A triangulation-based technique for building boundary identification from point cloud data
- Awrangjeb, Mohammad, Lu, Guojun
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015; Auckland, New Zealand; 23rd-24th November 2015 Vol. 2016-November, p. 1-6
- Full Text: false
- Reviewed:
- Description: Building boundary identification is an essential prerequisite in building outline generation from point cloud data. In this problem, boundary edges that constitute the building boundary are identified. The existing solutions to the identification of boundary edges from the input point set have one or more of the following problems: ineffective in finding appropriate edges in a concave shape, incapable of determining a 'hole' or 'concavity' inside the shape separately, dependant on additional information such as the scan direction that may be unavailable, and incompetent in determining the boundary of a point set from the boundaries of two or more subsets of the point set. This paper proposes a new solution to the identification of building boundary by using the maximum point-to-point distance in the input data. It properly detects the boundary edges for any type of shape and separately recognises holes, if any, inside the shape. The unique feature of the proposed solution is that it can identify the boundary of a point set from the boundaries of two or more subsets of the point set. It does not require any additional information other than the input point set. Experimental results show that the proposed solution can preserve details along the building boundary and offer high area-based completeness and quality, even in low density input data. © 2015 IEEE.
- Description: International Conference Image and Vision Computing New Zealand
Building change detection from LIDAR point cloud data based on connected component analysis
- Awrangjeb, Mohammad, Fraser, Clive, Lu, Guojun
- Authors: Awrangjeb, Mohammad , Fraser, Clive , Lu, Guojun
- Date: 2015
- Type: Text , Conference proceedings
- Relation: Isprs Geospatial Week 2015; La Grande Motte, France; 28th September-3rd October 2015; published in International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Vol. II-3, p. 393-400
- Full Text:
- Reviewed:
- Description: 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 under-segmentation 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.
- Authors: Awrangjeb, Mohammad , Fraser, Clive , Lu, Guojun
- Date: 2015
- Type: Text , Conference proceedings
- Relation: Isprs Geospatial Week 2015; La Grande Motte, France; 28th September-3rd October 2015; published in International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Vol. II-3, p. 393-400
- Full Text:
- Reviewed:
- Description: 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 under-segmentation 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.
- Authors: Awrangjeb, Mohammad
- Date: 2015
- Type: Text , Journal article
- Relation: Remote Sensing Vol. 7, no. 10 (2015), p. 14119-14150
- Full Text: false
- Reviewed:
- Description: Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support the creation of a building database from building footprints automatically extracted from LiDAR (light detection and ranging) point cloud data. An automatic building change detection technique by which buildings are automatically extracted from newly-available LiDAR point cloud data and compared to those within an existing building database is then presented. Buildings identified as totally new or demolished are directly added to the change detection output. However, for part-building demolition or extension, a connected component analysis algorithm is applied, and for each connected building component, the area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building-part. Using the developed GUI, a user can quickly examine each suggested change and indicate his/her decision to update the database, with a minimum number of mouse clicks. In experimental tests, the proposed change detection technique was found to produce almost no omission errors, and when compared to the number of reference building corners, it reduced the human interaction to 14% for initial building map generation and to 3% for map updating. Thus, the proposed approach can be exploited for enhanced automated building information updating within a topographic database. © 2015 by the authors.
Using point cloud data to identify, trace, and regularize the outlines of buildings
- Authors: Awrangjeb, Mohammad
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Remote Sensing Vol. 37, no. 3 (2016), p. 551-579
- Full Text:
- Reviewed:
- Description: Rectilinear building outline generation from the point set of a building usually works in three steps. Boundary edges that constitute the building outline are first identified. A sequence of points is then traced from the edges to define the building boundary. Finally, lines are generated from the sequence of points and adjusted to form a regular building outline. Existing solutions have shortcomings in one or more of the following cases: identifying details along a concave shape, separate identification of a 'hole' inside the shape, proper boundary tracing, and preservation of detailed information along a regularized building outline. This article proposes new solutions to all three steps. By using the maximum point-to-point distance in the input data, the solution to the identification step properly detects the boundary edges for any type of shape and separately recognizes holes, if any, inside the shape. The proposed tracing algorithm divides boundary edges into segments, accurately obtains the sequence of points for each segment and then merges them, if necessary, to produce a single boundary for each shape. The regularization step proposes an improved corner and line extraction algorithm and adjusts the extracted lines with respect to the automatically determined principal directions of buildings. In order to evaluate the performance, an evaluation system that makes corner correspondences between an extracted building outline and its reference outline is also proposed. Experimental results show that the proposed solutions can preserve detail along the building boundary and offer high pixel-based completeness and geometric accuracy, even in low-density input data. © 2016 The Author(s). Published by Taylor & Francis.
