24.2.2.1 Site Model Change Detection, Map Update

Chapter Contents (Back)
Remote Sensing. Registration. Site Model. Change Detection. Map Update. Aerial Image Analysis.
See also Change Detection -- Image Level.
See also Building Change Detection.

Ridd, M.K., Liu, J.J.,
A Comparison of Four Algorithms for Change Detection in an Urban Environment,
RSE(63), No. 2, February 1998, pp. 95-100. 9801
BibRef

Sarkar, S.[Sudeep], Boyer, K.L.[Kim L.],
Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors,
CVIU(71), No. 1, July 1998, pp. 110-136.
DOI Link BibRef 9807
Earlier: CVPR96(478-483).
IEEE DOI Change Detection. Analysis of construction sites. BibRef

Metternicht, G.[Graciela],
Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision,
PandRS(54), No. 4, September 1999, pp. 221-233. 9911
BibRef

Huertas, A.[Andres], and Nevatia, R.[Ramakant],
Detecting Changes in Aerial Views of Man-Made Structures,
IVC(18), No. 8, 15 May 2000, pp. 583-596.
Elsevier DOI 0003
BibRef USC Computer Vision BibRef
Earlier: ICCV98(73-80).
IEEE DOI
PDF File. BibRef
And: Radius97(319-334). BibRef
And: ARPA96(381-388). Change Detection. BibRef

Bejanin, M., Huertas, A., Medioni, G., and Nevatia, R.,
Model Validation for Change Detection,
WACV94(160-167).
IEEE Abstract. BibRef 9400 USC Computer Vision BibRef
And: ARPA94(I:287-294). BibRef USC Computer Vision Change Detection. BibRef

Huertas, A., Bejanin, M., and Nevatia, R.,
Model Registration and Validation,
Ascona95(33-42). BibRef 9500 USC Computer Vision BibRef

Chellappa, R., Burlina, P., Lin, C.L., Zhang, X., Davis, L.S., Rosenfeld, A.,
Site Model Based Image Registration and Change Detection,
UMD--Radius, June 1999.
PS File. BibRef 9906

Chellappa, R., Zheng, Q., Davis, L.S., DeMenthon, D.F., and Rosenfeld, A.,
Site-Model-Based Change Detection and Image Registration,
DARPA93(205-216). Change Detection, Differencing. Register the image by warping it and substract the image. BibRef 9300

Chellappa, R., Zhang, X.P.[Xiao-Peng], Philippe, B.,
Automatic Image-to-Site Model Registration,
ICASSP96(XX) Ctr. for Automation Rsch. University of Maryland. BibRef 9603

Smits, P.C., Myers, W.L.,
Echelon Approach to Characterize and Understand Spatial Structures of Change in Multitemporal Remote Sensing Imagery,
GeoRS(38), No. 5, September 2000, pp. 2299-2309.
IEEE Top Reference. 0010
BibRef

Agouris, P.[Peggy], Beard, K.[Kate], Mountrakis, G.[Georgios], Stefanidis, A.[Anthony],
Capturing and Modeling Geographic Object Change: A SpatioTemporal Gazetteer Framework,
PandRS(55), No. 10, October 2000, pp. 1241-1250. The framework links an image repository with changes, to instances of geographic entities. 0010
BibRef

Hazel, G.G.,
Object-level change detection in spectral imagery,
GeoRS(39), No. 3, March 2001, pp. 553-561.
IEEE Top Reference. 0104
BibRef

Yamamoto, T., Hanaizumi, H., Chino, S.,
A change detection method for remotely sensed multispectral and multitemporal images using 3-D segmentation,
GeoRS(39), No. 5, May 2001, pp. 976-985.
IEEE Top Reference. 0106
BibRef

Bruzzone, L., Fernandez-Prieto, D.,
An adaptive semiparametric and context-based approach to unsupervised change detection multitemporal remote-sensing images,
IP(11), No. 4, April 2002, pp. 452-466.
IEEE DOI 0205

See also iterative approach to partially supervised classification problems, An.
See also minimum-cost thresholding technique for unsupervised change detection, A. BibRef

Bruzzone, L., Fernandez-Prieto, D.,
An adaptive parcel-based technique for unsupervised change detection,
JRS(21), No. 4, March 2000, pp. 817. Uses the neighborhood to reduce noise. 0002
BibRef

Bruzzone, L., Fernández-Prieto, D.[Diego],
Automatic Analysis of the Difference Image for Unsupervised Change Detection,
GeoRS(38), No. 3, May 2000, pp. 1171-1182.
IEEE Top Reference. 0006
BibRef

Bruzzone, L.[Lorenzo], Fernández-Prieto, D.[Diego],
A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images,
PRL(23), No. 9, July 2002, pp. 1063-1071.
Elsevier DOI 0205
BibRef

Liu, X., Lathrop, Jr., R.G.,
Urban change detection based on an artificial neural network,
JRS(23), No. 12, June 2002, pp. 2513-2518. 0208
BibRef

Leclerc, Y.G.[Yvan G.], Luong, Q.T.[Q. Tuan], Fua, P.,
Self-Consistency and MDL: A Paradigm for Evaluating Point-Correspondence Algorithms, and Its Application to Detecting Changes in Surface Elevation,
IJCV(51), No. 1, January 2003, pp. 63-83.
DOI Link 0211
BibRef

Leclerc, Y.G., Luong, Q.T., and Fua, P.V.,
A framework for detecting changes in terrain,
DARPA98(621-630).
PDF File. BibRef 9800

Leclerc, Y.G.[Yvan G.], Luong, Q.T.[Q. Tuan], Fua, P.V.[Pascal V.], Miyajima, K.[Koji],
Detecting Changes in 3-D Shape using Self-Consistency,
CVPR00(I: 395-402).
IEEE DOI
PDF File. 0005
Applies to DEM representation BibRef

Leclerc, Y.G.[Yvan G.],
Continuous Terrain Modeling from Image Sequences with Applications to Change Detection,
DARPA97(431-436).
PDF File. BibRef 9700

Couteron, P.[Pierre],
Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs,
JRS(23), No. 17, September 2002, pp. 3407-3425.
WWW Link. 0211
BibRef

Seto, K.C.[Karen C.], Liu, W.G.[Wei-Guo],
Comparing ARTMAP Neural Network with the Maximum-Likelihood Classifier for Detecting Urban Change,
PhEngRS(69), No. 9, September 2003, pp. 981-990. An ARTMAP neural network was used to identify urban land-use change with different class resolutions; it generated more accurate results when compared to a Bayesian maximum-likelihood classifier.
WWW Link. 0309
BibRef

