24.1.10 General Urban Area Detection, Built-Up Area Detection

Lu, H.Q., Aggarwal, J.K.,
Applying Perceptual Organization to the Detection of Man-Made Objects in Non-Urban Scenes,
PR(25), No. 8, August 1992, pp. 835-853.
Elsevier DOI BibRef 9208

Yu, S., Berthod, M., Giraudon, G.,
Toward Robust Analysis of Satellite Images Using Map Information: Application to Urban Area Detection,
GeoRS(37), No. 4, July 1999, pp. 1925.
IEEE Top Reference. BibRef 9907

Yu, S., Berthod, M.,
Urban Area Detection in Satellite Images Using Map Knowledge by a Feedback Control Technique,
ICPR94(A:98-102).
IEEE DOI BibRef 9400

Borghys, D., Perneel, C., Acheroy, M.,
Automatic detection of built-up areas in high-resolution polarimetric SAR images,
PRL(23), No. 9, July 2002, pp. 1085-1093.
Elsevier DOI 0205
BibRef

Borghys, D., Perneel, C., Acheroy, M.,
A multivariate contour detector for high-resolution polarimetric SAR images,
ICPR00(Vol III: 646-651).
IEEE DOI 0009
BibRef

Borghys, D., Lacroix, V., Perneel, C.,
Edge and line detection in polarimetric SAR images,
ICPR02(II: 921-924).
IEEE DOI 0211
BibRef

Epstein, J.[Jeanne], Payne, K.[Karen], Kramer, E.[Elizabeth],
Techniques for Mapping Suburban Sprawl,
PhEngRS(68), No. 9, September 2002, pp. 913-919.
WWW Link. While the amount of time required to edit road coverages is significantly higher than that for traditional remote sensing techniques, the improved thematic accuracy, spatial contiguity, and potential future uses of the resulting dataset justifies its use in a state-wide mapping program. 0304
BibRef

Hasse, J.[John], Lathrop, R.G.[Richard G.],
A Housing-Unit-Level Approach to Characterizing Residential Sprawl,
PhEngRS(69), No. 9, September 2003, pp. 1021-1030.
WWW Link. 0309
Spatial measurements of new housing units provide a means for assessing the degree to which new residential development can be characterized as sprawling. BibRef

Dell'Acqua, F., Gamba, P.,
Texture-Based Characterization of Urban Environments on Satellite SAR Images,
GeoRS(41), No. 1, January 2003, pp. 153-159.
IEEE Top Reference. 0304
BibRef

Dell'Acqua, F., Gamba, P., Lisini, G.,
Improvements to urban area characterization using multitemporal and multiangle SAR images,
GeoRS(41), No. 9, September 2003, pp. 1996-2004.
IEEE Abstract. 0310
BibRef

Gamba, P.[Paolo], Dell'Acqua, F.[Fabio], Lisini, G., Trianni, G.,
Improved VHR Urban Area Mapping Exploiting Object Boundaries,
GeoRS(45), No. 8, August 2007, pp. 2676-2682.
IEEE DOI 0709
BibRef

Bian, L.[Ling],
Retrieving Urban Objects Using a Wavelet Transform Approach,
PhEngRS(69), No. 2, February 2003, pp. 133-142. The Harr wavelet transform is used to represent edge information for the retrieval of traditional industrial complexes from digital aerial photographs in an urban area.
WWW Link. 0304
BibRef

Clapham, Jr., W.B.,
Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale,
RSE(86), No. 3, 15 August 2003, pp. 322-340.
Elsevier DOI 0309
BibRef

Nobrega, R.A.A., O'Hara, C.G., Quintanilha, J.A.,
Detecting roads in informal settlements surrounding Sao Paulo City by using object-based classification,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Nobrega, R.A.A., O'Hara, C.G.,
Segmentation and object extraction from anisotropic diffusion filtered LiDAR intensity data,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Greenhill, D.R., Ripke, L.T., Hitchman, A.P., Jones, G.A., Wilkinson, G.G.,
Characterization of suburban areas for land use planning using landscape ecological indicators derived from Ikonos-2 multispectral imagery,
GeoRS(41), No. 9, September 2003, pp. 2015-2021.
IEEE Abstract. 0310
BibRef

