Automatic extraction of building roofs using LIDAR data and multispectral imagery
- Awrangjeb, Mohammad, Zhang, Chunsun, Fraser, Clive
- Authors: Awrangjeb, Mohammad , Zhang, Chunsun , Fraser, Clive
- Date: 2013
- Type: Text , Journal article
- Relation: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 83, no. (2013), p. 1-18
- Full Text: false
- Reviewed:
- Description: Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modelling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a ‘ground mask’. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines are extracted from the grey-scale version of the orthoimage and then classified into several classes such as ‘ground’, ‘tree’, ‘roof edge’ and ‘roof ridge’ using the ground mask and colour and texture information from the orthoimagery. During segmentation of the non-ground LIDAR points, the lines from the latter two classes are used as baselines to locate the nearby LIDAR points of the neighbouring planes. For each plane a robust seed region is thereby defined using the nearby non-ground LIDAR points of a baseline and this region is iteratively grown to extract the complete roof plane. Finally, a newly proposed rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method can successfully remove vegetation and so offers high extraction rates.
Segmentation of airborne point cloud data for automatic building roof extraction
- Gilani, Syed, Awrangjeb, Mohammad, Lu, Guojun
- Authors: Gilani, Syed , Awrangjeb, Mohammad , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: GIScience & Remote Sensing Vol. 55, no. 1 (2018), p. 63-89
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- Description: Roof plane segmentation is a complex task since point cloud data carry no connection information and do not provide any semantic characteristics of the underlying scanned surfaces. Point cloud density, complex roof profiles, and occlusion add another layer of complexity which often encounter in practice. In this article, we present a new technique that provides a better interpolation of roof regions where multiple surfaces intersect creating non-manifold points. As a result, these geometric features are preserved to achieve automated identification and segmentation of the roof planes from unstructured laser data. The proposed technique has been tested using the International Society for Photogrammetry and Remote Sensing benchmark and three Australian datasets, which differ in terrain, point density, building sizes, and vegetation. The qualitative and quantitative results show the robustness of the methodology and indicate that the proposed technique can eliminate vegetation and extract buildings as well as their non-occluding parts from the complex scenes at a high success rate for building detection (between 83.9% and 100% per-object completeness) and roof plane extraction (between 73.9% and 96% per-object completeness). The proposed method works more robustly than some existing methods in the presence of occlusion and low point sampling as indicated by the correctness of above 95% for all the datasets.
- Authors: Gilani, Syed , Awrangjeb, Mohammad , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: GIScience & Remote Sensing Vol. 55, no. 1 (2018), p. 63-89
- Full Text:
- Reviewed:
- Description: Roof plane segmentation is a complex task since point cloud data carry no connection information and do not provide any semantic characteristics of the underlying scanned surfaces. Point cloud density, complex roof profiles, and occlusion add another layer of complexity which often encounter in practice. In this article, we present a new technique that provides a better interpolation of roof regions where multiple surfaces intersect creating non-manifold points. As a result, these geometric features are preserved to achieve automated identification and segmentation of the roof planes from unstructured laser data. The proposed technique has been tested using the International Society for Photogrammetry and Remote Sensing benchmark and three Australian datasets, which differ in terrain, point density, building sizes, and vegetation. The qualitative and quantitative results show the robustness of the methodology and indicate that the proposed technique can eliminate vegetation and extract buildings as well as their non-occluding parts from the complex scenes at a high success rate for building detection (between 83.9% and 100% per-object completeness) and roof plane extraction (between 73.9% and 96% per-object completeness). The proposed method works more robustly than some existing methods in the presence of occlusion and low point sampling as indicated by the correctness of above 95% for all the datasets.
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
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- 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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