- Description: Rectilinear building outline generation from the point set of a building usually works in three steps. Boundary edges that constitute the building outline are first identified. A sequence of points is then traced from the edges to define the building boundary. Finally, lines are generated from the sequence of points and adjusted to form a regular building outline. Existing solutions have shortcomings in one or more of the following cases: identifying details along a concave shape, separate identification of a ‘hole’ inside the shape, proper boundary tracing, and preservation of detailed information along a regularized building outline. This article proposes new solutions to all three steps. By using the maximum point-to-point distance in the input data, the solution to the identification step properly detects the boundary edges for any type of shape and separately recognizes holes, if any, inside the shape. The proposed tracing algorithm divides boundary edges into segments, accurately obtains the sequence of points for each segment and then merges them, if necessary, to produce a single boundary for each shape. The regularization step proposes an improved corner and line extraction algorithm and adjusts the extracted lines with respect to the automatically determined principal directions of buildings. In order to evaluate the performance, an evaluation system that makes corner correspondences between an extracted building outline and its reference outline is also proposed. Experimental results show that the proposed solutions can preserve detail along the building boundary and offer high pixel-based completeness and geometric accuracy, even in low-density input data. © 2016 The Author(s). Published by Taylor & Francis.
- Authors: Awrangjeb, Mohammad
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Remote Sensing Vol. 37, no. 3 (2016), p. 551-579
- Full Text:
- Reviewed:
- Description: Rectilinear building outline generation from the point set of a building usually works in three steps. Boundary edges that constitute the building outline are first identified. A sequence of points is then traced from the edges to define the building boundary. Finally, lines are generated from the sequence of points and adjusted to form a regular building outline. Existing solutions have shortcomings in one or more of the following cases: identifying details along a concave shape, separate identification of a 'hole' inside the shape, proper boundary tracing, and preservation of detailed information along a regularized building outline. This article proposes new solutions to all three steps. By using the maximum point-to-point distance in the input data, the solution to the identification step properly detects the boundary edges for any type of shape and separately recognizes holes, if any, inside the shape. The proposed tracing algorithm divides boundary edges into segments, accurately obtains the sequence of points for each segment and then merges them, if necessary, to produce a single boundary for each shape. The regularization step proposes an improved corner and line extraction algorithm and adjusts the extracted lines with respect to the automatically determined principal directions of buildings. In order to evaluate the performance, an evaluation system that makes corner correspondences between an extracted building outline and its reference outline is also proposed. Experimental results show that the proposed solutions can preserve detail along the building boundary and offer high pixel-based completeness and geometric accuracy, even in low-density input data. © 2016 The Author(s). Published by Taylor & Francis.
- Description: Rectilinear building outline generation from the point set of a building usually works in three steps. Boundary edges that constitute the building outline are first identified. A sequence of points is then traced from the edges to define the building boundary. Finally, lines are generated from the sequence of points and adjusted to form a regular building outline. Existing solutions have shortcomings in one or more of the following cases: identifying details along a concave shape, separate identification of a ‘hole’ inside the shape, proper boundary tracing, and preservation of detailed information along a regularized building outline. This article proposes new solutions to all three steps. By using the maximum point-to-point distance in the input data, the solution to the identification step properly detects the boundary edges for any type of shape and separately recognizes holes, if any, inside the shape. The proposed tracing algorithm divides boundary edges into segments, accurately obtains the sequence of points for each segment and then merges them, if necessary, to produce a single boundary for each shape. The regularization step proposes an improved corner and line extraction algorithm and adjusts the extracted lines with respect to the automatically determined principal directions of buildings. In order to evaluate the performance, an evaluation system that makes corner correspondences between an extracted building outline and its reference outline is also proposed. Experimental results show that the proposed solutions can preserve detail along the building boundary and offer high pixel-based completeness and geometric accuracy, even in low-density input data. © 2016 The Author(s). Published by Taylor & Francis.
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