Herold, M., Gardner, M.E., Roberts, D.A.,
Spectral resolution requirements for mapping urban areas,
GeoRS(41), No. 9, September 2003, pp. 1907-1919.
IEEE Abstract. 0310
BibRef

Benediktsson, J.A., Pesaresi, M., Arnason, K.,
Classification and feature extraction for remote sensing images from urban areas based on morphological transformations,
GeoRS(41), No. 9, September 2003, pp. 1940-1949.
IEEE Abstract. 0310
BibRef

Fauvel, M., Chanussot, J., Benediktsson, J.A.,
Decision Fusion for the Classification of Urban Remote Sensing Images,
GeoRS(44), No. 10, October 2006, pp. 2828-2838.
IEEE DOI 0609
BibRef

Orun, A.B.[Ahmet B.],
Automated Identification of Man-Made Textural Features on Satellite Imagery by Bayesian Networks,
PhEngRS(70), No. 2, February 2004, pp. 211-217.
WWW Link. A classification technique which distinguishes between man-made and natural textural features on satellite imagery by the use of textural analyses and Bayesian networks is introduced. 0403
BibRef

Unsalan, C., Boyer, K.L.,
Classifying Land Development in High-Resolution Panchromatic Satellite Images Using Straight-Line Statistics,
GeoRS(42), No. 4, April 2004, pp. 907-919.
IEEE Abstract. 0407
BibRef
Earlier:
Classifying land development in high resolution satellite images using straight line statistics,
ICPR02(I: 127-130).
IEEE DOI 0211
BibRef

Unsalan, C., Boyer, K.L.,
A Theoretical and Experimental Investigation of Graph Theoretical Measures for Land Development in Satellite Imagery,
PAMI(27), No. 4, April 2005, pp. 575-589.
IEEE Abstract. 0501
BibRef
Earlier: ICPR04(II: 64-67).
IEEE DOI 0409
BibRef

Ünsalan, C.[Cem], Boyer, K.L.[Kim L.],
A system to detect houses and residential street networks in multispectral satellite images,
CVIU(98), No. 3, June 2005, pp. 423-461.
Elsevier DOI 0505
BibRef
Earlier: ICPR04(III: 49-52).
IEEE DOI 0409
BibRef

Unsalan, C.,
Measuring Land Development in Urban Regions Using Graph Theoretical and Conditional Statistical Features,
GeoRS(45), No. 12, December 2007, pp. 3989-3999.
IEEE DOI 0711
BibRef

Sirmacek, B.[Beril], Unsalan, C.[Cem],
Using local features to measure land development in urban regions,
PRL(31), No. 10, 15 July 2010, pp. 1155-1159.
Elsevier DOI 1008
Ikonos images; Gabor filtering; Local features; Spatial voting; Measuring land development BibRef

Sirmacek, B.[Beril], Unsalan, C.[Cem],
Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory,
GeoRS(47), No. 4, April 2009, pp. 1156-1167.
IEEE DOI 0903
BibRef

Unsalan, C.[Cem], Sirmacek, B.[Beril],
Road Network Detection Using Probabilistic and Graph Theoretical Methods,
GeoRS(50), No. 11, November 2012, pp. 4441-4453.
IEEE DOI 1210
BibRef
Earlier: A2, A1:
Road Network Extraction Using Edge Detection and Spatial Voting,
ICPR10(3113-3116).
IEEE DOI 1008
BibRef

Sirmacek, B.[Beril], Unsalan, C.[Cem],
A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images,
GeoRS(49), No. 1, January 2011, pp. 211-221.
IEEE DOI 1101
BibRef

Özcan, A.H.[Abdullah H.], Ünsalan, C.[Cem],
Probabilistic object detection and shape extraction in remote sensing data,
CVIU(195), 2020, pp. 102953.
Elsevier DOI 2005
BibRef

Zeljkovic, V., Dorado, A., Izquierdo, E.,
Combining a Fuzzy Rule-Based Classifier and Illumination Invariance for Improved Building Detection,
CirSysVideo(14), No. 11, November 2004, pp. 1277-1280.
IEEE Abstract. 0411
Edge based classification of buildings from video. BibRef

