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

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

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.

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

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,
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DOI Link 1203

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
Automatic estimation of fine terrain models from multiple high-resolution satellite images,
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,
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).
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

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

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.[Yihui],
Object-Based Analysis of Airborne LiDAR Data for Building Change Detection,
RS(6), No. 11, 2014, pp. 10733-10749.
DOI Link 1412

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

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

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.[Yuqi], Fan, C.[Chong],
Building Change Detection Using Old Aerial Images and New LiDAR Data,
RS(8), No. 12, 2016, pp. 1030.
DOI Link 1612

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.
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

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

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

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

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

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

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

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

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

Endo, Y.[Yukio], Adriano, B.[Bruno], Mas, E.[Erick], Koshimura, S.[Shunichi],
New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

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

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

Wen, D.[Dawei], 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

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

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

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

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

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

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

Jiang, H.[Huiwei], Hu, X.Y.[Xiang-Yun], Li, K.[Kun], Zhang, J.M.[Jin-Ming], Gong, J.[Jinqi], 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

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

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

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

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

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

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

Cao, S.S.[Shi-Song], Du, M.Y.[Ming-Yi], Zhao, W.[Wenji], 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

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

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,
DOI Link 2012

Tran, H., Khoshelham, K.,
Building Change Detection Through Comparison of a Lidar Scan With A Building Information Model,
DOI Link 1912

Fangi, G.,
Aleppo - Before and After,
DOI Link 1904

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

Ferguson, M., Law, K.,
A 2D-3D Object Detection System for Updating Building Information Models with Mobile Robots,
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,
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,
Springer DOI 1706

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

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

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

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

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

Cheriguene, R.S., Mahi, H.,
Buildings Change Detection on Quickbird Imagery,
buildings (structures) BibRef

Pontecorvo, C., Sherrah, J.[Jamie],
Anomaly Detection of Man-Made Objects in Large Aerial Images,
image classification BibRef

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

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,
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,
DOI Link 1504

Huang, J.[Jing], You, S.[Suya],
Change Detection in Laser-Scanned Data of Industrial Sites,
Data models. BibRef

Tetsuka, D.[Daiki], Okatani, T.[Takayuki],
Detecting Building-Level Changes of a City Using Street Images and a 2D City Map,
Buildings BibRef

Zong, K.[Kaibin], Sowmya, A.[Arcot], Trinder, J.,
Building Change Detection Based on Markov Random Field: Exploiting Both Pixel and Corner Features,
Kernel Partial Least Squares Based Hierarchical Building Change Detection Using High Resolution Aerial Images and Lidar Data,
Markov processes. airborne radar BibRef

Tian, J., Reinartz, P.,
Comparison of Two Fusion Based Building Change Detection Methods Using Satellite Stereo Imagery and DSMS,
HTML Version. 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,
DOI Link 1308

Beumier, C.[Charles], Idrissa, M.[Mahamadou],
Building Change Detection from Uniform Regions,
Springer DOI 1209

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,
DOI Link 1209

du Plessis, S.,
Identifying Building Change Using High Resolution Point Clouds: An Object-based Approach,
DOI Link 1209

Ishimaru, N., Iwamura, K., Kagawa, Y., Hino, T.,
Housediff: A Map-based Building Change Detection From High Resolution Satellite Imagery Using Geometric Optimization Method,
DOI Link 1209

Tanauchi, Y., Chikatsu, H.,
Efficient Extraction Method of the Change of Buildings for Fixed Property Investigation,
DOI Link 1209

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

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

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

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
Building Change Detection from Digital Surface Models and Multi-Spectral Images,
PDF File. 0711

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

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

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).

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

Last update:Jan 17, 2021 at 16:22:28