Knudsen, T.[Thomas], Olsen, B.P.[Brian P.],
Automated Change Detection for Updates of Digital Map Databases,
PhEngRS(69), No. 11, November 2003, pp. 1289-1298. The change detection algorithm uses vector and spectral data as input to an unsupervised spectral classification method which controls a subsequent Mahalanobis classification step.
WWW Link. 0401
BibRef

Peerbocus, M.A., Bauzer Medeiros, C., Jomier, G., Voisard, A.,
A System for Change Documentation Based on a Spatiotemporal Database,
GeoInfo(8), No. 2, June 2004, pp. 173-204.
DOI Link 0403
BibRef

Walter, V.[Volker],
Object-based classification of remote sensing data for change detection,
PandRS(58), No. 3-4, January 2004, pp. 225-238.
Elsevier DOI 0411
BibRef

Lacroix, V., Idrissa, M., Hincq, A., Bruynseels, H., Swartenbroekx, O.,
Detecting urbanization changes using SPOT5,
PRL(27), No. 4, March 2006, pp. 226-233.
Elsevier DOI 0602
Cartography; Change detection; SPOT5; Built-up area detection BibRef

Lambin, E.F., Linderman, M.,
Time Series of Remote Sensing Data for Land Change Science,
GeoRS(44), No. 7, Part 1, July 2006, pp. 1926-1928.
IEEE DOI 0606
BibRef

Gamba, P., Dell'Acqua, F., Lisini, G.,
Change Detection of Multitemporal SAR Data in Urban Areas Combining Feature-Based and Pixel-Based Techniques,
GeoRS(44), No. 10, October 2006, pp. 2820-2827.
IEEE DOI 0609
BibRef

Dell'Acqua, F., Gamba, P., Lisini, G.,
A Semi-Automatic High Resolution SAR Data Interpretation Procedure,
PIA07(19).
PDF File. 0711
BibRef

Holland, D.A., Boyd, D.S., Marshall, P.,
Updating topographic mapping in Great Britain using imagery from high-resolution satellite sensors,
PandRS(60), No. 3, May 2006, pp. 212-223.
Elsevier DOI 0610
cartography; change detection; land cover; IKONOS; QuickBird BibRef

Molinier, M.[Matthieu], Laaksonen, J.T.[Jorma T.], Hame, T.[Tuomas],
Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps,
GeoRS(45), No. 4, April 2007, pp. 861-874.
IEEE DOI 0704

See also PicSOM: Content-Based Image Retrieval with Self-Organizing Maps. BibRef

Chini, M., Pacifici, F., Emery, W.J., Pierdicca, N., del Frate, F.,
Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats,
GeoRS(46), No. 6, June 2008, pp. 1812-1821.
IEEE DOI 0711

See also Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines. BibRef

Pacifici, F.[Fabio], Emery, W.J.[William J.],
Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution,
CIARP09(929-942).
Springer DOI 0911

See also Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines. BibRef

Camps-Valls, G.[Gustavo], Gomez-Chova, L., Munoz-Mari, J., Rojo-Alvarez, J.L., Martinez-Ramon, M.,
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection,
GeoRS(46), No. 6, June 2008, pp. 1822-1835.
IEEE DOI 0711
BibRef

Tuia, D.[Devis], Marcos, D.[Diego], Camps-Valls, G.[Gustau],
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization,
PandRS(120), No. 1, 2016, pp. 1-12.
Elsevier DOI 1610
Feature extraction BibRef

Leiva-Murillo, J.M., Gomez-Chova, L., Camps-Valls, G.,
Multitask Remote Sensing Data Classification,
GeoRS(51), No. 1, January 2013, pp. 151-161.
IEEE DOI 1301
BibRef

Potere, D.[David], Feierabend, N.[Neal], Strahler, A.H.[Alan H.], Bright, E.E.[Eddie E.],
Wal-mart from Space: A New Source for Land Cover Change Validation,
PhEngRS(74), No. 7, July 2008, pp. 913-920.
WWW Link. 0804
Using a set of Wal-Mart store positions and opening dates to validate portions of three land-cover change-related products: a forest disturbance map based on Landsat GeoCover imagery and two MODIS vegetation index time series. BibRef

Wilkinson, D.W., Parker, R.C., Evans, D.L.,
Change Detection Techniques for Use in a Statewide Forest Inventory Program,
PhEngRS(74), No. 7, July 2008, pp. 893-902.
WWW Link. 0804
Analysis of modifi ed Change Vector Analysis (mCVA) and Simultaneous Image Differencing (SID) techniques for largescale forest change in Mississippi. BibRef

Liao, M.S.[Ming-Sheng], Jiang, L.M.[Li-Ming], Lin, H.[Hui], Huang, B.[Bo], Gong, J.Y.[Jian-Ya],
Urban Change Detection Based on Coherence and Intensity Characteristics of Sar Imagery,
PhEngRS(74), No. 8, August 2008, pp. 999-1066.
WWW Link. 0804
An unsupervised approach combining coherence and intensity characteristics of SAR imagery to detect and map landcover changes in an urban area. BibRef

Durieux, L.[Laurent], Lagabrielle, E.[Erwann], Nelson, A.[Andrew],
A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data,
PandRS(63), No. 4, July 2008, pp. 399-408.
Elsevier DOI 0804
Spot 5; Reunion Island; Integrated urban sprawl management; Object-based image analysis BibRef

Bouziani, M.[Mourad], Goita, K.[Kalifa], He, D.C.[Dong-Chen],
Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge,
PandRS(65), No. 1, January 2010, pp. 143-153.
Elsevier DOI 1001
Change detection; Urban; QuickBird; Ikonos; Knowledge base BibRef

Li, X.[Xia], Yeh, A.G.O.[Anthony Gar-On], Qian, J.P.[Jun-Ping], Ai, B.[Bin], Qi, Z.X.[Zhi-Xin],
A Matching Algorithm for Detecting Land Use Changes Using Case-Based Reasoning,
PhEngRS(75), No. 11, November 2009, pp. 1319-1333.
WWW Link. 1001
A matching algorithm to identify the temporal positions and the kind of changes by integrating object-oriented analysis and case-based reasoning for Multi-temporal SAR Images. BibRef

Lu, D.S.[Deng-Sheng], Moran, E.[Emilio], Hetrick, S.[Scott],
Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier,
PandRS(66), No. 3, May 2011, pp. 298-306.
Elsevier DOI 1103
Impervious surfaces; Urban-rural frontier; Landsat; QuickBird; Regression analysis BibRef

Jiang, J.X.[Ji-Xiang], Worboys, M.[Michael], Nittel, S.[Silvia],
Qualitative change detection using sensor networks based on connectivity information,
GeoInfo(15), No. 2, April 2011, pp. 305-328.
WWW Link. 1103
BibRef