Zeljkovic, V.[Vesna], Tameze, C.[Claude], Vincelette, R.[Robert], Izquierdo, E.,
Different non-linear diffusion filters combined with triangle method used for noise removal from polygonal shapes,
IET-IPR(4), No. 4, August 2010, pp. 313-333.
DOI Link 1008
BibRef
Earlier: A1, A2, A3, Only:
Combined nonlinear inverse diffusion filter and triangle method used for noise removal from polygonal shapes,
ICIP08(21-24).
IEEE DOI 0810
BibRef

Dorado, A.[Andres], Izquierdo, E.[Ebroul],
Exploiting Problem Domain Knowledge for Accurate Building Image Classification,
CIVR04(199-206).
Springer DOI 0505
BibRef

Myint, S.W.[Soe W.], Lam, N.[Nina],
Examining Lacunarity Approaches in Comparison with Fractal and Spatial Autocorrelation Techniques for Urban Mapping,
PhEngRS(71), No. 8, August 2005, pp. 927-938.
WWW Link. 0602
An evaluation and comparison of two lacunarity methods, fractal triangular prism and spatial autocorrelation, and original spectral band approaches in classifying urban images. BibRef

Zandona-Schneider, R., Papathanassiou, K.P., Hajnsek, I., Moreira, A.,
Polarimetric and Interferometric Characterization of Coherent Scatterers in Urban Areas,
GeoRS(44), No. 4, April 2006, pp. 971-984.
IEEE DOI 0604
BibRef

Matikainen, L.[Leena], Hyyppä, J.[Juha], Engdahl, M.[Marcus],
Mapping Built-up Areas from Multitemporal Interferometric SAR Images: A Segment-based Approach,
PhEngRS(72), No. 6, June 2006, pp. 701-714.
WWW Link. 0610
BibRef
Earlier:
Extracting Built-Up Areas from Multitemporal Interferometric SAR Images,
PCV02(B: 170). 0305
Built-up areas were accurately detected from a multitemporal interferometric ERS dataset using a region-based classification approach, and a correlation between the building density and intensity/coherence in the image was found. BibRef

Zhong, P.[Ping], Wang, R.S.[Run-Sheng],
Using Combination of Statistical Models and Multilevel Structural Information for Detecting Urban Areas From a Single Gray-Level Image,
GeoRS(45), No. 5, May 2007, pp. 1469-1482.
IEEE DOI 0704

See also Learning Conditional Random Fields for Classification of Hyperspectral Images. BibRef

Zhong, P.[Ping], Wang, R.S.[Run-Sheng],
A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images,
GeoRS(45), No. 12, December 2007, pp. 3978-3988.
IEEE DOI 0711
BibRef

Inglada, J.[Jordi],
Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features,
PandRS(62), No. 3, August 2007, pp. 236-248.
Elsevier DOI 0709
Object recognition; Man-made objects; Support vector machines; Geometrical moments BibRef

Inglada, J., Michel, J.,
Qualitative Spatial Reasoning for High-Resolution Remote Sensing Image Analysis,
GeoRS(47), No. 2, February 2009, pp. 599-612.
IEEE DOI 0903
BibRef

Vanegas, M.C., Bloch, I., Inglada, J.,
Alignment and Parallelism for the Description of High-Resolution Remote Sensing Images,
GeoRS(51), No. 6, 2013, pp. 3542-3557.
IEEE DOI fuzzy set theory; object alignment representation 1307
BibRef

Xu, H.Q.[Han-Qiu],
Extraction of Urban Built-up Land Features from Landsat Imagery Using a Thematic-oriented Index Combination Technique,
PhEngRS(73), No. 12, December 2007, pp. 1381-1392.
WWW Link. 0712
A new method to extract urban built-up land information from Landsat imagery based on three thematic indexes. BibRef

Wei, W.[Wang], Xin, Y.[Yang],
Feature extraction for man-made objects segmentation in aerial images,
MVA(19), No. 1, January 2008, pp. 57-64.
Springer DOI 0801
BibRef

Wei, W.[Wang], Xin, Y.[Yang], Guo, C.[Cao],
A Multiphase Level Set Evolution Scheme for Aerial Image Segmentation Using Multi-scale Image Geometric Analysis,
SSPR06(56-64).
Springer DOI 0608
BibRef

