24.2.2.2 Change Detection for Damage Assessment

Chapter Contents (Back)
Remote Sensing. Registration. Aerial Image Analysis. Change Detection. Damage Assessment. Building Damage.
See also Change Detection -- Image Level.
See also Forest Fire Evaluation, Wildfire Analysis, Brushfire Analysis, Fire Detection.
See also Flood Analysis, Flood Mapping, Flood Monitoring.
See also Tsunami Detection, Analysis, Warning, Disaster.

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OptEng(37), No. 3, March 1998, pp. 898-903. 9804
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RSE(70), No. 2, 1999, pp. 208-223. 9911
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Al-Khudhairy, D.H.A., Caravaggi, I., Giada, S.,
Structural Damage Assessments from Ikonos Data Using Change Detection, Object-Oriented Segmentation, and Classification Techniques,
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Gamba, P., Dell'Acqua, F., Trianni, G.,
Rapid Damage Detection in the Bam Area Using Multitemporal SAR and Exploiting Ancillary Data,
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IEEE DOI 0706

See also Improvements to urban area characterization using multitemporal and multiangle SAR images. BibRef

Dell'Acqua, F., Gamba, P.,
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PIEEE(100), No. 10, October 2012, pp. 2876-2890.
IEEE DOI 1210
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Trianni, G.[Giovanna], Gamba, P.[Paolo],
Fast damage mapping in case of earthquakes using multitemporal SAR data,
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Springer DOI 0909
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Sertel, E., Kaya, S., Curran, P.J.,
Use of Semivariograms to Identify Earthquake Damage in an Urban Area,
GeoRS(45), No. 6, June 2007, pp. 1590-1594.
IEEE DOI 0706
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Barnes, C.F., Fritz, H., Yoo, J.,
Hurricane Disaster Assessments With Image-Driven Data Mining in High-Resolution Satellite Imagery,
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Barnes, C.F.,
Image-Driven Data Mining for Image Content Segmentation, Classification, and Attribution,
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Bahirat, K., Bovolo, F., Bruzzone, L., Chaudhuri, S.,
A Novel Domain Adaptation Bayesian Classifier for Updating Land-Cover Maps With Class Differences in Source and Target Domains,
GeoRS(50), No. 7, July 2012, pp. 2810-2826.
IEEE DOI 1208
BibRef

Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment,
GeoRS(45), No. 6, June 2007, pp. 1658-1670.
IEEE DOI 0706

See also Detail-Preserving Scale-Driven Approach to Change Detection in Multitemporal SAR Images, A. BibRef

Bovolo, F.[Francesca], Marin, C.[Carlo], Bruzzone, L.[Lorenzo],
A Hierarchical Approach to Change Detection in Very High Resolution SAR Images for Surveillance Applications,
GeoRS(51), No. 4, April 2013, pp. 2042-2054.
IEEE DOI 1304
BibRef
Earlier:
A multilevel approach to change detection for port surveillance with very high resolution SAR images,
MultiTemp11(9-12).
IEEE DOI 1109
BibRef

Bertoluzza, M., Bruzzone, L.[Lorenzo], Bovolo, F.[Francesca],
Circular change detection in image time series inspired by two-dimensional phase unwrapping,
MultiTemp17(1-4)
IEEE DOI 1712
Big Data, geophysical image processing, geophysical techniques, image resolution, time series, 2D phase unwrapping, CD error, unwrapping BibRef

Bovolo, F., Bruzzone, L., Marconcini, M.,
A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure,
GeoRS(46), No. 7, July 2008, pp. 2070-2082.
IEEE DOI 0806
BibRef

Yin, L.[Li], Silverman, R.M.[Robert Mark],
Housing Abandonment and Demolition: Exploring the Use of Micro-Level and Multi-Year Models,
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Solano-Correa, Y.T.[Yady Tatiana], Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
Generation of Homogeneous VHR Time Series by Nonparametric Regression of Multisensor Bitemporal Images,
GeoRS(57), No. 10, October 2019, pp. 7579-7593.
IEEE DOI 1910
geophysical image processing, image classification, image resolution, optical sensors, regression analysis, very high geometrical resolution (VHR) time series (TS) BibRef

Solano-Correa, Y.T., Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo], Fernández-Prieto, D.,
Spatio-temporal evolution of crop fields in Sentinel-2 Satellite Image Time Series,
MultiTemp17(1-4)
IEEE DOI 1712
crops, geophysical techniques, Barrax, Sentinel-2 satellite image time series, Spain, Spatio-temporal mapping BibRef

