24.2.2.1.1 Building Change Detection

Chapter Contents (Back)
Remote Sensing. Registration. Change Detection. Building Change. Aerial Image Analysis. Site Models:
See also Site Model Change Detection, Map Update.
See also Change Detection -- Image Level.
See also Building Extraction, Analysis and Detection Systems, Multi-View.

Murakami, H.[Hiroshi], Nakagawa, K.[Katsuto], Hasegawa, H.[Hiroyuki], Shibata, T.[Taku], Iwanami, E.[Eiji],
Change detection of buildings using an airborne laser scanner,
PandRS(54), No. 2-3, July 1999, pp. 148-152.
Elsevier DOI Acquire a digital surface model of urban areas. Simple comparison between DSMs acquired at different times detected building changes. BibRef 9907

Steinle, E., Vögtle, T.,
Automated Extraction and Reconstruction of Buildings in Laser Scanning Data for Disaster Management,
Ascona01(309-318). Use LIDAR to quickly model buildings and detect changes. Approximage buildings by planar faces. 0201
BibRef

Jung, F.[Franck],
Detecting building changes from multitemporal aerial stereopairs,
PandRS(58), No. 3-4, January 2004, pp. 187-201.
Elsevier DOI 0411
BibRef

Carlotto, M.J.,
Detection and Analysis of Change in Remotely-Sensed Imagery with Application to Wide Area Surveillance,
IP(6), No. 1, January 1997, pp. 189-202.
IEEE DOI 9703
BibRef

Carlotto, M.J.,
A cluster-based approach for detecting man-made objects and changes in imagery,
GeoRS(43), No. 2, February 2005, pp. 374-387.
IEEE Abstract. 0501
BibRef

Lee, B.G., Tom, V.T., and Carlotto, M.J.,
A Signal-Symbol Approach to Change Detection,
AAAI-86(1138- ). The Analytic Sciences Corp. BibRef 8600

Matikainen, L., Hyyppä, J., Ahokas, E., Markelin, L., Kaartinen, H.,
Automatic Detection of Buildings and Changes in Buildings for Updating of Maps,
RS(2), No. 5, May 2010, pp. 1217-1248.
DOI Link 1203
BibRef

Champion, N.[Nicolas], Boldo, D.[Didier], Pierrot-Deseilligny, M.[Marc], Stamon, G.[Georges],
2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives,
PRL(31), No. 10, 15 July 2010, pp. 1138-1147.
Elsevier DOI 1008
BibRef
Earlier:
Automatic estimation of fine terrain models from multiple high-resolution satellite images,
ICIP09(577-580).
IEEE DOI 0911
Change detection; Building vector database; Digital Surface Models; Digital Terrain Models; High resolution satellite imagery; Quality assessment BibRef

Champion, N.[Nicolas], Stamon, G.[Georges], Pierrot-Deseilligny, M.[Marc],
Automatic GIS Updating from High Resolution Satellite Images,
MVA09(374-).
PDF File. 0905

See also Automatic Building Extraction from DEMs Using an Object Approach and Application to the 3D-City Modeling. BibRef

Debaque, B., Stamon, G., Pierrot-Deseilligny, M.,
An area-based alignment method for 3d urban models,
ICPR02(I: 61-64).
IEEE DOI 0211
Find a transformation and validate. BibRef

Chen, L.C.[Liang-Chien], Lin, L.J.[Li-Jer],
Detection of building changes from aerial images and light detecting and ranging (LIDAR) data,
AppRS(4), November 2010, pp. 041870.
DOI Link 1105
BibRef

Chen, L.C.[Liang-Chien], Lin, L.J.[Li-Jer], and Chang, W.C.[Wen-Chi],
Imaging data detects changes in urban areas over time,
SPIE(Newsroom), May 19, 2011
DOI Link 1105
A scheme for identifying altered features of cityscapes that compares existing building models with new lidar data points and aerial images improves the accuracy of 3D spatial information. BibRef

Nebiker, S.[Stephan], Lack, N.[Natalie], Deuber, M.[Marianne],
Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis,
RS(6), No. 9, 2014, pp. 8310-8336.
DOI Link 1410
BibRef

Qin, R.J.[Rong-Jun],
Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery,
PandRS(96), No. 1, 2014, pp. 179-192.
Elsevier DOI 1410
Stereo imagery BibRef

Pang, S.Y.[Shi-Yan], Hu, X.Y.[Xiang-Yun], Wang, Z.Z.[Zi-Zheng], Lu, Y.H.[Yi-Hui],
Object-Based Analysis of Airborne LiDAR Data for Building Change Detection,
RS(6), No. 11, 2014, pp. 10733-10749.
DOI Link 1412
BibRef