Wei, W.[Wang], Xin, Y.[Yang],
Rapid, man-made object morphological segmentation for aerial images using a multi-scaled, geometric image analysis,
IVC(28), No. 4, April 2010, pp. 626-633.
Elsevier DOI 1002
Man-made object segmentation; Multi-scaled geometric image analysis; Watershed; The non-subsampled contourlet transform BibRef

Huang, M.J.[Ming-Jer], Shyue, S.W.[Shiahn-Wern], Lee, L.H.[Liang-Hwei], Kao, C.C.[Chih-Chung],
A Knowledge-based Approach to Urban Feature Classification Using Aerial Imagery with Lidar Data,
PhEngRS(74), No. 12, December 2008, pp. 1473-1486.
WWW Link. 0804
Applying knowledge- and segment-based vertical stratification, rule-based classification, and aggregation schemes with knowledge-based correction to improve the classification accuracy of urban features BibRef

Guindon, B.[Bert], Zhang, Y.[Ying],
Automated Urban Delineation from Landsat Imagery Based on Spatial Information Processing,
PhEngRS(75), No. 7, July 2009, pp. 845-858.
WWW Link. 0910
Proposed methods to automatically delineate urban areas on Landsat imagery based on extracted line information. BibRef

Karantzalos, K.[Konstantinos], Argialas, D.[Demertre],
A Region-based Level Set Segmentation for Automatic Detection of Man-made Objects from Aerial and Satellite Images,
PhEngRS(75), No. 6, June 2009, pp. 667-678.
WWW Link. 0910
A region-based segmentation developed, tested, and evaluated for automatically detecting roads, buildings, and other man-made objects from aerial and satellite images.
See also Large-Scale Building Reconstruction Through Information Fusion and 3-D Priors. BibRef

Esch, T., Thiel, M., Schenk, A., Roth, A., Muller, A., Dech, S.,
Delineation of Urban Footprints From TerraSAR-X Data by Analyzing Speckle Characteristics and Intensity Information,
GeoRS(48), No. 2, February 2010, pp. 905-916.
IEEE DOI 1002

See also Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information. BibRef

Margarit, G., Mallorqui, J.J., Pipia, L.,
Polarimetric Characterization and Temporal Stability Analysis of Urban Target Scattering,
GeoRS(48), No. 4, April 2010, pp. 2038-2048.
IEEE DOI 1003
BibRef

Ouma, Y.O., Tateishi, R., Sri-Sumantyo, J.T.,
Urban features recognition and extraction from very-high resolution multi-spectral satellite imagery: A micro-macro texture determination and integration framework,
IET-IPR(4), No. 4, August 2010, pp. 235-254.
DOI Link 1008
BibRef

Lafarge, F.[Florent], Gimel'farb, G.L.[Georgy L.], Descombes, X.[Xavier],
Geometric Feature Extraction by a Multimarked Point Process,
PAMI(32), No. 9, September 2010, pp. 1597-1609.
IEEE DOI 1008
BibRef
Earlier:
A Geometric Primitive Extraction Process for Remote Sensing Problems,
ACIVS08(xx-yy).
Springer DOI 0810
Tree crowns, building footprints, roads. Describe the image in terms of a finite library of geometric objects. Segments, lines, line ends, circles, rectangles, bands (||), band ends (U). BibRef

Lafarge, F., Descombes, X., Zerubia, J.B.,
Textural Kernel for SVM Classification in Remote Sensing: Application to Forest Fire Detection and Urban Area Extraction,
ICIP05(III: 1096-1099).
IEEE DOI 0512
BibRef

Mallet, C.[Clement], Lafarge, F.[Florent], Roux, M., Soergel, U.[Uwe], Bretar, F.[Frederic], Heipke, C.[Christian],
A Marked Point Process for Modeling Lidar Waveforms,
IP(19), No. 12, December 2010, pp. 3204-3221.
IEEE DOI 1011
BibRef
Earlier: A1, A2, A5, A4, A6, Only:
Lidar waveform modeling using a marked point process,
ICIP09(1713-1716).
IEEE DOI 0911
BibRef

Schmidt, A., Kruse, C., Rottensteiner, F., Soergel, U., Heipke, C.,
Network Detection In Raster Data Using Marked Point Processes,
ISPRS16(B3: 701-708).
DOI Link 1610
BibRef
Earlier: A1, A3, A4, A5, Only:
Extraction of fluvial networks in lidar data using marked point processes,
PCV14(297-304).
DOI Link 1404
BibRef

