates ◽  
Sentinel 2

Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the atmosphere. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study investigates the use of Sentinel-2 data for analysing lands conversion associated to Land Use, Land Use Change and Forestry sector in the Wallonia region (southern Belgium). This region is characterized by one of the lowest conversion rates across European countries, which constitutes a particular challenge in identifying land changes. The proposed research tests the most commonly used change detection techniques on a bi-temporal and multi-temporal set of mosaics of Sentinel-2 data from the years 2016 and 2018. Our results reveal that land conversion is a very rare phenomenon in Wallonia. All the change detection techniques tested have been found to substantially overestimate the changes. In spite of this moderate results our study has demonstrated the potential of Sentinel-2 regarding land conversion. However, in this specific context of very low magnitude of land conversion in Wallonia, change detection techniques appear to be not sufficient to exceed the signal to noise ratio.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 689-695
Author(s):  
Z. Li ◽  
W. Zhang ◽  
J. Shan
Keyword(s):  
Large Scale ◽  
Traditional Approach ◽  
Holistic Approach ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Optimization Strategy ◽  
Building Roof ◽  
Building Model ◽  
Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

scholarly journals A NEW MASK FOR AUTOMATIC BUILDING DETECTION FROM HIGH DENSITY POINT CLOUD DATA AND MULTISPECTRAL IMAGERY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 89-96 ◽  
Author(s):  
M. Awrangjeb ◽  
F. U. Siddiqui
Keyword(s):  
Surface Model ◽  
Building Detection ◽  
Multispectral Imagery ◽  
Residential Areas ◽  
Existing Building ◽  
Cloud Data ◽  
Flat Surfaces ◽  
Detection Techniques ◽  
Gradient Function ◽  
Dominant Direction

In complex urban and residential areas, there are buildings which are not only connected with and/or close to one another but also partially occluded by their surrounding vegetation. Moreover, there may be buildings whose roofs are made of transparent materials. In transparent buildings, there are point returns from both the ground (or materials inside the buildings) and the rooftop. These issues confuse the previously proposed building masks which are generated from either ground points or non-ground points. The normalised digital surface model (nDSM) is generated from the non-ground points and usually it is hard to find individual buildings and trees using the nDSM. In contrast, the primary building mask is produced using the ground points, thereby it misses the transparent rooftops. This paper proposes a new building mask based on the non-ground points. The dominant directions of non-ground lines extracted from the multispectral imagery are estimated. A dummy grid with the target mask resolution is rotated at each dominant direction to obtain the corresponding height values from the non-ground points. Three sub-masks are then generated from the height grid by estimating the gradient function. Two of these sub-masks capture planar surfaces whose height remain constant in along and across the dominant direction, respectively. The third sub-mask contains only the flat surfaces where the height (ideally) remains constant in all directions. All the sub-masks generated in all estimated dominant directions are combined to produce the candidate building mask. Although the application of the gradient function helps in removal of most of the vegetation, the final building mask is obtained through removal of planar vegetation, if any, and tiny isolated false candidates. Experimental results on three Australian data sets show that the proposed method can successfully remove vegetation, thereby separate buildings from occluding vegetation and detect buildings with transparent roof materials. While compared to existing building detection techniques, the proposed technique offers higher objectbased completeness, correctness and quality, specially in complex scenes with aforementioned issues. It is not only capable of detecting transparent buildings, but also small garden sheds which are sometimes as small as 5&amp;thinsp;m<sup>2</sup> in area.

Sign in / Sign up

Export Citation Format

Share Document