Du, S.H.[Shi-Hong], Zhang, F.L.[Fang-Li], Zhang, X.Y.[Xiu-Yuan],
Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach,
PandRS(105), No. 1, 2015, pp. 107-119.
Elsevier DOI 1506
Very high resolution (VHR) images BibRef

Fruehmann, R.[Richard], Waugh, R.[Rachael], Dulieu-Barton, J.[Janice],
A fresh look at assessing structural performance using imaging techniques,
SPIE(Newsroom), June 15, 2015.
DOI Link 1507
A lock-in algorithm is used to combine digital image correlation with thermoelastic stress analyses to offer greater data richness, paving the way to strain-based nondestructive evaluation. BibRef

Hullo, J.F.[Jean-François], Thibault, G.[Guillaume], Boucheny, C.[Christian], Dory, F.[Fabien], Mas, A.[Arnaud],
Multi-Sensor As-Built Models of Complex Industrial Architectures,
RS(7), No. 12, 2015, pp. 15827.
DOI Link 1601
BibRef

Wang, C.M.[Chun-Mei], Yang, Q.[Qinke], Jupp, D.L.B.[David Laurence Barry], Pang, G.[Guowei],
Modeling Change of Topographic Spatial Structures with DEM Resolution Using Semi-Variogram Analysis and Filter Bank,
IJGI(5), No. 7, 2016, pp. 107.
DOI Link 1608
BibRef

Qin, R.J.[Rong-Jun], Tian, J.J.[Jiao-Jiao], Reinartz, P.[Peter],
3D change detection-Approaches and applications,
PandRS(122), No. 1, 2016, pp. 41-56.
Elsevier DOI 1612
3D change detection BibRef

Du, S.J.[Shou-Ji], Zhang, Y.S.[Yun-Sheng], Qin, R.J.[Rong-Jun], Yang, Z.H.[Zhi-Hua], Zou, Z.R.[Zheng-Rong], Tang, Y.Q.[Yu-Qi], Fan, C.[Chong],
Building Change Detection Using Old Aerial Images and New LiDAR Data,
RS(8), No. 12, 2016, pp. 1030.
DOI Link 1612
BibRef

Xiao, P.F.[Peng-Feng], Yuan, M.[Min], Zhang, X.L.[Xue-Liang], Feng, X.Z.[Xue-Zhi], Guo, Y.W.[Yan-Wen],
Cosegmentation for Object-Based Building Change Detection From High-Resolution Remotely Sensed Images,
GeoRS(55), No. 3, March 2017, pp. 1587-1603.
IEEE DOI 1703
Buildings BibRef

Li, W.Z.[Wen-Zhuo], Sun, K.[Kaimin], Li, D.R.[De-Ren], Bai, T.[Ting], Sui, H.G.[Hai-Gang],
A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Wang, L.[Lin], Guo, Q.S.[Qing-Sheng], Liu, Y.[Yuangang], Sun, Y.[Yageng], Wei, Z.W.[Zhi-Wei],
Contextual Building Selection Based on a Genetic Algorithm in Map Generalization,
IJGI(6), No. 9, 2017, pp. xx-yy.
DOI Link 1710
BibRef

Lee, J.[Jaeeun], Jang, H.[Hanme], Yang, J.H.[Jong-Hyeon], Yu, K.[Kiyun],
Machine Learning Classification of Buildings for Map Generalization,
IJGI(6), No. 10, 2017, pp. xx-yy.
DOI Link 1710
BibRef

Moya, L.[Luis], Perez, L.R.M.[Luis R. Marval], Mas, E.[Erick], Adriano, B.[Bruno], Koshimura, S.[Shunichi], Yamazaki, F.[Fumio],
Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Natsuaki, R.[Ryo], Nagai, H.[Hiroto], Tomii, N.[Naoya], Tadono, T.[Takeo],
Sensitivity and Limitation in Damage Detection for Individual Buildings Using InSAR Coherence: A Case Study in 2016 Kumamoto Earthquakes,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Vetrivel, A.[Anand], Gerke, M.[Markus], Kerle, N.[Norman], Nex, F.[Francesco], Vosselman, G.[George],
Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning,
PandRS(140), 2018, pp. 45-59.
Elsevier DOI 1805
Oblique images, UAV, 3D point cloud features, CNN features, Multiple-kernel-learning, Transfer learning, Structural damage detections BibRef