Guo, L.[Li], Boukir, S.[Samia],
Fast Data Selection for SVM Training Using Ensemble Margin,
PRL(51), No. 1, 2015, pp. 112-119.
Elsevier DOI 1412
BibRef
And:
Ensemble margin framework for image classification,
ICIP14(4231-4235)
IEEE DOI 1502
Accuracy. Boundary points
See also Exploring Issues of Training Data Imbalance and Mislabelling on Random Forest Performance for Large Area Land Cover Classification Using the Ensemble Margin. BibRef

Guo, L.[Li], Boukir, S.[Samia],
Margin-based ordered aggregation for ensemble pruning,
PRL(34), No. 6, 15 April 2013, pp. 603-609.
Elsevier DOI 1303
Ensemble learning; Ensemble pruning; Margin; Ordered aggregation; Bagging BibRef

Boukir, S.[Samia], Mellor, A.,
Ensemble diversity analysis on remote sensing data classification using random forests,
ICIP17(1302-1306)
IEEE DOI 1803
Training, Ensemble diversity, classification, ensemble margin, land cover, training data selection BibRef

Boukir, S.[Samia], Guo, L.[Li], Chehata, N.[Nesrine],
Classification of Remote Sensing Data Using Margin-Based Ensemble Methods,
ICIP13(2602-2606)
IEEE DOI 1402
BibRef
And: A2, A1, A3:
Support Vectors Selection for Supervised Learning Using an Ensemble Approach,
ICPR10(37-40).
IEEE DOI 1008
Bagging BibRef

Paladini, R., Martorella, M., Berizzi, F.,
Classification of Man-Made Targets via Invariant Coherency-Matrix Eigenvector Decomposition of Polarimetric SAR/ISAR Images,
GeoRS(49), No. 8, August 2011, pp. 3022-3034.
IEEE DOI 1108
BibRef

Stumpf, A., Lachiche, N., Malet, J.P., Kerle, N., Puissant, A.,
Active Learning in the Spatial Domain for Remote Sensing Image Classification,
GeoRS(52), No. 5, May 2014, pp. 2492-2507.
IEEE DOI 1403
Entropy BibRef

Geiß, C., Taubenböck, H., Wurm, M., Esch, T., Nast, M., Schillings, C., Blaschke, T.,
Remote Sensing-Based Characterization of Settlement Structures for Assessing Local Potential of District Heat,
RS(3), No. 7, July 2011, pp. 1447-1471.
DOI Link 1203
BibRef

Yang, F.[Fan], Matsushita, B.[Bunkei], Yang, W.[Wei], Fukushima, T.[Takehiko],
Mapping the human footprint from satellite measurements in Japan,
PandRS(88), No. 1, 2014, pp. 80-90.
Elsevier DOI 1402
Human footprint BibRef

As-syakur, A., Adnyana, I., Arthana, I., Nuarsa, I.,
Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area,
RS(4), No. 10, October 2012, pp. 2957-2970.
DOI Link 1210
BibRef

Henze, F.[Frank], Lehmann, H.[Heike], Bruschke, B.[Bettina],
Analysis of Historic Maps and Images for Research on Urban Development of Baalbek/Lebanon,
PFG(2009), No. 3, 2009, pp. 221-234.
WWW Link. 1211
BibRef

Gamba, P.,
Human Settlements: A Global Challenge for EO Data Processing and Interpretation,
PIEEE(100), No. 3, March 2013, pp. 570-581.
IEEE DOI 1303
BibRef

Li, W.W.[Wen-Wen], Li, L.[Linna], Goodchild, M.F.[Michael F.], Anselin, L.[Luc],
A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making,
IJGI(2), No. 2, 2013, pp. 413-431.
DOI Link 1307
BibRef

Petrovic, S.[Stefan], Karlsson, K.[Kenneth],
Model for Determining Geographical Distribution of Heat Saving Potentials in Danish Building Stock,
IJGI(3), No. 1, 2014, pp. 143-165.
DOI Link 1404
BibRef

Mei, A.[Alessandro], Salvatori, R.[Rosamaria], Fiore, N.[Nicola], Allegrini, A.[Alessia], d'Andrea, A.[Antonio],
Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation,
RS(6), No. 4, 2014, pp. 2765-2781.
DOI Link 1405
BibRef