Crispell, D., Mundy, J., Taubin, G.,
A Variable-Resolution Probabilistic Three-Dimensional Model for Change Detection,
GeoRS(50), No. 2, February 2012, pp. 489-500.
IEEE DOI 1201
BibRef

Hebel, M.[Marcus], Stilla, U.[Uwe],
Simultaneous Calibration of ALS Systems and Alignment of Multiview LiDAR Scans of Urban Areas,
GeoRS(50), No. 6, June 2012, pp. 2364-2379.
IEEE DOI 1205
BibRef

Hebel, M.[Marcus], Arens, M.[Michael], Stilla, U.[Uwe],
Change Detection in Urban Areas by Direct Comparison of Multi-view and Multi-temporal ALS Data,
PIA11(185-196).
Springer DOI 1110
BibRef

Chaabouni-Chouayakh, H.[Houda], Reinartz, P.[Peter],
Towards Automatic 3D Change Detection inside Urban Areas by Combining Height and Shape Information,
PFG(2011), No. 4, 2011, pp. 205-217.
WWW Link. 1211
BibRef

Chaabouni-Chouayakh, H.[Houda], Krauss, T.[Thomas], d'Angelo, P.[Pablo], Reinartz, P.[Peter],
3D Change Detection inside Urban Areas using different Digital Surface Models,
PCVIA10(B:86).
PDF File. 1009
BibRef

Listner, C.[Clemens], Niemeyer, I.[Irmgard],
Object-based Change Detection,
PFG(2011), No. 4, 2011, pp. 233-245.
WWW Link. 1211
BibRef

Huh, Y.[Yong], Yang, S.C.[Sung-Chul], Ga, C.[Chillo], Yu, K.[Kiyun], Shi, W.Z.[Wen-Zhong],
Line segment confidence region-based string matching method for map conflation,
PandRS(78), No. 1, April 2013, pp. 69-84.
Elsevier DOI 1304
Map conflation; Spatial uncertainty; Confidence region of a line segment; String matching; Corresponding point pair BibRef

Hussain, M.[Masroor], Chen, D.M.[Dong-Mei], Cheng, A.[Angela], Wei, H.[Hui], Stanley, D.[David],
Change detection from remotely sensed images: From pixel-based to object-based approaches,
PandRS(80), No. 1, June 2013, pp. 91-106.
Elsevier DOI 1305
Remote sensing; Change detection; Pixel-based; Object-based; Spatial-data-mining BibRef

Hwang, J.S.[Jin-Sang], Yun, H.S.[Hong-Sik], Jeong, T.J.[Tae-Jun], Suh, Y.[Yong_Cheol], Huang, H.[He],
Frequent Unscheduled Updates of the National Base Map Using the Land-Based Mobile Mapping System,
RS(5), No. 5, 2013, pp. 2513-2533.
DOI Link 1307
BibRef

Lubitz, C.[Christin], Motagh, M.[Mahdi], Wetzel, H.U.[Hans-Ulrich], Kaufmann, H.[Hermann],
Remarkable Urban Uplift in Staufen im Breisgau, Germany: Observations from TerraSAR-X InSAR and Leveling from 2008 to 2011,
RS(5), No. 6, 2013, pp. 3082-3100.
DOI Link 1307
BibRef

Touya, G.[Guillaume], Coupé, A.[Adeline], Le Jollec, J.[Jérémie], Dorie, O.[Olivier], Fuchs, F.[Frank],
Conflation Optimized by Least Squares to Maintain Geographic Shapes,
IJGI(2), No. 3, 2013, pp. 621-644.
DOI Link 1307
BibRef

Li, H.F.[Hai-Feng], Wu, B.[Bo],
Adaptive geo-information processing service evolution: Reuse and local modification method,
PandRS(83), No. 1, 2013, pp. 165-183.
Elsevier DOI 1308
Geography information services BibRef

Jaud, M.[Marion], Rouveure, R.[Raphaël], Faure, P.[Patrice], Monod, M.O.[Marie-Odile],
Methods for FMCW radar map georeferencing,
PandRS(84), No. 0, 2013, pp. 33-42.
Elsevier DOI 1309
Radar mapping
See also Method for orthorectification of terrestrial radar maps. BibRef

Rössmann, H.[Heiner], Peyker, J.[Joachim], Völker, A.[Andreas], Klink, A.[Adrian],
Einsatz von Change-Detection-Methoden bei der Fortführung von Versiegelungs- und Gebäudedatenbeständen,
PFG(2013), No. 5, 2013, pp. 447-458.
DOI Link 1310
BibRef

Wang, J.H.[Jin-Hu], González-Jorge, H.[Higinio], Lindenbergh, R.[Roderik], Arias-Sánchez, P.[Pedro], Menenti, M.[Massimo],
Automatic Estimation of Excavation Volume from Laser Mobile Mapping Data for Mountain Road Widening,
RS(5), No. 9, 2013, pp. 4629-4651.
DOI Link 1310
BibRef

Paris, P.[Paul], Mitasova, H.[Helena],
Barrier Island Dynamics Using Mass Center Analysis: A New Way to Detect and Track Large-Scale Change,
IJGI(3), No. 1, 2014, pp. 49-65.
DOI Link 1402
BibRef

Vassilakis, E.[Emmanuel], Papadopoulou-Vrynioti, K.[Kyriaki],
Quantification of Deltaic Coastal Zone Change Based on Multi-Temporal High Resolution Earth Observation Techniques,
IJGI(3), No. 1, 2014, pp. 18-28.
DOI Link 1402
BibRef

Wentz, E.A.[Elizabeth A.], Anderson, S.[Sharolyn], Fragkias, M.[Michail], Netzband, M.[Maik], Mesev, V.[Victor], Myint, S.W.[Soe W.], Quattrochi, D.[Dale], Rahman, A.[Atiqur], Seto, K.C.[Karen C.],
Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing,
RS(6), No. 5, 2014, pp. 3879-3905.
DOI Link 1407
Not really the detection of climate change. BibRef

Qin, R.J.[Rong-Jun],
An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images,
RS(6), No. 9, 2014, pp. 7911-7932.
DOI Link 1410
BibRef

Fraser, R.H.[Robert H.], Olthof, I.[Ian], Kokelj, S.V.[Steven V.], Lantz, T.C.[Trevor C.], Lacelle, D.[Denis], Brooker, A.[Alexander], Wolfe, S.[Stephen], Schwarz, S.[Steve],
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization,
RS(6), No. 11, 2014, pp. 11533-11557.
DOI Link 1412
BibRef