Zhou, X.D.[Xiao-Dong], Chen, Z.[Zhe], Zhang, X.[Xiang], Ai, T.[Tinghua],
Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Zhai, W.[Wei], Huang, C.L.[Chun-Lin], Pei, W.[Wansheng],
Two New Polarimetric Feature Parameters for the Recognition of the Different Kinds of Buildings in Earthquake-Stricken Areas Based on Entropy and Eigenvalues of PolSAR Decomposition,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Feurer, D., Vinatier, F.,
Joining multi-epoch archival aerial images in a single SfM block allows 3-D change detection with almost exclusively image information,
PandRS(146), 2018, pp. 495-506.
Elsevier DOI 1812
Automation, Multitemporal DEMs, SfM photogrammetry, Analog imagery, 3-D change detection, Cost-effective/frugal BibRef

Ji, M.[Min], Liu, L.[Lanfa], Buchroithner, M.[Manfred],
Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Ji, M.[Min], Liu, L.[Lanfa], Du, R.[Runlin], Buchroithner, M.F.[Manfred F.],
A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Zhang, Y.F.[Yun-Fei], Huang, J.C.[Jin-Cai], Deng, M.[Min], Chen, C.[Chi], Zhou, F.B.[Fang-Bin], Xie, S.C.[Shu-Chun], Fang, X.L.[Xiao-Liang],
Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations,
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901
BibRef

Pang, S.Y.[Shi-Yan], Hu, X.Y.[Xiang-Yun], Zhang, M.[Mi], Cai, Z.L.[Zhong-Liang], Liu, F.Z.[Feng-Zhu],
Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Wen, D.W.[Da-Wei], Huang, X.[Xin], Zhang, A.[Anlu], Ke, X.[Xinli],
Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Li, L.[Lu], Wang, C.[Chao], Zhang, H.[Hong], Zhang, B.[Bo], Wu, F.[Fan],
Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Ji, S.P.[Shun-Ping], Shen, Y.Y.[Yan-Yun], Lu, M.[Meng], Zhang, Y.J.[Yong-Jun],
Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Kushiyama, Y.[Yuzuru], Matsuoka, M.[Masashi],
Time Series GIS Map Dataset of Demolished Buildings in Mashiki Town after the 2016 Kumamoto, Japan Earthquake,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Ghaffarian, S.[Saman], Kerle, N.[Norman], Pasolli, E.[Edoardo], Arsanjani, J.J.[Jamal Jokar],
Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Zhang, Z.C.[Zhen-Chao], Vosselman, G.[George], Gerke, M.[Markus], Persello, C.[Claudio], Tuia, D.[Devis], Yang, M.Y.[Michael Ying],
Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Ma, H.J.[Hao-Jie], Liu, Y.L.[Ya-Lan], Ren, Y.H.[Yu-Huan], Yu, J.X.[Jing-Xian],
Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Jiang, H.[Huiwei], Hu, X.Y.[Xiang-Yun], Li, K.[Kun], Zhang, J.M.[Jin-Ming], Gong, J.Q.[Jin-Qi], Zhang, M.[Mi],
PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Zhou, K., Lindenbergh, R., Gorte, B., Zlatanova, S.,
LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data,
PandRS(162), 2020, pp. 200-213.
Elsevier DOI 2004
Change detection, 3D city model, Building, LiDAR data, VHR images, Dense matching BibRef

Javadi, S.[Saleh], Dahl, M.[Mattias], Pettersson, M.I.[Mats I.],
Change Detection in Aerial Images Using Three-Dimensional Feature Maps,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Dai, C.G.[Chen-Guang], Zhang, Z.C.[Zhen-Chao], Lin, D.[Dong],
An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Suchocki, C.[Czeslaw], Damiecka-Suchocka, M.[Marzena], Katzer, J.[Jacek], Janicka, J.[Joanna], Rapinski, J.[Jacek], Stalowska, P.[Paulina],
Remote Detection of Moisture and Bio-Deterioration of Building Walls by Time-Of-Flight and Phase-Shift Terrestrial Laser Scanners,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Suchocki, C.[Czeslaw], Blaszczak-Bak, W.[Wioleta], Damiecka-Suchocka, M.[Marzena], Jagoda, M.[Marcin], Masiero, A.[Andrea],
On the Use of the OptD Method for Building Diagnostics,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Miura, H.[Hiroyuki], Aridome, T.[Tomohiro], Matsuoka, M.[Masashi],
Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Tan, Y.[Yi], Li, S.[Silin], Wang, Q.[Qian],
Automated Geometric Quality Inspection of Prefabricated Housing Units Using BIM and LiDAR,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Cao, S.S.[Shi-Song], Du, M.Y.[Ming-Yi], Zhao, W.J.[Wen-Ji], Hu, Y.G.[Yun-Gang], Mo, Y.[You], Chen, S.S.[Shan-Shan], Cai, Y.[Yile], Peng, Z.Q.[Zi-Qiang], Zhang, C.Y.[Chao-Yi],
Multi-level monitoring of three-dimensional building changes for megacities: Trajectory, morphology, and landscape,
PandRS(167), 2020, pp. 54-70.
Elsevier DOI 2008
Airborne laser scanner, Megacity, Object-Grid-City block building change detection, 3D morphological parameters BibRef