Li, N.[Na], Bruzzone, L.[Lorenzo], Chen, Z.P.[Zeng-Ping], Liu, F.[Fang],
A Novel Technique Based on the Combination of Labeled Co-Occurrence Matrix and Variogram for the Detection of Built-up Areas in High-Resolution SAR Images,
RS(6), No. 5, 2014, pp. 3857-3878.
DOI Link 1407
BibRef

Oltra-Carrio, R., Cubero-Castan, M., Briottet, X., Sobrino, J.A.,
Analysis of the Performance of the TES Algorithm Over Urban Areas,
GeoRS(52), No. 11, November 2014, pp. 6989-6998.
IEEE DOI 1407
Asphalt BibRef

Zhang, J.[Jun], Li, P.J.[Pei-Jun], Wang, J.F.[Jin-Fei],
Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture,
RS(6), No. 8, 2014, pp. 7339-7359.
DOI Link 1410
BibRef

Scott, D.[Douglas], Petropoulos, G.P.[George P.], Moxley, J.[Janet], Malcolm, H.[Heath],
Quantifying the Physical Composition of Urban Morphology throughout Wales Based on the Time Series (1989-2011) Analysis of Landsat TM/ETM+ Images and Supporting GIS Data,
RS(6), No. 12, 2014, pp. 11731-11752.
DOI Link 1412
BibRef

Lelo, K.[Keti],
A GIS Approach to Urban History: Rome in the 18th Century,
IJGI(3), No. 4, 2014, pp. 1293-1316.
DOI Link 1412
BibRef

Conesa, F.C.[Francesc C.], Devanthéry, N.[Núria], Balbo, A.L.[Andrea L.], Madella, M.[Marco], Monserrat, O.[Oriol],
Use of Satellite SAR for Understanding Long-Term Human Occupation Dynamics in the Monsoonal Semi-Arid Plains of North Gujarat, India,
RS(6), No. 11, 2014, pp. 11420-11443.
DOI Link 1412
BibRef

Gueguen, L.[Lionel],
Classifying Compound Structures in Satellite Images: A Compressed Representation for Fast Queries,
GeoRS(53), No. 4, April 2015, pp. 1803-1818.
IEEE DOI 1502
geophysical image processing. High res imagery, hierarchical segmentation, descriptions. BibRef

Gueguen, L.[Lionel], Pesaresi, M.[Martino], Ehrlich, D.[Daniele], Lu, L.L.[Lin-Lin],
Urbanization analysis by mutual information based change detection between SPOT 5 panchromatic images,
MultiTemp11(157-160).
IEEE DOI 1109
BibRef

Li, C.[Cheng], Thinh, N.X.[Nguyen Xuan], Zhao, J.[Jie],
Spatiotemporally Varying Relationships between Urban Growth Patterns and Driving Factors in Xuzhou City, China,
PFG(2014), No. 6, 2014, pp. 535-548.
DOI Link 1503
BibRef

Duan, Y.L.[Yu-Lin], Shao, X.W.[Xiao-Wei], Shi, Y.[Yun], Miyazaki, H.[Hiroyuki], Iwao, K.[Koki], Shibasaki, R.[Ryosuke],
Unsupervised Global Urban Area Mapping via Automatic Labeling from ASTER and PALSAR Satellite Images,
RS(7), No. 2, 2015, pp. 2171-2192.
DOI Link 1503
BibRef

Bitelli, G.[Gabriele], Conte, P.[Paolo], Csoknyai, T.[Tamas], Franci, F.[Francesca], Girelli, V.A.[Valentina A.], Mandanici, E.[Emanuele],
Aerial Thermography for Energetic Modelling of Cities,
RS(7), No. 2, 2015, pp. 2152-2170.
DOI Link 1503
BibRef

Ban, Y.F.[Yi-Fang], Jacob, A.[Alexander], Gamba, P.[Paolo],
Spaceborne SAR data for global urban mapping at 30M resolution using a robust urban extractor,
PandRS(103), No. 1, 2015, pp. 28-37.
Elsevier DOI 1504
KTH-Pavia Urban Extractor BibRef

