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
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.Q.[Zhi-Qi],
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
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
Bozzano, M.[Matteo],
Sguerso, D.[Domenico],
Zatelli, P.[Paolo],
Zendri, D.[Davide],
Besana, A.[Angelo],
Accuracy Evaluation for Plan-Reliefs and Historical Maps Created
during WWI in Northern Italy,
IJGI(13), No. 3, 2024, pp. 101.
DOI Link
2404
BibRef
Wang, W.[Wenda],
Li, X.[Xiao],
Wang, T.[Ting],
Wang, S.H.[Shao-Hua],
Wang, R.[Runqiao],
Xu, D.[Dachuan],
Zhou, J.[Junyuan],
Spatial-Temporal Evolution Characteristics Analysis of Color Steel
Buildings in Lanzhou City,
IJGI(13), No. 6, 2024, pp. 179.
DOI Link
2406
BibRef
Asrat, K.T.[Kaleab Taye],
Cho, H.J.[Hyung-Ju],
A Comprehensive Survey on High-Definition Map Generation and
Maintenance,
IJGI(13), No. 7, 2024, pp. 232.
DOI Link
2408
BibRef
Zumaya, C.R.C.[Carlos Roberto Cueto],
Catalano, I.[Iacopo],
Queralta, J.P.[Jorge Peña],
Building Better Models: Benchmarking Feature Extraction and Matching
for Structure from Motion at Construction Sites,
RS(16), No. 16, 2024, pp. 2974.
DOI Link
2408
BibRef
Lei, Z.[Zhen],
Yuan, Z.[Zhangshun],
Lei, T.L.[Ting L.],
On the Theoretical Link between Optimized Geospatial Conflation
Models for Linear Features,
IJGI(13), No. 9, 2024, pp. 310.
DOI Link
2410
BibRef
Tian, J.L.[Ji-Long],
Wu, J.J.[Jiang-Jiang],
Chen, H.[Hao],
Ma, M.Y.[Meng-Yu],
MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via
Denoising Diffusion Bridge Model,
RS(16), No. 19, 2024, pp. 3716.
DOI Link
2410
BibRef
Deng, K.[Kai],
Hu, X.Y.[Xiang-Yun],
Zhang, Z.[Zhili],
Su, B.[Bo],
Feng, C.[Cunjun],
Zhan, Y.Z.[Yuan-Zeng],
Wang, X.K.[Xing-Kun],
Duan, Y.S.[Yan-Song],
Cross-modal change detection using historical land use maps and
current remote sensing images,
PandRS(218), 2024, pp. 114-132.
Elsevier DOI
2412
Historical land use maps, Remote sensing images, Deep learning,
Cross-modal change detection
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
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
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 .