Janicka, J.[Joanna], Rapinski, J.[Jacek], Blaszczak-Bak, W.[Wioleta], Suchocki, C.[Czeslaw],
Application of the Msplit Estimation Method in the Detection and Dimensioning of the Displacement of Adjacent Planes,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
TLS for building and other structure monitoring, evaluation of changes. BibRef

Mohamadi, B.[Bahaa], Balz, T.[Timo], Younes, A.[Ali],
Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas: Case Study of Alexandria, Egypt,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Li, Q.Y.[Qing-Yu], Shi, Y.L.[Yi-Lei], Auer, S.[Stefan], Roschlaub, R.[Robert], Möst, K.[Karin], Schmitt, M.[Michael], Glock, C.[Clemens], Zhu, X.X.[Xiao-Xiang],
Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Tian, Y.[Yi], Hao, M.[Ming], Zhang, H.[Hua],
Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Lyu, X.Z.[Xu-Zhe], Hao, M.[Ming], Shi, W.Z.[Wen-Zhong],
Building Change Detection Using a Shape Context Similarity Model for LiDAR Data,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Zhang, H.M.[Hai-Ming], Wang, M.C.[Ming-Chang], Wang, F.Y.[Feng-Yan], Yang, G.D.[Guo-Dong], Zhang, Y.[Ying], Jia, J.Q.[Jun-Qian], Wang, S.Q.[Si-Qi],
A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Zhang, K.Y.[Kai-Yu], Fu, X.[Xikai], Lv, X.L.[Xiao-Lei], Yuan, J.[Jili],
Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Liu, D.[Dan], Li, D.J.[Da-Jun], Wang, M.Z.[Mei-Zhen], Wang, Z.M.[Zhi-Ming],
3D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density,
IJGI(10), No. 3, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Wang, H.B.[Hai-Bo], Qi, J.C.[Jian-Chao], Lei, Y.F.[Yu-Fei], Wu, J.[Jun], Li, B.[Bo], Jia, Y.L.[Yi-Lin],
A Refined Method of High-Resolution Remote Sensing Change Detection Based on Machine Learning for Newly Constructed Building Areas,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Takagi, M.[Motohiro], Hayase, K.[Kazuya], Kitahara, M.[Masaki], Shimamura, J.[Jun],
Building Change Detection by Using Past Map Information and Optical Aerial Images,
IEICE(E104-D), No. 6, June 2021, pp. 897-900.
WWW Link. 2106
BibRef

Peng, D.F.[Dai-Feng], Bruzzone, L.[Lorenzo], Zhang, Y.J.[Yong-Jun], Guan, H.Y.[Hai-Yan], Ding, H.Y.[Hai-Yong], Huang, X.[Xu],
SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images,
GeoRS(59), No. 7, July 2021, pp. 5891-5906.
IEEE DOI 2106
Image segmentation, Remote sensing, Data models, Machine learning, Buildings, Feature extraction, Task analysis, semisupervised convolutional network BibRef

de Gélis, I.[Iris], Lefèvre, S.[Sébastien], Corpetti, T.[Thomas],
Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Jovanovic, D.[Dušan], Gavrilovic, M.[Milan], Sladic, D.[Dubravka], Radulovic, A.[Aleksandra], Govedarica, M.[Miro],
Building Change Detection Method to Support Register of Identified Changes on Buildings,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Jung, S.[Sejung], Lee, W.H.[Won Hee], Han, Y.[Youkyung],
Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor's Elevation and Azimuth Angles,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Diakogiannis, F.I.[Foivos I.], Waldner, F.[François], Caccetta, P.[Peter],
Looking for Change? Roll the Dice and Demand Attention,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
building change BibRef