Asahara, A.[Akinori], Hayashi, H.[Hideki], Kai, T.[Takashi],
Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior,
IJGI(4), No. 2, 2015, pp. 515-534.
DOI Link 1504
BibRef

Bennie, J.[Jonathan], Duffy, J.P.[James P.], Davies, T.W.[Thomas W.], Correa-Cano, M.E.[Maria Eugenia], Gaston, K.J.[Kevin J.],
Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems,
RS(7), No. 3, 2015, pp. 2715-2730.
DOI Link 1504
BibRef

Chen, X.[Xi], Nordhaus, W.[William],
A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa,
RS(7), No. 4, 2015, pp. 4937-4947.
DOI Link 1505
BibRef

Zhang, X.[Xiya], Li, P.J.[Pei-Jun], Cai, C.[Cai],
Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification,
RS(7), No. 6, 2015, pp. 7671.
DOI Link 1507
BibRef

Wan, B.[Bo], Guo, Q.H.[Qing-Hua], Fang, F.[Fang], Su, Y.J.[Yan-Jun], Wang, R.[Run],
Mapping US Urban Extents from MODIS Data Using One-Class Classification Method,
RS(7), No. 8, 2015, pp. 10143.
DOI Link 1509
BibRef

Johnson, B.A.[Brian A.], Bragais, M.[Milben], Endo, I.[Isao], Magcale-Macandog, D.B.[Damasa B.], Macandog, P.B.M.[Paula Beatrice M.],
Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery,
IJGI(4), No. 4, 2015, pp. 2292.
DOI Link 1511
BibRef

Zhang, L.B.[Li-Bao], Zhang, J.[Jue], Wang, S.[Shuang], Chen, J.[Jie],
Residential Area Extraction Based on Saliency Analysis for High Spatial Resolution Remote Sensing Images,
JVCIR(33), No. 1, 2015, pp. 273-285.
Elsevier DOI 1512
Remote sensing image processing BibRef

Zhang, L.B.[Li-Bao], Li, A.[Aoxue], Zhang, Z.J.[Zhong-Jun], Yang, K.[Kaina],
Global and Local Saliency Analysis for the Extraction of Residential Areas in High-Spatial-Resolution Remote Sensing Image,
GeoRS(54), No. 7, July 2016, pp. 3750-3763.
IEEE DOI 1606
Biological system modeling BibRef

Zhang, Z.H.[Zhong-Hao], Yang, X.Q.[Xiao-Qin], Xiao, R.[Rui],
Fractal Characterization of Settlement Patterns and Their Spatial Determinants in Coastal Zones,
IJGI(4), No. 4, 2015, pp. 2728.
DOI Link 1601
Human settlement. BibRef

Hu, Z.W.[Zhong-Wen], Li, Q.Q.[Qing-Quan], Zhang, Q.[Qian], Wu, G.F.[Guo-Feng],
Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection,
RS(8), No. 2, 2016, pp. 155.
DOI Link 1603
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Tomaszewska, M.A.[Monika A.], Henebry, G.M.[Geoffrey M.],
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Gevaert, C.M.[Caroline M.], Persello, C.[Claudio], Sliuzas, R., Vosselman, G.[George],
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Rizaldy, A.[Aldino], Persello, C.[Claudio], Gevaert, C.M.[Caroline M.], Oude Elberink, S.[Sander], Vosselman, G.[George],
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Ma, X.L.[Xiao-Long], Tong, X.H.[Xiao-Hua], Liu, S.[Sicong], Luo, X.[Xin], Xie, H.[Huan], Li, C.M.[Cheng-Ming],
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Wang, R.[Run], Wan, B.[Bo], Guo, Q.H.[Qing-Hua], Hu, M.S.[Mao-Sheng], Zhou, S.P.[Shun-Ping],
Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data,
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Liao, C.[Cong], Dai, T.[Teqi], Cai, H.Y.[Hong-Yu], Zhang, W.X.[Wen-Xin],
Examining the Driving Factors Causing Rapid Urban Expansion in China: An Analysis Based on GlobeLand30 Data,
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Rimal, B.[Bhagawat], Zhang, L.[Lifu], Keshtkar, H.[Hamidreza], Wang, N.[Nan], Lin, Y.[Yi],
Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model,
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Rimal, B.[Bhagawat], Zhang, L.[Lifu], Keshtkar, H.[Hamidreza], Haack, B.N.[Barry N.], Rijal, S.[Sushila], Zhang, P.[Peng],
Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain,
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Esch, T.[Thomas], Heldens, W.[Wieke], Hirner, A.[Andreas], Keil, M.[Manfred], Marconcini, M.[Mattia], Roth, A.[Achim], Zeidler, J.[Julian], Dech, S.[Stefan], Strano, E.[Emanuele],
Breaking new ground in mapping human settlements from space: The Global Urban Footprint,
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Elsevier DOI 1712
Global mapping, Human settlements, Urbanization, Automation, Processing, Image analysis, Texture, TerraSAR-X, TanDEM-X BibRef