Bovolo, F., Marchesi, S., Bruzzone, L.,
A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images,
GeoRS(50), No. 6, June 2012, pp. 2196-2212.
IEEE DOI 1205

See also Detail-Preserving Scale-Driven Approach to Change Detection in Multitemporal SAR Images, A. BibRef

Marinelli, D., Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
A Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors,
GeoRS(57), No. 7, July 2019, pp. 4913-4928.
IEEE DOI 1907
BibRef
Earlier:
A novel method for unsupervised multiple Change Detection in hyperspectral images based on binary Spectral Change Vectors,
MultiTemp17(1-4)
IEEE DOI 1712
Hyperspectral imaging, Image coding, Principal component analysis, Complexity theory, multitemporal images. geophysical techniques, land cover, vegetation, ad-hoc techniques, agricultural area, Spatial resolution
See also Support Vector Domain Method For Change Detection In Multitemporal Images, A. BibRef

Saha, S., Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images,
GeoRS(57), No. 6, June 2019, pp. 3677-3693.
IEEE DOI 1906
Feature extraction, Remote sensing, Image segmentation, Optical imaging, Optical sensors, very high-resolution images BibRef

Saha, S., Bovolo, F., Bruzzone, L.,
Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding,
GeoRS(59), No. 3, March 2021, pp. 1917-1929.
IEEE DOI 1806
Optical imaging, Radar polarimetry, Feature extraction, Optical sensors, Synthetic aperture radar, Transcoding, Buildings, very high-resolution images BibRef

Fernandez-Prieto, D., Marconcini, M.,
A Novel Partially Supervised Approach to Targeted Change Detection,
GeoRS(49), No. 12, December 2011, pp. 5016-5038.
IEEE DOI 1201
BibRef

Chini, M., Pierdicca, N., Emery, W.J.,
Exploiting SAR and VHR Optical Images to Quantify Damage Caused by the 2003 Bam Earthquake,
GeoRS(47), No. 1, January 2009, pp. 145-152.
IEEE DOI 0901
BibRef

Tagliavini, F., Reichenbach, P., Maragna, D., Guzzetti, F., Pasuto, A.,
Comparison of 2-D and 3-D computer models for the M. Salta rock fall, Vajont Valley, northern Italy,
GeoInfo(13), No. 3, September 2009, pp. xx-yy.
Springer DOI 0905
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Cohen, A.R.[Andrew R.], Bjornsson, C.S.[Christopher S.], Temple, S.[Sally], Banker, G.[Gary], Roysam, B.[Badrinath],
Automatic Summarization of Changes in Biological Image Sequences Using Algorithmic Information Theory,
PAMI(31), No. 8, August 2009, pp. 1386-1403.
IEEE DOI 0906
Summarize changes in a medical sequence. Tissue strain due to insertion of probe/tool. BibRef

Brunner, D., Lemoine, G., Bruzzone, L.,
Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery,
GeoRS(48), No. 5, May 2010, pp. 2403-2420.
IEEE DOI 1006

See also Building Height Retrieval From VHR SAR Imagery Based on an Iterative Simulation and Matching Technique. BibRef

Manfredi, M., Aldrighi, M., Dell'Acqua, F.,
Eigenmethod for Feature Matching of Pre- and Postevent Images Exploiting Adjacency,
GeoRS(48), No. 7, July 2010, pp. 2890-2898.
IEEE DOI 1007
BibRef

Guglielmino, F., Nunnari, G., Puglisi, G., Spata, A.,
Simultaneous and Integrated Strain Tensor Estimation From Geodetic and Satellite Deformation Measurements to Obtain Three-Dimensional Displacement Maps,
GeoRS(49), No. 6, June 2011, pp. 1815-1826.
IEEE DOI 1106
BibRef

Splinter, K.D., Strauss, D.R., Tomlinson, R.B.,
Assessment of Post-Storm Recovery of Beaches Using Video Imaging Techniques: A Case Study at Gold Coast, Australia,
GeoRS(49), No. 12, December 2011, pp. 4704-4716.
IEEE DOI 1201
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Sjahputera, O., Scott, G.J., Claywell, B.C., Klaric, M.N., Hudson, N.J., Keller, J.M., Davis, C.H.,
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GeoRS(49), No. 12, December 2011, pp. 4687-4703.
IEEE DOI 1201
BibRef

Klaric, M.N., Claywell, B.C., Scott, G.J., Hudson, N.J., Sjahputera, O., Li, Y., Barratt, S.T., Keller, J.M., Davis, C.H.,
GeoCDX: An Automated Change Detection and Exploitation System for High-Resolution Satellite Imagery,
GeoRS(51), No. 4, April 2013, pp. 2067-2086.
IEEE DOI 1304
BibRef