Olthof, I.[Ian], Fraser, R.H.[Robert H.],
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification,
RS(6), No. 11, 2014, pp. 11558-11578.
DOI Link 1412
BibRef

Ahmed, M.[Mahmuda], Karagiorgou, S.[Sophia], Pfoser, D.[Dieter], Wenk, C.[Carola],
A comparison and evaluation of map construction algorithms using vehicle tracking data,
GeoInfo(19), No. 3, July 2015, pp. 601-632.
WWW Link. 1505
Not from images, from tracking vehicles by other means. BibRef

Maurer, J.[Joshua], Rupper, S.[Summer],
Tapping into the Hexagon spy imagery database: A new automated pipeline for geomorphic change detection,
PandRS(108), No. 1, 2015, pp. 113-127.
Elsevier DOI 1511
Stereo imagery BibRef

Dorn, H.[Helen], Törnros, T.[Tobias], Zipf, A.[Alexander],
Quality Evaluation of VGI Using Authoritative Data: A Comparison with Land Use Data in Southern Germany,
IJGI(4), No. 3, 2015, pp. 1657.
DOI Link 1511
BibRef

Wen, D.W.[Da-Wei], Huang, X.[Xin], Zhang, L.P.[Liang-Pei], Benediktsson, J.A.,
A Novel Automatic Change Detection Method for Urban High-Resolution Remotely Sensed Imagery Based on Multi-Index Scene Representation,
GeoRS(54), No. 1, January 2016, pp. 609-625.
IEEE DOI 1601
feature extraction BibRef

Zhang, P.Z.[Pu-Zhao], Gong, M.G.[Mao-Guo], Su, L.Z.[Lin-Zhi], Liu, J.[Jia], Li, Z.Z.[Zhi-Zhou],
Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images,
PandRS(116), No. 1, 2016, pp. 24-41.
Elsevier DOI 1604
Change detection BibRef

Zhan, T.[Tao], Gong, M.G.[Mao-Guo], Liu, J.[Jia], Zhang, P.Z.[Pu-Zhao],
Iterative feature mapping network for detecting multiple changes in multi-source remote sensing images,
PandRS(146), 2018, pp. 38-51.
Elsevier DOI 1812
Change detection, Iterative feature mapping network, Hierarchical clustering analysis, Multiple changes, Multi-source images BibRef

Gong, M.[Maoguo], Zhan, T.[Tao], Zhang, P.Z.[Pu-Zhao], Miao, Q.G.[Qi-Guang],
Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images,
GeoRS(55), No. 5, May 2017, pp. 2658-2673.
IEEE DOI 1705
feature extraction, geophysical image processing, land cover, neural nets, remote sensing, bitemporal multispectral, change detection, change feature extraction, hierarchical difference representation learning, high resolution remotely sensed imagery, land cover transition, multispectral remote sensing images, neural networks, preclassification map, satellite sensors, semantic difference, superpixel based difference representation learning, Feature extraction, Image analysis, Image resolution, Image segmentation, Neural networks, Remote sensing, Robustness, Change detection, difference representation learning, multispectral images, neural network, superpixel, segmentation BibRef

Zhang, P.Z.[Pu-Zhao], Lv, Z., Zhang, D., Chen, J.,
A Shape Similarity Based Change Detection Approach of Multi-resolution Remote Sensing Images,
AnnalsPRS(I-7), No. 2012, pp. 263-266.
DOI Link 1209
BibRef

Zhang, X.C.[Xin-Chang], Guo, T.S.[Tai-Sheng], Huang, J.F.[Jian-Feng], Xin, Q.C.[Qin-Chuan],
Propagating Updates of Residential Areas in Multi-Representation Databases Using Constrained Delaunay Triangulations,
IJGI(5), No. 6, 2016, pp. 80.
DOI Link 1608
BibRef

Chen, Q.A.[Qi-Ang], Chen, Y.H.[Yun-Hao],
Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis,
RS(8), No. 7, 2016, pp. 549.
DOI Link 1608
BibRef

Kaiser, P., Wegner, J.D., Lucchi, A., Jaggi, M., Hofmann, T., Schindler, K.,
Learning Aerial Image Segmentation From Online Maps,
GeoRS(55), No. 11, November 2017, pp. 6054-6068.
IEEE DOI 1711
Image segmentation, Manuals, Roads, Semantics, Training, Training data, Urban areas, Crowdsourcing, image classification, machine learning, neural networks, supervised learning, terrain mapping, urban, areas BibRef

Lu, C.H.[Chih-Heng], Ni, C.F.[Chuen-Fa], Chang, C.P.[Chung-Pai], Yen, J.Y.[Jiun-Yee], Chuang, R.Y.[Ray Y.],
Coherence Difference Analysis of Sentinel-1 SAR Interferogram to Identify Earthquake-Induced Disasters in Urban Areas,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Gstaiger, V.[Veronika], Tian, J.J.[Jiao-Jiao], Kiefl, R.[Ralph], Kurz, F.[Franz],
2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Azimi, S.M., Kiefl, R.[Ralph], Gstaiger, V.[Veronika], Bahmanyar, R., Merkle, N., Henry, C., Rosenbaum, D., Kurz, F.[Franz],
Automatic Object Segmentation to Support Crisis Management Of Large-scale Events,
ISPRS21(B2-2021: 433-440).
DOI Link 2201
BibRef

Maalek, R.[Reza], Lichti, D.D.[Derek D.], Ruwanpura, J.Y.[Janaka Y.],
Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
Construction monitoring. BibRef

Tucci, G.[Grazia], Gebbia, A.[Antonio], Conti, A.[Alessandro], Fiorini, L.[Lidia], Lubello, C.[Claudio],
Monitoring and Computation of the Volumes of Stockpiles of Bulk Material by Means of UAV Photogrammetric Surveying,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Iwai, Y.[Yuki], Murayama, Y.J.[Yu-Ji],
Geographical Analysis on the Projection and Distortion of INO's Tokyo Map in 1817,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Rozsa, Z.[Zoltan], Golarits, M.[Marcell], Sziranyi, T.[Tamas],
Localization of Map Changes by Exploiting SLAM Residuals,
ACIVS20(312-324).
Springer DOI 2003
BibRef