Xue, J.K.[Jun-Kang], Xu, H.[Hao], Yang, H.[Hui], Wang, B.[Biao], Wu, P.[Penghai], Choi, J.[Jaewan], Cai, L.X.[Li-Xiao], Wu, Y.[Yanlan],
Multi-Feature Enhanced Building Change Detection Based on Semantic Information Guidance,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Shen, L.[Li], Lu, Y.[Yao], Chen, H.[Hao], Wei, H.[Hao], Xie, D.H.[Dong-Hai], Yue, J.[Jiabao], Chen, R.[Rui], Lv, S.[Shouye], Jiang, B.[Bitao],
S2Looking: A Satellite Side-Looking Dataset for Building Change Detection,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Wang, H.[Hao], Lv, X.L.[Xiao-Lei], Zhang, K.Y.[Kai-Yu], Guo, B.[Bin],
Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Ye, Y.X.[Yuan-Xin], Zhou, L.[Liang], Zhu, B.[Bai], Yang, C.[Chao], Sun, M.M.[Miao-Miao], Fan, J.W.[Jian-Wei], Fu, Z.T.[Zhi-Tao],
Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Schorcht, M.[Martin], Hecht, R.[Robert], Meinel, G.[Gotthard],
Comparative Study on Matching Methods for the Distinction of Building Modifications and Replacements Based on Multi-Temporal Building Footprint Data,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Pan, J.P.[Jian-Ping], Li, X.[Xin], Cai, Z.Y.[Zhuo-Yan], Sun, B.[Bowen], Cui, W.[Wei],
A Self-Attentive Hybrid Coding Network for 3D Change Detection in High-Resolution Optical Stereo Images,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Zheng, H.H.[Han-Hong], Gong, M.[Maoguo], Liu, T.F.[Tong-Fei], Jiang, F.L.[Fen-Long], Zhan, T.[Tao], Lu, D.[Di], Zhang, M.Y.[Ming-Yang],
HFA-Net: High frequency attention siamese network for building change detection in VHR remote sensing images,
PR(129), 2022, pp. 108717.
Elsevier DOI 2206
Building change detection, High frequency enhancement, Spatial-wise attention, Convolutional neural network BibRef

Shen, Q.[Qian], Huang, J.[Jiru], Wang, M.[Min], Tao, S.[Shikang], Yang, R.[Rui], Zhang, X.[Xin],
Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery,
PandRS(189), 2022, pp. 78-94.
Elsevier DOI 2206
Multitask learning, Height displacement, High-spatial-resolution remote sensing, Siamese network BibRef

Aliabad, F.A.[Fahime Arabi], Malamiri, H.R.G.[Hamid Reza Ghafarian], Shojaei, S.[Saeed], Sarsangi, A.[Alireza], Ferreira, C.S.S.[Carla Sofia Santos], Kalantari, Z.[Zahra],
Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zheng, J.X.[Jia-Xiang], Tian, Y.C.[Yi-Chen], Yuan, C.[Chao], Yin, K.[Kai], Zhang, F.F.[Fei-Fei], Chen, F.M.[Fang-Miao], Chen, Q.[Qiang],
MDESNet: Multitask Difference-Enhanced Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Chen, Z.L.[Zhan-Long], Zhou, Y.[Yuan], Wang, B.[Bin], Xu, X.W.[Xu-Wei], He, N.[Nan], Jin, S.[Shuai], Jin, S.[Shenrui],
EGDE-Net: A building change detection method for high-resolution remote sensing imagery based on edge guidance and differential enhancement,
PandRS(191), 2022, pp. 203-222.
Elsevier DOI 2208
Building change detection, Transformer, Edge guidance, Feature fusion BibRef

Xu, X.[Xuwei], Zhou, Y.[Yuan], Lu, X.[Xiechun], Chen, Z.L.[Zhan-Long],
FERA-Net: A Building Change Detection Method for High-Resolution Remote Sensing Imagery Based on Residual Attention and High-Frequency Features,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Zhang, J.[Jian], Pan, B.[Bin], Zhang, Y.[Yu], Liu, Z.L.[Zhang-Le], Zheng, X.[Xin],
Building Change Detection in Remote Sensing Images Based on Dual Multi-Scale Attention,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Xu, C.[Chuan], Ye, Z.Y.[Zhao-Yi], Mei, L.[Liye], Shen, S.[Sen], Zhang, Q.[Qi], Sui, H.G.[Hai-Gang], Yang, W.[Wei], Sun, S.H.[Shao-Hua],
SCAD: A Siamese Cross-Attention Discrimination Network for Bitemporal Building Change Detection,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Stilla, U.[Uwe], Xu, Y.S.[Yu-Sheng],
Change detection of urban objects using 3D point clouds: A review,
PandRS(197), 2023, pp. 228-255.
Elsevier DOI 2303
Change detection, Point clouds, Urban objects, Applications BibRef

de Gélis, I.[Iris], Lefèvre, S.[Sébastien], Corpetti, T.[Thomas],
Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning,
PandRS(197), 2023, pp. 274-291.
Elsevier DOI 2303
3D point clouds, Change detection, Deep learning, Siamese network, 3D Kernel Point Convolution BibRef