Matsuoka, M., Nojima, N.,
Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery,
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DOI Link 1203
BibRef

Bielski, C., Gentilini, S., Pappalardo, M.,
Post-Disaster Image Processing for Damage Analysis Using GENESI-DR, WPS and Grid Computing,
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DOI Link 1203
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Ghoshal, S., James, L., Singer, M., Aalto, R.,
Channel and Floodplain Change Analysis over a 100-Year Period: Lower Yuba River, California,
RS(2), No. 7, July 2010, pp. 1797-1825.
DOI Link 1203
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Rocchini, D.,
Ecological Status and Change by Remote Sensing,
RS(2), No. 10, October 2010, pp. 2424-2425.
DOI Link 1203
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Vassilakis, E.,
Remote Sensing of Environmental Change in the Antirio Deltaic Fan Region, Western Greece,
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DOI Link 1203
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Tong, X.H.[Xiao-Hua], Hong, Z.H.[Zhong-Hua], Liu, S.J.[Shi-Jie], Zhang, X.[Xue], Xie, H.[Huan], Li, Z.Y.[Zheng-Yuan], Yang, S.[Sonlin], Wang, W.[Weian], Bao, F.[Feng],
Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake,
PandRS(68), No. 1, March 2012, pp. 13-27.
Elsevier DOI 1204
Building collapse detection; Earthquake; Rational polynomial coefficient; Geopositioning accuracy; Digital elevation model; IKONOS stereo images BibRef

Debella-Gilo, M.[Misganu], Kääb, A.[Andreas],
Locally adaptive template sizes for matching repeat images of Earth surface mass movements,
PandRS(69), No. 1, April 2012, pp. 10-28.
Elsevier DOI 1202
Image matching; Normalized cross-correlation; Mass movement; Displacement; Adaptive template BibRef

Towler, J., Krawiec, B., Kochersberger, K.,
Radiation Mapping in Post-Disaster Environments Using an Autonomous Helicopter,
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Geospatial Assessment of Recovery Rates Following a Tornado Disaster,
GeoRS(50), No. 11, November 2012, pp. 4313-4322.
IEEE DOI 1210
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Yamaguchi, Y.,
Disaster Monitoring by Fully Polarimetric SAR Data Acquired With ALOS-PALSAR,
PIEEE(100), No. 10, October 2012, pp. 2851-2860.
IEEE DOI 1210
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Hooper, A., Prata, F., Sigmundsson, F.,
Remote Sensing of Volcanic Hazards and Their Precursors,
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IEEE DOI 1210
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Klonus, S.[Sascha], Ehlers, M.[Manfred], Tomowski, D.[Daniel], Michel, U.[Ulrich], Reinartz, P.[Peter],
Detection of Damaged Buildings in Crisis Areas from Panchromatic Remote Sensing Data,
PFG(2011), No. 4, 2011, pp. 219-231.
WWW Link. 1211
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Ehlers, M., Klonus, S., Jarmer, T., Sofina, N., Michel, U., Reinartz, P., Sirmacek, B.,
Cest Analysis: Automated Change Detection From Very-high-resolution Remote Sensing Images,
ISPRS12(XXXIX-B7:317-322).
DOI Link 1209
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Pesci, A.[Arianna], Teza, G.[Giordano], Bonali, E.[Elena], Casula, G.[Giuseppe], Boschi, E.[Enzo],
A laser scanning-based method for fast estimation of seismic-induced building deformations,
PandRS(79), No. 1, May 2013, pp. 185-198.
Elsevier DOI 1305
Architecture; Change detection; Laser scanning; Model; Performance BibRef

Tong, X.H.[Xiao-Hua], Lin, X.F.[Xiao-Fei], Feng, T.T.[Tian-Tian], Xie, H.[Huan], Liu, S.J.[Shi-Jie], Hong, Z.H.[Zhong-Hua], Chen, P.[Peng],
Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery,
PandRS(79), No. 1, May 2013, pp. 53-67.
Elsevier DOI 1305
Hybrid approach; Shadow analysis; Building collapse detection; High-resolution satellite image; Earthquake-induced damage assessment; Accuracy BibRef

Brett, P.T.B., Guida, R.,
Earthquake Damage Detection in Urban Areas Using Curvilinear Features,
GeoRS(51), No. 9, 2013, pp. 4877-4884.
IEEE DOI 1309
Buildings BibRef