Chen, Q.[Qiang], Cheng, Q.H.[Qian-Hao], Wang, J.F.[Jin-Fei], Du, M.Y.[Ming-Yi], Zhou, L.[Lei], Liu, Y.[Yang],
Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Koteich, B.[Bilal], Saux, É.[Éric], Laddada, W.[Wissame],
Knowledge-Based Recommendation for On-Demand Mapping: Application to Nautical Charts,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Manish, R.[Raja], Hasheminasab, S.M.[Seyyed Meghdad], Liu, J.[Jidong], Koshan, Y.[Yerassyl], Mahlberg, J.A.[Justin Anthony], Lin, Y.C.[Yi-Chun], Ravi, R.[Radhika], Zhou, T.[Tian], McGuffey, J.[Jeremy], Wells, T.[Timothy], Bullock, D.[Darcy], Habib, A.[Ayman],
Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Jiang, H.W.[Hui-Wei], Peng, M.[Min], Zhong, Y.J.[Yuan-Jun], Xie, H.F.[Hao-Feng], Hao, Z.[Zemin], Lin, J.M.[Jing-Ming], Ma, X.L.[Xiao-Li], Hu, X.Y.[Xiang-Yun],
A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Li, P.L.[Peng-Long], Hu, X.Y.[Xiang-Yun], Hu, Y.[Yan], Ding, Y.[Yi], Wang, L.[Lan], Li, L.[Li],
A Detection Method of Artificial Area From High Resolution Remote Sensing Images Based On Multi Scale And Multi Feature Fusion,
Hannover17(387-392).
DOI Link 1805
Straight lines. Large areas (urban area) and single house in countryside. BibRef

Lv, Y.[Ye], Wang, G.F.[Guo-Feng], Hu, X.Y.[Xiang-Yun],
Machine Learning Based Road Detection from High Resolution Imagery,
ISPRS16(B3: 891-898).
DOI Link 1610
BibRef

Fyleris, T.[Tautvydas], Krišciunas, A.[Andrius], Gružauskas, V.[Valentas], Calneryte, D.[Dalia], Barauskas, R.[Rimantas],
Urban Change Detection from Aerial Images Using Convolutional Neural Networks and Transfer Learning,
IJGI(11), No. 4, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Lei, T.L.[Ting L.], Lei, Z.[Zhen],
Harmonizing Full and Partial Matching in Geospatial Conflation: A Unified Optimization Model,
IJGI(11), No. 7, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Berrio, J.S.[Julie Stephany], Worrall, S.[Stewart], Shan, M.[Mao], Nebot, E.[Eduardo],
Long-Term Map Maintenance Pipeline for Autonomous Vehicles,
ITS(23), No. 8, August 2022, pp. 10427-10440.
IEEE DOI 2208
Feature extraction, Pipelines, Maintenance engineering, Transient analysis, Visualization, Autonomous vehicles, map update BibRef

Yu, Q.Y.[Qing-Ying], Hu, F.[Fan], Ye, Z.[Zhen], Chen, C.M.[Chuan-Ming], Sun, L.P.[Li-Ping], Luo, Y.L.[Yong-Long],
High-Frequency Trajectory Map Matching Algorithm Based on Road Network Topology,
ITS(23), No. 10, October 2022, pp. 17530-17545.
IEEE DOI 2210
Trajectory, Roads, Hidden Markov models, Network topology, Global Positioning System, Topology, Clustering algorithms, vehicle trajectory BibRef

Qin, J.X.[Jian-Xin], Yang, W.J.[Wen-Jie], Wu, T.[Tao], He, B.[Bin], Xiang, L.G.[Long-Gang],
Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery,
IJGI(11), No. 10, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Niu, Y.T.[Yi-Ting], Guo, H.T.[Hai-Tao], Lu, J.[Jun], Ding, L.[Lei], Yu, D.H.[Dong-Hang],
SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef
And: Correction: RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Liang, H.[Han], Cho, J.[Jongyoung], Seo, S.Y.[Su-Young],
Construction Site Multi-Category Target Detection System Based on UAV Low-Altitude Remote Sensing,
RS(15), No. 6, 2023, pp. 1560.
DOI Link 2304
BibRef

Pang, S.Y.[Shi-Yan], Li, X.Y.[Xin-Yu], Chen, J.[Jia], Zuo, Z.[Zhiqi], Hu, X.Y.[Xiang-Yun],
Prior Semantic Information Guided Change Detection Method for Bi-temporal High-Resolution Remote Sensing Images,
RS(15), No. 6, 2023, pp. 1655.
DOI Link 2304
BibRef

Chen, H.[Hongruixuan], Yokoya, N.[Naoto], Chini, M.[Marco],
Fourier domain structural relationship analysis for unsupervised multimodal change detection,
PandRS(198), 2023, pp. 99-114.
Elsevier DOI 2304
Change detection, Multimodal remote sensing images, Fourier domain, Structural relationship, Graph spectral convolution BibRef

Timár, G.[Gábor],
Possible Projection of the First Military Survey of the Habsburg Empire in Lower Austria and Hungary (Late 18th Century): An Improvement in Fitting Historical Topographic Maps to Modern Cartographic Systems,
IJGI(12), No. 6, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Wijaya, B.[Benny], Yang, M.M.[Meng-Meng], Wen, T.[Tuopu], Jiang, K.[Kun], Wang, Y.L.[Yun-Long], Fu, Z.[Zheng], Tang, X.[Xuewei], Sigomo, D.O.[Dennis Octovan], Miao, J.[Jinyu], Yang, D.[Diange],
Multi-Session High-Definition Map-Monitoring System for Map Update,
IJGI(13), No. 1, 2024, pp. 6.
DOI Link 2402
BibRef

Zhang, S.[Songyi], Wang, R.S.[Run-Sheng], Jian, Z.Q.[Zhi-Qiang], Zhan, W.[Wei], Zheng, N.N.[Nan-Ning], Tomizuka, M.[Masayoshi],
Clothoid-Based Reference Path Reconstruction for HD Map Generation,
ITS(25), No. 1, January 2024, pp. 587-601.
IEEE DOI 2402
Planning, Optimization, Shape, Splines (mathematics), Roads, Trajectory, Reconstruction algorithms, Path reconstruction, HD map, clothoid BibRef

Tu, L.[Lilin], Huang, X.[Xin], Li, J.Y.[Jia-Yi], Yang, J.[Jie], Gong, J.Y.[Jian-Ya],
A multi-task learning method for extraction of newly constructed areas based on bi-temporal hyperspectral images,
PandRS(208), 2024, pp. 308-323.
Elsevier DOI Code:
WWW Link. 2402
Unsupervised change detection, Multivariate alternation detection (MAD), Hyperspectral images BibRef


Bernhard, M.[Maximilian], Strauß, N.[Niklas], Schubert, M.[Matthias],
MapFormer: Boosting Change Detection by Using Pre-change Information,
ICCV23(16791-16800)
IEEE DOI Code:
WWW Link. 2401
BibRef