Yang, H.P.[Hai-Ping], Chen, Y.Y.[Yuan-Yuan], Wu, W.[Wei], Pu, S.L.[Shi-Liang], Wu, X.Y.[Xiao-Yang], Wan, Q.M.[Qi-Ming], Dong, W.[Wen],
A Lightweight Siamese Neural Network for Building Change Detection Using Remote Sensing Images,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Xu, C.[Chuan], Ye, Z.Y.[Zhao-Yi], Mei, L.[Liye], Yang, W.[Wei], Hou, Y.Y.[Ying-Ying], Shen, S.[Sen], Ouyang, W.[Wei], Ye, Z.W.[Zhi-Wei],
Progressive Context-Aware Aggregation Network Combining Multi-Scale and Multi-Level Dense Reconstruction for Building Change Detection,
RS(15), No. 8, 2023, pp. 1958.
DOI Link 2305
BibRef

Li, Y.[Yute], Chen, H.[He], Dong, S.[Shan], Zhuang, Y.[Yin], Li, L.L.[Lian-Lin],
Multi-Temporal SamplePair Generation for Building Change Detection Promotion in Optical Remote Sensing Domain Based on Generative Adversarial Network,
RS(15), No. 9, 2023, pp. xx-yy.
DOI Link 2305
BibRef

Huang, L.[Liang], Tian, Q.Y.[Qiu-Yuan], Tang, B.H.[Bo-Hui], Le, W.P.[Wei-Peng], Wang, M.[Min], Ma, X.[Xianguang],
Siam-EMNet: A Siamese EfficientNet-MANet Network for Building Change Detection in Very High Resolution Images,
RS(15), No. 16, 2023, pp. 3972.
DOI Link 2309
BibRef

Zhang, H.C.[Huang-Chuang], Li, G.[Ge],
A Digital Grid Model for Complex Time-Varying Environments in Civil Engineering Buildings,
RS(15), No. 16, 2023, pp. 4037.
DOI Link 2309
BibRef

Chen, Y.[Yao], Zhang, J.[Jindou], Shao, Z.F.[Zhen-Feng], Huang, X.[Xiao], Ding, Q.[Qing], Li, X.[Xianyi], Huang, Y.[Youju],
A Siamese Multiscale Attention Decoding Network for Building Change Detection on High-Resolution Remote Sensing Images,
RS(15), No. 21, 2023, pp. 5127.
DOI Link 2311
BibRef

He, R.J.[Ren-Jie], Li, W.[Wenyao], Mei, S.H.[Shao-Hui], Dai, Y.C.[Yu-Chao], He, M.Y.[Ming-Yi],
EFP-Net: A Novel Building Change Detection Method Based on Efficient Feature Fusion and Foreground Perception,
RS(15), No. 22, 2023, pp. 5268.
DOI Link 2311
BibRef

Zhu, Y.P.[Yang-Peng], Fan, L.J.[Li-Juan], Li, Q.Y.[Qian-Yu], Chang, J.[Jing],
Multi-Scale Discrete Cosine Transform Network for Building Change Detection in Very-High-Resolution Remote Sensing Images,
RS(15), No. 21, 2023, pp. 5243.
DOI Link 2311
BibRef

Fuentes-Reyes, M.[Mario], Xie, Y.X.[Yu-Xing], Yuan, X.T.[Xiang-Tian], d'Angelo, P.[Pablo], Kurz, F.[Franz], Cerra, D.[Daniele], Tian, J.J.[Jiao-Jiao],
A 2D/3D multimodal data simulation approach with applications on urban semantic segmentation, building extraction and change detection,
PandRS(205), 2023, pp. 74-97.
Elsevier DOI Code:
WWW Link. 2311
3D change detection, Building extraction, Urban semantic segmentation, Synthetic datasets BibRef

Feng, W.Q.[Wen-Qing], Guan, F.[Fangli], Tu, J.H.[Ji-Hui], Sun, C.H.[Chen-Hao], Xu, W.[Wei],
Detection of Changes in Buildings in Remote Sensing Images via Self-Supervised Contrastive Pre-Training and Historical Geographic Information System Vector Maps,
RS(15), No. 24, 2023, pp. 5670.
DOI Link 2401
BibRef


Srivastava, K.[Kushagra], Patel, D.[Dhruv], Jha, A.K.[Aditya Kumar], Jha, M.K.[Mohhit Kumar], Singh, J.[Jaskirat], Sarvadevabhatla, R.K.[Ravi Kiran], Ramancharla, P.K.[Pradeep Kumar], Kandath, H.[Harikumar], Krishna, K.M.[K. Madhava],
UAV-based Visual Remote Sensing for Automated Building Inspection,
CVCivil22(299-316).
Springer DOI 2304
BibRef

Yuan, X., Azimi, S.M., Henry, C., Gstaiger, V., Codastefano, M., Manalili, M., Cairo, S., Modugno, S., Wieland, M., Schneibel, A., Merkle, N.,
Automated Building Segmentation and Damage Assessment From Satellite Images for Disaster Relief,
ISPRS21(B3-2021: 741-748).
DOI Link 2201
BibRef