Dong, L.G.[Lai-Gen], Shan, J.[Jie],
A comprehensive review of earthquake-induced building damage detection with remote sensing techniques,
PandRS(84), No. 0, 2013, pp. 85-99.
Elsevier DOI 1309
Earthquakes BibRef

Frattini, P.[Paolo], Crosta, G.B.[Giovanni B.], Allievi, J.[Jacopo],
Damage to Buildings in Large Slope Rock Instabilities Monitored with the PSInSAR™ Technique,
RS(5), No. 10, 2013, pp. 4753-4773.
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Kerfoot, W.C.[W. Charles], Hobmeier, M.M.[Martin M.], Yousef, F.[Foad], Green, S.A.[Sarah A.], Regis, R.[Robert], Brooks, C.N.[Colin N.], Shuchman, R.[Robert], Anderson, J.[Jamey], Reif, M.[Molly],
Light Detection and Ranging (LiDAR) and Multispectral Scanner (MSS) Studies Examine Coastal Environments Influenced by Mining,
IJGI(3), No. 1, 2014, pp. 66-95.
DOI Link 1402
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Thomas, J., Kareem, A., Bowyer, K.W.,
Automated Poststorm Damage Classification of Low-Rise Building Roofing Systems Using High-Resolution Aerial Imagery,
GeoRS(52), No. 7, July 2014, pp. 3851-3861.
IEEE DOI 1403
Buildings BibRef

Li, N.[Ning], Wang, R.[Robert], Liu, Y.[Yabo], Du, K.N.[Kang-Ning], Chen, J.Q.[Jia-Qi], Deng, Y.K.[Yun-Kai],
Robust river boundaries extraction of dammed lakes in mountain areas after Wenchuan Earthquake from high resolution SAR images combining local connectivity and ACM,
PandRS(94), No. 1, 2014, pp. 91-101.
Elsevier DOI 1407
Airborne SAR imagery BibRef

Plank, S.[Simon],
Rapid Damage Assessment by Means of Multi-Temporal SAR: A Comprehensive Review and Outlook to Sentinel-1,
RS(6), No. 6, 2014, pp. 4870-4906.
DOI Link 1407
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Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
A Detail-Preserving Scale-Driven Approach to Change Detection in Multitemporal SAR Images,
GeoRS(43), No. 12, December 2005, pp. 2963-2972.
IEEE DOI 0512
BibRef
Earlier:
An Adaptive Multiscale Approach to Unsupervised Change Detection in Multitemporal SAR Images,
ICIP05(I: 665-668).
IEEE DOI 0512

See also Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment, A. BibRef

Marin, C., Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
Building Change Detection in Multitemporal Very High Resolution SAR Images,
GeoRS(53), No. 5, May 2015, pp. 2664-2682.
IEEE DOI 1502
radar detection BibRef

Domínguez, E.M.[E. Méndez], Meier, E., Small, D., Schaepman, M.E., Bruzzone, L., Henke, D.,
A Multisquint Framework for Change Detection in High-Resolution Multitemporal SAR Images,
GeoRS(56), No. 6, June 2018, pp. 3611-3623.
IEEE DOI 1806
Array signal processing, Azimuth, Backscatter, Detectors, Spatial resolution, Synthetic aperture radar, Image processing, wavelets BibRef

Liu, S.C.[Si-Cong], Bruzzone, L.[Lorenzo], Bovolo, F.[Francesca], Du, P.J.[Pei-Jun],
Hierarchical Unsupervised Change Detection in Multitemporal Hyperspectral Images,
GeoRS(53), No. 1, January 2015, pp. 244-260.
IEEE DOI 1410

See also Detail-Preserving Scale-Driven Approach to Change Detection in Multitemporal SAR Images, A.
See also Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment, A. artificial satellites BibRef

Bruzzone, L.[Lorenzo], Bovolo, F.[Francesca], Paris, C., Solano-Correa, Y.T.[Y. Tatiana], Zanetti, M., Fernández-Prieto, D.,
Analysis of multitemporal Sentinel-2 images in the framework of the ESA Scientific Exploitation of Operational Missions,
MultiTemp17(1-4)
IEEE DOI 1712
land cover, ESA scientific exploitation of operational missions, Vegetation mapping BibRef