Trzeciak, M.[Maciej], Pluta, K.[Kacper], Fathy, Y.[Yasmin], Alcalde, L.[Lucio], Chee, S.[Stanley], Bromley, A.[Antony], Brilakis, I.[Ioannis], Alliez, P.[Pierre],
ConSLAM: Periodically Collected Real-world Construction Dataset for SLAM and Progress Monitoring,
CVCivil22(317-331).
Springer DOI 2304
BibRef

Xiong, R.X.[Ruo-Xin], Zhu, Y.S.[Yuan-Sheng], Wang, Y.Y.[Yan-Yu], Liu, P.K.[Peng-Kun], Tang, P.B.[Ping-Bo],
Facilitating Construction Scene Understanding Knowledge Sharing and Reuse via Lifelong Site Object Detection,
CVCivil22(228-243).
Springer DOI 2304
BibRef

Corley, I.[Isaac], Najafirad, P.[Peyman],
Supervising Remote Sensing Change Detection Models With 3d Surface Semantics,
ICIP22(3753-3757)
IEEE DOI 2211
Integrated optics, Solid modeling, Buildings, Semantics, Optical imaging, Feature extraction, self-supervised learning, above ground level maps BibRef

Adam, A.[Aikaterini], Sattler, T.[Torsten], Karantzalos, K.[Konstantinos], Pajdla, T.[Tomas],
Objects Can Move: 3D Change Detection by Geometric Transformation Consistency,
ECCV22(XXXIII:108-124).
Springer DOI 2211
BibRef

Bastani, F.[Favyen], Madden, S.[Sam],
Beyond Road Extraction: A Dataset for Map Update using Aerial Images,
ICCV21(11885-11894)
IEEE DOI 2203
Satellites, Roads, Benchmark testing, Trajectory, Topology, Task analysis, Vision applications and systems, BibRef

Ebel, P., Saha, S., Zhu, X.X.,
Fusing Multi-modal Data for Supervised Change Detection,
ISPRS21(B3-2021: 243-249).
DOI Link 2201
BibRef

Dahle, F., Arroyo Ohori, K., Agugiaro, G., Briels, S.,
Automatic Change Detection of Digital Maps Using Aerial Images And Point Clouds,
ISPRS21(B2-2021: 457-464).
DOI Link 2201
BibRef

Morreale, L.[Luca], Aigerman, N.[Noam], Kim, V.[Vladimir], Mitra, N.J.[Niloy J.],
Neural Surface Maps,
CVPR21(4637-4646)
IEEE DOI 2111
Geometry, Solid modeling, Shape, Neural networks, Distortion, Task analysis BibRef

Truong-Hong, L., Lindenbergh, R.C.,
A Framework to Extract Structural Elements of Construction Site From Laser Scanning,
ISPRS20(B2:501-506).
DOI Link 2012
BibRef

Sanchez, E.H.[Eduardo Hugo], Serrurier, M.[Mathieu], Ortner, M.[Mathias],
Learning Disentangled Representations via Mutual Information Estimation,
ECCV20(XXII:205-221).
Springer DOI 2011
What is similar in the 2 images and what is different. BibRef

Revaud, J.[Jerome], Heo, M.[Minhyeok], Rezende, R.S.[Rafael S.], You, C.[Chanmi], Jeong, S.G.[Seong-Gyun],
Did It Change? Learning to Detect Point-Of-Interest Changes for Proactive Map Updates,
CVPR19(4081-4090).
IEEE DOI 2002
BibRef

Lu, Y., Zhang, J., Tong, X., Han, W., Zhao, H.,
Classification Accuracy Assessment for Regional Vector Data Product Based On Spatial Sampling: a Case Study of Japan,
ISSDQ19(1243-1247).
DOI Link 1912
BibRef

Bauman, T., Almog, O., Dalyot, S.,
Towards The Automatic Detection of Geospatial Changes Based On Digital Elevation Models Produced By UAV Imagery,
PIA19(47-53).
DOI Link 1912
BibRef

Gonçalves, J.A., Jordão, N., Pinhal, A.,
Orientation of UAV Image Blocks By Surface Matching,
UAV-g19(317-321).
DOI Link 1912
Align with map for updating. BibRef

Flood, G.[Gabrielle], Gillsjö, D.[David], Heyden, A.[Anders], Åström, K.[Kalle],
Efficient Merging of Maps and Detection of Changes,
SCIA19(348-360).
Springer DOI 1906
BibRef

Vincke, S., Bassier, M., Vergauwen, M.,
Image Recording Challenges for Photogrammetric Construction Site Monitoring,
3DARCH19(747-753).
DOI Link 1904
BibRef

Kakaletsis, E.[Efstratios], Tzelepi, M.[Maria], Kaplanoglou, P.I.[Pantelis I.], Symeonidis, C.[Charalampos], Nikolaidis, N.[Nikos], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Semantic Map Annotation Through UAV Video Analysis Using Deep Learning Models in ROS,
MMMod19(II:328-340).
Springer DOI 1901
BibRef

Homayounfar, N., Ma, W., Lakshmikanth, S.K., Urtasun, R.,
Hierarchical Recurrent Attention Networks for Structured Online Maps,
CVPR18(3417-3426)
IEEE DOI 1812
Roads, Task analysis, Semantics, Image segmentation, Convolution, Laser radar BibRef

Chen, K.T.[Kuan-Ting], Wang, F.E.[Fu-En], Lin, J.T.[Juan-Ting], Chan, F.H.[Fu-Hsiang], Sun, M.[Min],
The World Is Changing: Finding Changes on the Street,
CVTSV16(I: 420-435).
Springer DOI 1704
BibRef

Degol, J.[Joseph], Golparvar-Fard, M.[Mani], Hoiem, D.[Derek],
Geometry-Informed Material Recognition,
CVPR16(1554-1562)
IEEE DOI 1612
3D to assist 2D in material recogniton. i.e. construction site. BibRef

Jia, Y.H.[Yong-Hong], Zhou, M.T.[Ming-Ting], Ye, J.S.[Jin-Shan],
Object-oriented Change Detection Based On Multi-scale Approach,
ISPRS16(B7: 517-522).
DOI Link 1610
BibRef

Park, J.G., Harada, I., Kwak, Y.,
Object-based Classification And Change Detection Of Hokkaido, Japan,
ISPRS16(B8: 1003-1007).
DOI Link 1610
BibRef

Alrajhi, M.[Muhamad], Janjua, K.S.[Khurram Shahzad], Khan, M.A.[Mohammad Afroz], Alobeid, A.[Abdalla],
Updating Maps Using High Resolution Satellite Imagery,
ISPRS16(B4: 711-719).
DOI Link 1610
BibRef

Keinan, E., Felus, Y.A., Tal, Y., Zilberstien, O., Elihai, Y.,
Updating National Topographic Data Base Using Change Detection Methods,
ISPRS16(B7: 529-536).
DOI Link 1610
BibRef