Yuan, W., Yuan, X., Fan, Z., Guo, Z., Shi, X., Gong, J., Shibasaki, R.,
Graph Neural Network Based Multi-feature Fusion for Building Change Detection,
ISPRS21(B3-2021: 377-382).
DOI Link 2201
BibRef

Lian, X., Yuan, W., Guo, Z., Cai, Z., Song, X., Shibasaki, R.,
End-to-end Building Change Detection Model In Aerial Imagery And Digital Surface Model Based on Neural Networks,
ISPRS20(B2:1239-1246).
DOI Link 2012
BibRef

Tran, H., Khoshelham, K.,
Building Change Detection Through Comparison of a Lidar Scan With A Building Information Model,
Indoor3D19(889-893).
DOI Link 1912
BibRef

Fangi, G.,
Aleppo - Before and After,
3DARCH19(333-338).
DOI Link 1904
BibRef

Azzola, P., Cardaci, A., Versaci, A.,
Integrated 3D Survey and Diagnostic Analysis for the Building Engineering: the Former Kindergarten San Filippo Neri in Dalmine,
3DARCH19(51-56).
DOI Link 1904
BibRef

Ferguson, M., Law, K.,
A 2D-3D Object Detection System for Updating Building Information Models with Mobile Robots,
WACV19(1357-1365)
IEEE DOI 1904
image colour analysis, image sensors, Kalman filters, mobile robots, object detection, robot vision, Cameras BibRef

Gonçalves, J.[Joana], Mateus, R.[Ricardo], Silvestre, J.D.[José Dinis],
Comparative Analysis of Inspection and Diagnosis Tools for Ancient Buildings,
EuroMed18(II:289-298).
Springer DOI 1811
Inspection of the state of conservation of buildings. BibRef

Gálai, B.[Bence], Benedek, C.[Csaba],
Change Detection in Urban Streets by a Real Time Lidar Scanner and MLS Reference Data,
ICIAR17(210-220).
Springer DOI 1706
BibRef

Sabuncu, A., Avci, Z.D.U.[Z. D. Uca], Sunar, F.,
Preliminary Results Of Earthquake-induced Building Damage Detection With Object-based Image Classification,
ISPRS16(B7: 347-350).
DOI Link 1610
BibRef

Hron, V., Halounova, L.,
Nationwide Hybrid Change Detection Of Buildings,
ISPRS16(B7: 497-504).
DOI Link 1610
BibRef

Vacca, G., Mistretta, F., Stochino, F., Dessi, A.,
Terrestrial Laser Scanner For Monitoring The Deformations And The Damages Of Buildings,
ISPRS16(B5: 453-460).
DOI Link 1610
BibRef

Peng, D.F.[Dai-Feng], Zhang, Y.J.[Yong-Jun],
Building Change Detection By Combining Lidar Data And Ortho Image,
ISPRS16(B3: 669-676).
DOI Link 1610
BibRef

Chen, J., Hou, J.L., Deng, M.,
An Approach To Alleviate The False Alarm In Building Change Detection From Urban VHR Image,
ISPRS16(B7: 459-465).
DOI Link 1610
BibRef

Cheriguene, R.S., Mahi, H.,
Buildings Change Detection on Quickbird Imagery,
CGiV16(368-371)
IEEE DOI 1608
buildings (structures) BibRef

Pontecorvo, C., Sherrah, J.[Jamie],
Anomaly Detection of Man-Made Objects in Large Aerial Images,
DICTA15(1-8)
IEEE DOI 1603
image classification BibRef

Nakagawa, M., Yamamoto, T., Tanaka, S., Noda, Y., Hashimoto, K., Ito, M., Miyo, M.,
Location-Based Infrastructure Inspection for Sabo Facilities,
Gi4DM15(257-262).
DOI Link 1602
BibRef

Chen, B.H.[Bao-Hua], Deng, L.[Lei], Duan, Y.Q.[Yue-Qi], Huang, S.Y.[Si-Yuan], Zhou, J.[Jie],
Building change detection based on 3D reconstruction,
ICIP15(4126-4130)
IEEE DOI 1512
2D-3D registration BibRef

Hron, V., Halounova, L.,
Use of Aerial Images for Regular Updates of Buildings in the Fundamental Base of Geographic Data of the Czech Republic,
PIA15(73-79).
DOI Link 1504
BibRef

Huang, J.[Jing], You, S.[Suya],
Change Detection in Laser-Scanned Data of Industrial Sites,
WACV15(733-740)
IEEE DOI 1503
Data models. BibRef