Liu, S.C.[Si-Cong], Du, P.J.[Pei-Jun],
Object-Oriented Change Detection from Multi-Temporal Remotely Sensed Images,
GEOBIA10(xx-yy).
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Wang, J.H.[Jian-Hua], Qin, Q.M.[Qi-Ming], Zhao, J.H.[Jiang-Hua], Ye, X.[Xin], Feng, X.[Xiao], Qin, X.B.[Xue-Bin], Yang, X.C.[Xiu-Cheng],
Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image,
RS(7), No. 4, 2015, pp. 4948-4967.
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Wang, J.H.[Jian-Hua], Qin, Q.M.[Qi-Ming], Gao, Z.L.[Zhong-Ling], Zhao, J.H.[Jiang-Hua], Ye, X.[Xin],
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IJGI(5), No. 7, 2016, pp. 114.
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Vetrivel, A.[Anand], Gerke, M.[Markus], Kerle, N.[Norman], Vosselman, G.[George],
Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images,
PandRS(105), No. 1, 2015, pp. 61-78.
Elsevier DOI 1506
Oblique images BibRef

Osaragi, T.[Toshihiro],
Modeling Obstruction and Restoration of Urban Commutation Networks in the Wake of a Devastating Earthquake in Tokyo,
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Jiang, W.G.[Wei-Guo], Jia, K.[Kai], Wu, J.J.[Jian-Jun], Tang, Z.H.[Zheng-Hong], Wang, W.J.[Wen-Jie], Liu, X.F.[Xiao-Fu],
Evaluating the Vegetation Recovery in the Damage Area of Wenchuan Earthquake Using MODIS Data,
RS(7), No. 7, 2015, pp. 8757.
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Xu, H.[Hao], Cheng, L.[Liang], Li, M.C.[Man-Chun], Chen, Y.M.[Yan-Ming], Zhong, L.S.[Li-Shan],
Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data,
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Taneja, A.[Aparna], Ballan, L.[Luca], Pollefeys, M.[Marc],
Geometric Change Detection in Urban Environments Using Images,
PAMI(37), No. 11, November 2015, pp. 2193-2206.
IEEE DOI 1511
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Earlier:
Image based detection of geometric changes in urban environments,
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IEEE DOI 1201
object detection. to direct 3D analysis to changed areas. BibRef

Zhang, H.Z.[Huai-Zhen], Wang, X.M.[Xiao-Meng], Fan, J.R.[Jian-Rong], Chi, T.H.[Tian-He], Yang, S.[Shun], Peng, L.[Ling],
Monitoring Earthquake-Damaged Vegetation after the 2008 Wenchuan Earthquake in the Mountainous River Basins, Dujiangyan County,
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Zhang, H.Z.[Huai-Zhen], Chi, T.[Tianhe], Fan, J.R.[Jian-Rong], Hu, K.H.[Kai-Heng], Peng, L.[Ling],
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Zhai, W.[Wei], Shen, H.F.[Huan-Feng], Huang, C.L.[Chun-Lin], Pei, W.S.[Wan-Sheng],
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Zhai, W.[Wei], Huang, C.L.[Chun-Lin], Pei, W.S.[Wan-Sheng],
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Vetrivel, A.[Anand], Gerke, M.[Markus], Kerle, N.[Norman], Vosselman, G.[George],
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Gueguen, L., Hamid, R.,
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IEEE DOI 1606
geophysical image processing BibRef

Cerra, D.[Daniele], Plank, S.[Simon], Lysandrou, V.[Vasiliki], Tian, J.J.[Jiao-Jiao],
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Earlier: A1, A4, A3, A2:
Automatic Damage Detection For Sensitive Cultural Heritage Sites,
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Cooner, A.J.[Austin J.], Shao, Y.[Yang], Campbell, J.B.[James B.],
Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake,
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Chen, S.W., Wang, X.S., Sato, M.,
Urban Damage Level Mapping Based on Scattering Mechanism Investigation Using Fully Polarimetric SAR Data for the 3.11 East Japan Earthquake,
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IEEE DOI 1612
earthquakes BibRef

Gong, L.X.[Li-Xia], Wang, C.[Chao], Wu, F.[Fan], Zhang, J.F.[Jing-Fa], Zhang, H.[Hong], Li, Q.A.[Qi-Ang],
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Liu, H.[Hai], Koyama, C.[Christian], Zhu, J.F.[Jin-Feng], Liu, Q.[Qinghuo], Sato, M.[Motoyuki],
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Karimzadeh, S.[Sadra], Mastuoka, M.[Masashi],
Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy,
RS(9), No. 4, 2017, pp. xx-yy.
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Janalipour, M.[Milad], Mohammadzadeh, A.[Ali],
A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images,
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Tu, J.H.[Ji-Hui], Li, D.R.[De-Ren], Feng, W.Q.[Wen-Qing], Han, Q.[Qinhu], Sui, H.G.[Hai-Gang],
Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images,
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Xiao, Z.F.[Zhi-Feng], Long, Y.[Yang], Li, D.R.[De-Ren], Wei, C.S.[Chun-Shan], Tang, G.[Gefu], Liu, J.Y.[Jun-Yi],
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Frank, J.[Jared], Rebbapragada, U.[Umaa], Bialas, J.[James], Oommen, T.[Thomas], Havens, T.C.[Timothy C.],
Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage,
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Charrua, A.B.[Alberto Bento], Padmanaban, R.[Rajchandar], Cabral, P.[Pedro], Bandeira, S.[Salomão], Romeiras, M.M.[Maria M.],
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Amplitude Correlation, InSAR, Beirut Explosion, Hazard Assessment, Hypothesis Testing, Phase Filter BibRef