Cantemir, A., Visan, A., Parvulescu, N., Dogaru, M.,
The Use Of Multiple Data Sources In The Process Of Topographic Maps Updating,
ISPRS16(B4: 19-24).
DOI Link 1610
BibRef

Tuttas, S., Braun, A., Borrmann, A., Stilla, U.,
Evaluation Of Acquisition Strategies For Image-based Construction Site Monitoring,
ISPRS16(B5: 733-740).
DOI Link 1610
BibRef

Matikainen, L.[Leena], Hyyppä, J.[Juha], Litkey, P.[Paula],
Multispectral Airborne Laser Scanning For Automated Map Updating,
ISPRS16(B3: 323-330).
DOI Link 1610
BibRef

Xu, Y., Tuttas, S., Stilla, U.,
Segmentation of 3D outdoor scenes using hierarchical clustering structure and perceptual grouping laws,
PRRS16(1-6)
IEEE DOI 1704
image segmentation BibRef

Xu, Y., Tuttas, S., Heogner, L., Stilla, U.,
Classification Of Photogrammetric Point Clouds Of Scaffolds For Construction Site Monitoring Using Subspace Clustering And Pca,
ISPRS16(B3: 725-732).
DOI Link 1610
BibRef

Lee, L., Smith, B., Chen, T.,
Fine-grain uncommon object detection from satellite images,
AIPR15(1-6)
IEEE DOI 1605
geophysical image processing BibRef

Yang, C.H., Soergel, U.,
Change Detection Based on Persistent Scatterer Interferometry: Case Study of Monitoring an Urban Area,
CMRT15(123-130).
DOI Link 1602
BibRef

Vakalopoulou, M.[Maria], Karatzalos, K.[Konstantinos], Komodakis, N.[Nikos], Paragios, N.[Nikos],
Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data,
EarthObserv15(61-69)
IEEE DOI 1510
Computational complexity BibRef

Yang, L.M.[Li-Ming], Normand, J.M.[Jean-Marie], Moreau, G.[Guillaume],
Augmenting off-the-shelf paper maps using intersection detection and geographical information systems,
MVA15(190-193)
IEEE DOI 1507
Cities and towns BibRef

Hsu, S.F.[Sheng-Fa], Tseng, Y.J.[Yi-Jen], Hsu, M.F.[Min-Fu],
A Study on Spatial Changes within Rukai Indigenous Settlements during the Japanese Colonial Era,
EuroMed14(651-658).
Springer DOI 1412
BibRef

Matzen, K.[Kevin], Snavely, N.[Noah],
Scene Chronology,
ECCV14(VII: 615-630).
Springer DOI 1408
Award, ECCV. Changes in urban scene from large image set reconstruction.
See also Modeling the World from Internet Photo Collections. BibRef

Yastikli, N., Bagci, I., Beser, C.,
The Processing of Image Data Collected by Light UAV Systems for GIS Data Capture and Updating,
SSG13(267-270).
DOI Link 1402
BibRef

Gilichinsky, M., Peled, A.,
Detection of discrepancies in land-use classification using multitemporal Ikonos satellite data,
SSG13(103-108).
DOI Link 1402
BibRef

Malinverni, E.S., Tassetti, A.N.,
GIS-Based Smart Cartography Using 3D Modeling,
GeoInfo13(47-52).
DOI Link 1402
BibRef

Vakilian, A.A.[A. Asefpour], Momeni, M.,
Mapping from space: Ontology Based Map Production Using Satellite Imageries,
SMPR13(49-54).
DOI Link 1311
BibRef
And: SMPR13(453-458).
DOI Link 1311
BibRef

Hajahmadi, S., Mokhtarzadeh, M., Mohammadzadeh, A., Valadanzouj, M.J.,
Uncertain Training Data Edition for Automatic Object-Based Change Map Extraction,
SMPR13(185-189).
DOI Link 1311
BibRef

Sakurada, K.[Ken], Okatani, T.[Takayuki],
Change Detection from a Street Image Pair using CNN Features and Superpixel Segmentation,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Sakurada, K.[Ken], Okatani, T.[Takayuki], Deguchi, K.[Koichiro],
Detecting Changes in 3D Structure of a Scene from Multi-view Images Captured by a Vehicle-Mounted Camera,
CVPR13(137-144)
IEEE DOI 1309
BibRef

Swearingen, T., Cheriyadat, A.,
Spatial feature evaluation for aerial scene analysis,
AIPR12(1-6)
IEEE DOI 1307
distance measurement BibRef

Košecka, J.[Jana],
Detecting Changes in Images of Street Scenes,
ACCV12(IV:590-601).
Springer DOI 1304
BibRef

Gkadolou, E., Tomai, E., Stefanakis, E., Kritikos, G.,
Ontological Standardization for Historical Map Collections: Studying The Greek Borderlines of 1881,
AnnalsPRS(I-2), No. 2012, pp. 203-208.
DOI Link 1209
BibRef

Sekimoto, Y., Watanabe, A., Nakamura, T., Horanont, T.,
Digital Archiving of People Flow by Recycling Large-Scale Social Survey Data of Developing Cities,
ISPRS12(XXXIX-B2:101-106).
DOI Link 1209
BibRef

Sofina, N., Ehlers, M.,
Object-based Change Detection Using High-resolution Remotely Sensed Data And Gis,
ISPRS12(XXXIX-B7:345-349).
DOI Link 1209
BibRef

Raza, A.,
Working With Spatio-temporal Data Type,
ISPRS12(XXXIX-B2:5-10).
DOI Link 1209
BibRef

Nedkov, S., Zlatanova, S.,
Google Maps For Crowdsourced Emergency Routing,
ISPRS12(XXXIX-B4:477-482).
DOI Link 1209
BibRef

Tian, W., Zhu, X., Liu, Y.,
A Bottom-up Geosptial Data Update Mechanism For Spatial Data Infrastructure Updating,
ISPRS12(XXXIX-B4:445-448).
DOI Link 1209
BibRef

Matikainen, L., Karila, K., Litkey, P., Ahokas, E., Munck, A., Karjalainen, M., Hyyppä, J.,
The Challenge Of Automated Change Detection: Developing A Method For The Updating Of Land Parcels,
AnnalsPRS(I-4), No. 2012, pp. 239-244.
DOI Link 1209
BibRef

Becker, C., Ostermann, J., Pahl, M.,
Mono-temporal GIS Update Assistance System Based on Unsupervised Coherence Analysis and Evolutionary Optimisation,
AnnalsPRS(I-4), No. 2012, pp. 233-238.
DOI Link 1209
BibRef

