Semantic supported urban change detection using ALS point clouds

Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fang, Li [verfasserIn] Liu, Jinzhou [verfasserIn] Pan, Yue [verfasserIn] Ye, Zhen [verfasserIn] Tong, Xiaohua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Urban change detection Airborne laser scanning Deep learning

Übergeordnetes Werk:	Enthalten in: International journal of applied earth observation and geoinformation - Amsterdam [u.a.] : Elsevier Science, 1999, 118
Übergeordnetes Werk:	volume:118

DOI / URN:	10.1016/j.jag.2023.103271

Katalog-ID:	ELV009766847

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520			\|a Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas.
650		4	\|a Urban change detection
650		4	\|a Airborne laser scanning
650		4	\|a Deep learning
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700	1		\|a Pan, Yue \|e verfasserin \|0 (orcid)0000-0003-1320-147X \|4 aut
700	1		\|a Ye, Zhen \|e verfasserin \|4 aut
700	1		\|a Tong, Xiaohua \|e verfasserin \|4 aut
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author_variant	l f lf j l jl y p yp z y zy x t xt
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allfields	10.1016/j.jag.2023.103271 doi (DE-627)ELV009766847 (ELSEVIER)S1569-8432(23)00093-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Fang, Li verfasserin (orcid)0000-0002-0969-4083 aut Semantic supported urban change detection using ALS point clouds 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas. Urban change detection Airborne laser scanning Deep learning Liu, Jinzhou verfasserin aut Pan, Yue verfasserin (orcid)0000-0003-1320-147X aut Ye, Zhen verfasserin aut Tong, Xiaohua verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 118 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:118 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 118
spelling	10.1016/j.jag.2023.103271 doi (DE-627)ELV009766847 (ELSEVIER)S1569-8432(23)00093-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Fang, Li verfasserin (orcid)0000-0002-0969-4083 aut Semantic supported urban change detection using ALS point clouds 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas. Urban change detection Airborne laser scanning Deep learning Liu, Jinzhou verfasserin aut Pan, Yue verfasserin (orcid)0000-0003-1320-147X aut Ye, Zhen verfasserin aut Tong, Xiaohua verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 118 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:118 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 118
allfields_unstemmed	10.1016/j.jag.2023.103271 doi (DE-627)ELV009766847 (ELSEVIER)S1569-8432(23)00093-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Fang, Li verfasserin (orcid)0000-0002-0969-4083 aut Semantic supported urban change detection using ALS point clouds 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas. Urban change detection Airborne laser scanning Deep learning Liu, Jinzhou verfasserin aut Pan, Yue verfasserin (orcid)0000-0003-1320-147X aut Ye, Zhen verfasserin aut Tong, Xiaohua verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 118 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:118 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 118
allfieldsGer	10.1016/j.jag.2023.103271 doi (DE-627)ELV009766847 (ELSEVIER)S1569-8432(23)00093-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Fang, Li verfasserin (orcid)0000-0002-0969-4083 aut Semantic supported urban change detection using ALS point clouds 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas. Urban change detection Airborne laser scanning Deep learning Liu, Jinzhou verfasserin aut Pan, Yue verfasserin (orcid)0000-0003-1320-147X aut Ye, Zhen verfasserin aut Tong, Xiaohua verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 118 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:118 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 118
allfieldsSound	10.1016/j.jag.2023.103271 doi (DE-627)ELV009766847 (ELSEVIER)S1569-8432(23)00093-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Fang, Li verfasserin (orcid)0000-0002-0969-4083 aut Semantic supported urban change detection using ALS point clouds 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas. Urban change detection Airborne laser scanning Deep learning Liu, Jinzhou verfasserin aut Pan, Yue verfasserin (orcid)0000-0003-1320-147X aut Ye, Zhen verfasserin aut Tong, Xiaohua verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 118 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:118 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 118
language	English
source	Enthalten in International journal of applied earth observation and geoinformation 118 volume:118
sourceStr	Enthalten in International journal of applied earth observation and geoinformation 118 volume:118
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authorswithroles_txt_mv	Fang, Li @@aut@@ Liu, Jinzhou @@aut@@ Pan, Yue @@aut@@ Ye, Zhen @@aut@@ Tong, Xiaohua @@aut@@
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title	Semantic supported urban change detection using ALS point clouds
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title_full	Semantic supported urban change detection using ALS point clouds
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title_sort	semantic supported urban change detection using als point clouds
title_auth	Semantic supported urban change detection using ALS point clouds
abstract	Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas.
abstractGer	Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas.
abstract_unstemmed	Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. Evaluations using the object-based method show that changes were correctly identified in both of the tested areas.
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title_short	Semantic supported urban change detection using ALS point clouds
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author2	Liu, Jinzhou Pan, Yue Ye, Zhen Tong, Xiaohua
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up_date	2024-07-07T00:16:55.856Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV009766847</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240114093228.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230530s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jag.2023.103271</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV009766847</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1569-8432(23)00093-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">KARTEN</subfield><subfield code="q">DE-1a</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">38.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.41</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fang, Li</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-0969-4083</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Semantic supported urban change detection using ALS point clouds</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Airborne laser scanning (ALS) has experienced significant development and boosted widespread applications over the recent years. With increasing data quality and acquisition convenience, multi-temporal ALS point clouds have been used for change detection (CD) in earth observation. Data registration or geometric difference calculations can effectively obtain geometric changes using point clouds acquired from various epochs. However, the lack of semantic labeling means that traditional CD methods are unable to distinguish the changes in semantic information or remove the interference in some categories. Therefore, we propose a semantic supported change detection (SSCD) method. First, a point cloud semantic segmentation network is developed under the classic PointNet++ frame to provide semantic labeling for change detection, including an attention-based local feature embedding module that uses the spatially sensitive features of 3D points and an integrated multi-scale framework with local spatial attention convolution. Secondly, the point cloud is voxelized based on an Octree, where the label of each voxel is determined by the result of semantic segmentation. Finally, the change detection result is obtained by comparing the changes in voxel coordinates and semantic information of the two point clouds. We tested the proposed method using experiments conducted on the AHN datasets. In the two selected experimental areas, the accuracy of change detection rate reached 0.9099 and 0.9029, respectively. The F1 rate reached 0.4571 and 0.3455. 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Semantic supported urban change detection using ALS point clouds

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