Yang, W.T.[Wan-Ting], Zhang, X.F.[Xian-Feng], Luo, P.[Peng],
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Chen, X.[Xue], Achilli, V.[Vladimiro], Fabris, M.[Massimo], Menin, A.[Andrea], Monego, M.[Michele], Tessari, G.[Giulia], Floris, M.[Mario],
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Wu, C.[Chuyi], Zhang, F.[Feng], Xia, J.S.[Jun-Shi], Xu, Y.C.[Yi-Chen], Li, G.Q.[Guo-Qing], Xie, J.[Jibo], Du, Z.H.[Zhen-Hong], Liu, R.Y.[Ren-Yi],
Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets,
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Nie, Y.L.[Yu-Liang], Zeng, Q.M.[Qi-Ming], Zhang, H.Z.[Hai-Zhen], Wang, Q.[Qing],
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Hasanlou, M.[Mahdi], Shah-Hosseini, R.[Reza], Seydi, S.T.[Seyd Teymoor], Karimzadeh, S.[Sadra], Matsuoka, M.[Masashi],
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Boloorani, A.D.[Ali Darvishi], Darvishi, M.[Mehdi], Weng, Q.H.[Qi-Hao], Liu, X.T.[Xiang-Tong],
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Adriano, B.[Bruno], Yokoya, N.[Naoto], Xia, J.[Junshi], Miura, H.[Hiroyuki], Liu, W.[Wen], Matsuoka, M.[Masashi], Koshimura, S.[Shunichi],
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Multimodal remote sensing, Disaster damage mapping, Deep convolutional neural network BibRef

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Chaidas, K.[Konstantinos], Tataris, G.[George], Soulakellis, N.[Nikolaos],
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Rashidian, V.[Vahid], Baise, L.G.[Laurie G.], Koch, M.[Magaly], Moaveni, B.[Babak],
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Balado, J.[Jesús], Arias, P.[Pedro], Lorenzo, H.[Henrique], Meijide-Rodríguez, A.[Adrián],
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Zhang, L.[Lin], Hu, X.Y.[Xiang-Yun], Zhang, M.[Mi], Shu, Z.[Zhen], Zhou, H.[Hao],
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PandRS(177), 2021, pp. 147-160.
Elsevier DOI 2106
Change detection, Object level, Attention, Dual learning, Data augmentation BibRef

Karimzadeh, S.[Sadra], Matsuoka, M.[Masashi],
A Preliminary Damage Assessment Using Dual Path Synthetic Aperture Radar Analysis for the M 6.4 Petrinja Earthquake (2020), Croatia,
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Wang, C.[Chao], Qiu, X.[Xing], Huan, H.[Hai], Wang, S.[Shuai], Zhang, Y.[Yan], Chen, X.H.[Xiao-Hui], He, W.[Wei],
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Zhang, Y.[Ying], Roffey, M.[Matthew], Leblanc, S.G.[Sylvain G.],
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de Giorgi, A.[Andrea], Solarna, D.[David], Moser, G.[Gabriele], Tapete, D.[Deodato], Cigna, F.[Francesca], Boni, G.[Giorgio], Rudari, R.[Roberto], Serpico, S.B.[Sebastiano Bruno], Pisani, A.R.[Anna Rita], Montuori, A.[Antonio], Zoffoli, S.[Simona],
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Park, S.[Sangki], Jung, K.[Kichul],
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IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
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DOI Link 2201
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Moya, L.[Luis], Geiß, C.[Christian], Hashimoto, M.[Masakazu], Mas, E.[Erick], Koshimura, S.[Shunichi], Strunz, G.[Günter],
Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification,
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IEEE DOI 2109
Buildings, Training, Remote sensing, Training data, Earthquakes, Support vector machines, Machine learning, Automatic labeling, support vector machine (SVM) BibRef