Zhu, L., Shimamura, H., Tachibana, K.,
Updating Building Maps Based On Object Extraction And Building Height Estimation,
ISPRS12(XXXIX-B7:371-374).
DOI Link 1209
BibRef

Duncan, P., Smit, J.,
An Investigation Of Automatic Change Detection For Topographic Map Updating,
ISPRS12(XXXIX-B7:311-316).
DOI Link 1209
BibRef

He, X.Y.[Xiao-Ying],
Change Detection for Map Updating with Classification Posterior Probability of HJ Image and TM Image,
ISIDF11(1-3).
IEEE DOI 1111
BibRef

Tian, J.J.[Jiao-Jiao], Reinartz, P.,
Multitemporal 3D Change Detection in Urban Areas Using Stereo Information from Different Sensors,
ISIDF11(1-4).
IEEE DOI 1111
BibRef

Su, J.[Juan], Wang, R.M.[Ren-Ming], Du, K.[Kai],
A Change Detection Method for Man-Made Objects in SAR Images Based on Curvelet and Level Set,
ICIG11(543-547).
IEEE DOI 1109
BibRef

Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., Zoppetti, C.,
A robust change detection feature for Cosmo-SkyMed detected SAR images,
MultiTemp11(125-128).
IEEE DOI 1109
BibRef

Bustos, C.[Carolina], Campanella, O.[Osvaldo], Kpalma, K.[Kidiyo], Magnago, F.[Fernando], Ronsin, J.[Joseph],
A method for change detection with multi-temporal satellite images based on Principal Component Analysis,
MultiTemp11(197-200).
IEEE DOI 1109
BibRef

Verbesselt, J.[Jan], Herold, M.[Martin], Hyndman, R.[Rob], Zeileis, A.[Achim], Culvenor, D.[Darius],
A robust approach for phenological change detection within satellite image time series,
MultiTemp11(41-44).
IEEE DOI 1109
BibRef

Moller, M., Glaser, C., Birger, J.,
Automatic interpolation of phenological phases in Germany,
MultiTemp11(37-40).
IEEE DOI 1109
BibRef

van Goor, B.[Bas], Lindenbergh, R.[Roderik], Soudarissanane, S.[Sylvie],
Identifying Corresponding Segments From Repeated Scan Data,
Laser11(xx-yy).
DOI Link 1109
Registration of 3D scan data to enable detailed change detection. BibRef

Neuman, B.[Bradford],
Segmentation-Based Online Change Detection for Mobile Robots,
CMU-RI-TR-10-30, August, 2010.
WWW Link. 1102
Finding changes while traversing the route. BibRef

Haberdar, H.[Hakan], Shah, S.K.[Shishir K.],
Change Detection in Dynamic Scenes using Local Adaptive Transform,
BMVC13(xx-yy).
DOI Link 1402
BibRef
Earlier:
Disparity Map Refinement for Video Based Scene Change Detection Using a Mobile Stereo Camera Platform,
ICPR10(3890-3893).
IEEE DOI 1008
BibRef

Al-Khateeb, H.[Hussein], Petrou, M.[Maria],
Automatic change detection in an indoor environment,
CVCGI10(53-58).
IEEE DOI 1006
BibRef

Yu, C.H.[Chang-Hui], Shen, S.H.[Shao-Hong], Huang, J.[Jun], Yi, Y.H.[Yao-Hua],
An object-based change detection approach using high-resolution remote sensing image and GIS data,
IASP10(565-569).
IEEE DOI 1004
BibRef

Di, F.P.[Feng-Ping], Li, X.W.[Xiao-Wen], Zhu, C.G.[Chong-Guang],
A New Method in Change Detection of Remote Sensing Image,
CISP09(1-4).
IEEE DOI 0910
BibRef

Arav, R., Filin, S.,
Detection and Quantification of Morphological Changes Using Multi-resolution Terrestrial Laser Scans,
AnnalsPRS(I-7), No. 2012, pp. 197-202.
DOI Link 1209
BibRef

Zeibak, R., Filin, S.,
Change Detection via Terrestrial Laser Scanning,
Laser07(430).
PDF File. 0709
BibRef

Moeller, M.S.[Matthias S.], Blaschke, T.[Thomas],
Urban Change Extraction from High Resolution Satellite Image,
IfromI06(xx-yy).
PDF File. 0607
BibRef

Girardeau-Montaut, D., Roux, M., Marc, R., Thibault, G.,
Change detection on point cloud data acquired with a ground laser scanner,
Laser05(xx-yy).
PDF File. 0509
BibRef

Schindler, G.[Grant], Dellaert, F.[Frank],
Probabilistic temporal inference on reconstructed 3D scenes,
CVPR10(1410-1417).
IEEE DOI Video of talk:
WWW Link. 1006
Large-scale reconstructions, but with changes. Find changes. BibRef

Schindler, G.[Grant], Dellaert, F.[Frank], Kang, S.B.[Sing Bing],
Inferring Temporal Order of Images From 3D Structure,
CVPR07(1-7).
IEEE DOI 0706
Sort (by time) a set of photos. Use fixed structures. Changes. BibRef

Pollard, T.[Thomas], Mundy, J.L.[Joseph L.],
Change Detection in a 3-d World,
CVPR07(1-6).
IEEE DOI 0706
BibRef

Niemeyer, I.,
Object-Based Change Detection: An Unsupervised Approach,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Ringle, K., Vögtle, T., Peschel, T.,
Utilisation of historical plans of the castle of Heidelberg for change detection and new construction activities,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Liu, W.[Wei], Prinet, V.[Véronique],
Probabilistic Modeling for Structural Change Inference,
ACCV06(I:836-846).
Springer DOI 0601
BibRef

Ceresola, S., Fusiello, A., Bicego, M., Belussi, A., Murino, V.,
Automatic Updating of Urban Vector Maps,
CIAP05(1133-1139).
Springer DOI 0509
BibRef

Perera, A.G.A., Hoogs, A.J.,
Bayesian object-level change detection in grayscale imagery,
ICPR04(I: 71-75).
IEEE DOI 0409
BibRef

Borchani, M., Cloppet, F., Volkan, A., Georges, S.,
Change detection in aerial images,
CRV04(354-360).
IEEE DOI 0408
BibRef

Hoogs, A.J.[Anthony J.],
Combining Geometric and Appearance Models for Change Detection,
DARPA97(565-576). BibRef 9700

Regazzoni, C.S.[Carlo S.], Teschioni, A.[Andrea], Stringa, E.[Elena],
A long term change detection method for surveillance applications,
CIAP97(II: 485-492).
Springer DOI 9709
BibRef

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


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