Tetsuka, D.[Daiki], Okatani, T.[Takayuki],
Detecting Building-Level Changes of a City Using Street Images and a 2D City Map,
WACV15(349-356)
IEEE DOI 1503
Buildings BibRef

Zong, K.[Kaibin], Sowmya, A.[Arcot], Trinder, J.,
Building Change Detection Based on Markov Random Field: Exploiting Both Pixel and Corner Features,
DICTA15(1-7)
IEEE DOI 1603
BibRef
Earlier:
Kernel Partial Least Squares Based Hierarchical Building Change Detection Using High Resolution Aerial Images and Lidar Data,
DICTA13(1-7)
IEEE DOI 1402
Markov processes. airborne radar BibRef

Tian, J., Reinartz, P.,
Comparison of Two Fusion Based Building Change Detection Methods Using Satellite Stereo Imagery and DSMS,
IWIDF13(103-108).
DOI Link 1311

See also Region Based Forest Change Detection from CARTOSAT-1 Stereo Imagery. BibRef

Saldana, M., Johanson, C.,
Procedural Modeling for Rapid-Prototyping of Multiple Building Phases,
3DARCH13(205-210).
DOI Link 1308
BibRef

Beumier, C.[Charles], Idrissa, M.[Mahamadou],
Building Change Detection from Uniform Regions,
CIARP12(648-655).
Springer DOI 1209
BibRef

Dini, G.R., Jacobsen, K., Rottensteiner, F., Al Rajhi, M., Heipke, C.,
3D Building Change Detection Using High Resolution Stereo Images and a GIS Database,
ISPRS12(XXXIX-B7:299-304).
DOI Link 1209
BibRef

du Plessis, S.,
Identifying Building Change Using High Resolution Point Clouds: An Object-based Approach,
ISPRS12(XXXIX-B7:305-309).
DOI Link 1209
BibRef

Ishimaru, N., Iwamura, K., Kagawa, Y., Hino, T.,
Housediff: A Map-based Building Change Detection From High Resolution Satellite Imagery Using Geometric Optimization Method,
ISPRS12(XXXIX-B4:73-78).
DOI Link 1209
BibRef

Tanauchi, Y., Chikatsu, H.,
Efficient Extraction Method of the Change of Buildings for Fixed Property Investigation,
ISPRS12(XXXIX-B5:57-62).
DOI Link 1209
BibRef

Champion, N., Rottensteiner, F., Matikainen, L.[Leena], Liang, X., Hyyppä, J.[Juha], Olsen, B.P.,
A Test of Automatic Building Change Detection Approaches,
CMRT09(145-150).
PDF File. 0909
BibRef

Champion, N.,
2D Building Change Detection from High Resolution Aerial Images and Correlation Digital Surface Models,
PIA07(197).
PDF File. 0711
BibRef

Nakagawa, M.[Masafumi], Shibasaki, R.[Ryosuke],
Building Change Detection Using 3-D Texture Model,
ISPRS08(B3a: 173 ff).
PDF File. 0807
BibRef

Rottensteiner, F.[Franz],
Automated Updating of Building Data Bases from Digital Surface Models and Multi-Spectral Images: Potential and Limitations,
ISPRS08(B3a: 265 ff).
PDF File. 0807
BibRef
Earlier:
Building Change Detection from Digital Surface Models and Multi-Spectral Images,
PIA07(145).
PDF File. 0711
BibRef

Li, W.M.[Wei-Ming], Li, X.M.[Xiao-Ming], Wu, Y.H.[Yi-Hong], Hu, Z.Y.[Zhan-Yi],
A Novel Framework for Urban Change Detection Using VHR Satellite Images,
ICPR06(II: 312-315).
IEEE DOI 0609
BibRef

Watanabe, S., Miyajima, K.,
Detecting Building Changes Using Epipolar Constraint from Aerial Images Taken at Different Positions,
ICIP01(II: 201-204).
IEEE DOI 0108
BibRef

Jamet, O., Maitre, H., Le Men, H.,
Applying the Theory of Evidence to Vector-D.E.M. Comparison for the Building Planimetric Change Detection,
ISPRSGIS99(29-34). BibRef 9900

Lu, W., Doihara, T., Matsumoto, Y.,
Detection of Building Changes from Aerial Images Through Information Fusion,
MVA98(xx-yy). BibRef 9800

Mukawa, N.[Naoki], Miyajima, K.[Koji], Watanabe, S.[Shintaro],
Detecting Changes of Buildings from Aerial Images Using Shadow and Shading Model,
ICPR98(Vol II: 1408-1412).
IEEE DOI 9808
BibRef

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Change Detection for Damage Assessment .


Last update:Mar 16, 2024 at 20:36:19