Lin, D.M.[Da-Ming], Wang, J.[Jie], Li, Y.D.[Yun-Dong],
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Donato, A.[Antonio], Randazzo, L.[Luciana], Ricca, M.[Michela], Rovella, N.[Natalia], Collina, M.[Matteo], Ruggieri, N.[Nicola], Dodaro, F.[Francesco], Costanzo, A.[Antonio], Alberghina, M.F.[Maria F.], Schiavone, S.[Salvatore], Buongiorno, M.F.[Maria F.], La Russa, M.F.[Mauro F.],
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Shi, L.F.[Ling-Fei], Zhang, F.[Feng], Xia, J.[Junshi], Xie, J.[Jibo], Zhang, Z.[Zhe], Du, Z.H.[Zhen-Hong], Liu, R.Y.[Ren-Yi],
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Lin, C.[Chen], Li, Y.[Yundong], Liu, Y.[Yi], Wang, X.[Xiang], Geng, S.[Shuo],
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Feature extraction, Buildings, Generative adversarial networks, Training, Deep learning, Hurricanes, generative adversarial network (GAN) BibRef

Lin, Q.G.[Qi-Gen], Ci, T.Y.[Tian-Yu], Wang, L.B.[Lei-Bin], Mondal, S.K.[Sanjit Kumar], Yin, H.X.[Hua-Xiang], Wang, Y.[Ying],
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Jing, Y.[Yafei], Ren, Y.[Yuhuan], Liu, Y.[Yalan], Wang, D.C.[Da-Cheng], Yu, L.J.[Lin-Jun],
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Zhan, Y.H.[Yi-Hao], Liu, W.[Wen], Maruyama, Y.[Yoshihisa],
Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake,
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Wang, Y.[Yu], Cui, L.[Liangyi], Zhang, C.[Chenzong], Chen, W.L.[Wen-Li], Xu, Y.[Yang], Zhang, Q.Q.[Qiang-Qiang],
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Wang, C.[Chao], Zhang, Y.[Yan], Xie, T.[Tao], Guo, L.[Lin], Chen, S.S.[Shi-Shi], Li, J.Y.[Jun-Yong], Shi, F.[Fan],
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RS(14), No. 5, 2022, pp. xx-yy.
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Paris, L.[Leonardo], Rossi, M.L.[Maria Laura], Cipriani, G.[Giorgia],
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Rapid Assessment of Building Damage Using Multi-Source Data: A Case Study of April 2015 Nepal Earthquake,
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Seydi, S.T.[Seyd Teymoor], Rastiveis, H.[Heidar], Kalantar, B.[Bahareh], Halin, A.A.[Alfian Abdul], Ueda, N.[Naonori],
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Jang, A.[Arum], Ju, Y.K.[Young K.], Park, M.J.[Min Jae],
Structural Stability Evaluation of Existing Buildings by Reverse Engineering with 3D Laser Scanner,
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Bouchard, I.[Isabelle], Rancourt, M.È.[Marie-Ève], Aloise, D.[Daniel], Kalaitzis, F.[Freddie],
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Hake, F.[Frederic], Göttert, L.[Leonard], Neumann, I.[Ingo], Alkhatib, H.[Hamza],
Using Machine-Learning for the Damage Detection of Harbour Structures,
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Yang, J.[Jian], Tang, W.M.[Wei-Ming], Xuan, W.[Wei], Xi, R.J.[Rui-Jie],
Tight Integration of GNSS and Static Level for High Accuracy Dilapidated House Deformation Monitoring,
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Sun, X.L.[Xiao-Lin], Chen, X.[Xi], Yang, L.[Liao], Wang, W.S.[Wei-Sheng], Zhou, X.X.[Xi-Xuan], Wang, L.[Lili], Yao, Y.[Yuan],
Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake,
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Zhang, H.M.[Hai-Ming], Wang, M.C.[Ming-Chang], Zhang, Y.X.[Yong-Xian], Ma, G.R.[Guo-Rui],
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Xia, L.G.[Lie-Gang], Chen, J.[Jun], Luo, J.C.[Jian-Cheng], Zhang, J.X.[Jun-Xia], Yang, D.Z.[De-Zhi], Shen, Z.F.[Zhan-Feng],
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Yamazaki, F.[Fumio], Liu, W.[Wen], Horie, K.[Kei],
Use of Multi-Temporal LiDAR Data to Extract Collapsed Buildings and to Monitor Their Removal Process after the 2016 Kumamoto Earthquake,
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Yu, Z.B.[Zheng-Bo], Chen, Z.[Zhe], Sun, Z.C.[Zhong-Chang], Guo, H.D.[Hua-Dong], Leng, B.[Bo], He, Z.Q.[Zi-Qiong], Yang, J.[Jinpei], Xing, S.W.[Shu-Wen],
SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Aimaiti, Y.[Yusupujiang], Sanon, C.[Christina], Koch, M.[Magaly], Baise, L.G.[Laurie G.], Moaveni, B.[Babak],
War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images,
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Takhtkeshha, N.[Narges], Mohammadzadeh, A.[Ali], Salehi, B.[Bahram],
A Rapid Self-Supervised Deep-Learning-Based Method for Post-Earthquake Damage Detection Using UAV Data (Case Study: Sarpol-e Zahab, Iran),
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Ge, J.Y.[Jia-Yi], Tang, H.[Hong], Yang, N.[Naisen], Hu, Y.J.[Yi-Jiang],
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Elsevier DOI 2301
Building damage, Remote sensing, Incremental learning, Style transfer, Disaster response BibRef

He, Y.J.[Yong-Jun], Wang, J.F.[Jin-Fei], Liao, C.H.[Chun-Hua], Zhou, X.[Xin], Shan, B.[Bo],
MS4D-Net: Multitask-Based Semi-Supervised Semantic Segmentation Framework with Perturbed Dual Mean Teachers for Building Damage Assessment from High-Resolution Remote Sensing Imagery,
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Zuo, H.[Heng], Guo, H.[Huiyong],
Structural Nonlinear Damage Identification Method Based on the Kullback-Leibler Distance of Time Domain Model Residuals,
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Cho, S.[Shinki], Xiu, H.[Haoyi], Matsuoka, M.[Masashi],
Backscattering Characteristics of SAR Images in Damaged Buildings Due to the 2016 Kumamoto Earthquake,
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Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging,
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Huang, Q.H.[Qi-Hao], Jin, G.[Guowang], Xiong, X.[Xin], Ye, H.[Hao], Xie, Y.Z.[Yu-Zhi],
Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR,
RS(15), No. 12, 2023, pp. xx-yy.
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Ge, J.Y.[Jia-Yi], Tang, H.[Hong], Ji, C.[Chao],
Self-Incremental Learning for Rapid Identification of Collapsed Buildings Triggered by Natural Disasters,
RS(15), No. 15, 2023, pp. xx-yy.
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Anticipating the Collapse of Urban Infrastructure: A Methodology Based on Earth Observation and MT-InSAR,
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Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification,
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Ahmadi, S.A.[Seyed Ali], Mohammadzadeh, A.[Ali], Yokoya, N.[Naoto], Ghorbanian, A.[Arsalan],
BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images,
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Aydin, N.[Nezir], Yilmaz, O.[Oktay], Deveci, M.[Muhammet], Lv, Z.H.[Zhi-Han],
Heuristics Based Optimization for Multidepot Drone Location and Routing Problem to Detect Post-Earthquake Damages,
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IEEE DOI 2402
Drones, Earthquakes, Buildings, Routing, Mathematical models, Path planning, Image resolution, drones BibRef

Liu, H.[Hui], Yuan, M.Z.[Ming-Ze], Li, M.[Mei], Li, B.[Ben], Chen, N.[Ning], Wang, J.Z.[Jin-Zheng], Li, X.[Xu], Wu, X.[Xiaohu],
TDFPI: A Three-Dimensional and Full Parameter Inversion Model and Its Application for Building Damage Assessment in Guotun Coal Mining Areas, Shandong, China,
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Zhu, L.[Linye], Sun, W.B.[Wen-Bin], Fan, D.Q.[De-Qin], Xing, H.Q.[Hua-Qiao], Liu, X.Q.[Xiao-Qi],
Unsupervised spatial self-similarity difference-based change detection method for multi-source heterogeneous images,
PR(149), 2024, pp. 110237.
Elsevier DOI 2403
Heterogeneous images, multi-source, change detection, unsupervised method BibRef

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The Change You Want to See (Now in 3D),
OpenSUN3D(2052-2061)
IEEE DOI Code:
WWW Link. 2401
Different camera positions, different times. BibRef

Tu, Y.B.[Yun-Bin], Li, L.[Liang], Su, L.[Li], Zha, Z.J.[Zheng-Jun], Yan, C.G.[Cheng-Gang], Huang, Q.M.[Qing-Ming],
Self-supervised Cross-view Representation Reconstruction for Change Captioning,
ICCV23(2793-2803)
IEEE DOI Code:
WWW Link. 2401
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