Mboga, N.[Nicholus], Persello, C.[Claudio], Bergado, J.R.[John Ray], Stein, A.[Alfred],
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Ajami, A.[Alireza], Kuffer, M.[Monika], Persello, C.[Claudio], Pfeffer, K.[Karin],
Identifying a Slums' Degree of Deprivation from VHR Images Using Convolutional Neural Networks,
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Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia,
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Valdiviezo-Navarro, J.C.[Juan C.], Tellez-Quinones, A.[Alejandro], Salazar-Garibay, A.[Adan], Lopez-Caloca, A.A.[Alejandra A.],
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Digital image processing, Image analysis, Optical sensing and sensors , Passive remote sensing BibRef

Dawson, T.[Teddy], Sandoval, J.S.O.[J.S. Onésimo], Sagan, V.[Vasit], Crawford, T.[Thomas],
A Spatial Analysis of the Relationship between Vegetation and Poverty,
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Zhou, Y.[Yi], Tu, M.G.[Ming-Guang], Wang, S.X.[Shi-Xin], Liu, W.L.[Wen-Liang],
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Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery,
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Quan, S., Xiong, B., Xiang, D., Kuang, G.,
Derivation of the Orientation Parameters in Built-Up Areas: With Application to Model-Based Decomposition,
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IEEE DOI 1808
airborne radar, geophysical image processing, image classification, land cover, radar imaging, radar polarimetry, scattering characteristics BibRef

Ehrlich, D.[Daniele], Melchiorri, M.[Michele], Florczyk, A.J.[Aneta J.], Pesaresi, M.[Martino], Kemper, T.[Thomas], Corbane, C.[Christina], Freire, S.[Sergio], Schiavina, M.[Marcello], Siragusa, A.[Alice],
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Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China,
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DOI Link 1811
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Delineation of Built-Up Areas from Very High-Resolution Satellite Imagery Using Multi-Scale Textures and Spatial Dependence,
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Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia,
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Osgouei, P.E.[Paria Ettehadi], Kaya, S.[Sinasi], Sertel, E.[Elif], Alganci, U.[Ugur],
Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery,
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Yang, J.[Jing], Zhu, J.[Jie], Sun, Y.Z.[Yi-Zhong], Zhao, J.H.[Jian-Hua],
Delimitating Urban Commercial Central Districts by Combining Kernel Density Estimation and Road Intersections: A Case Study in Nanjing City, China,
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Gadal, S.[Sébastien], Ouerghemmi, W.[Walid],
Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk,
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Urban Mapping Accuracy Enhancement in High-Rise Built-Up Areas Deployed by 3D-Orthorectification Correction from WorldView-3 and LiDAR Imageries,
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Karunarathne, A.[Ananda], Lee, G.[Gunhak],
Estimating Hilly Areas Population Using a Dasymetric Mapping Approach: A Case of Sri Lanka's Highest Mountain Range,
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Luo, P.[Peng], Zhang, X.F.[Xian-Feng], Cheng, J.[Junyi], Sun, Q.[Quan],
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Roni, R.[Rezaul], Jia, P.[Peng],
An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh,
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Ma, D.[Ding], Guo, R.Z.[Ren-Zhong], Zheng, Y.[Ye], Zhao, Z.G.[Zhi-Gang], He, F.N.[Fang-Ning], Zhu, W.[Wei],
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Masot, A.N.[Ana Nieto], Alonso, G.C.[Gema Cárdenas], Moriche, Á.E.[Ángela Engelmo],
Spatial Analysis of the Rural-Urban Structure of the Spanish Municipalities,
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Li, Q.M.[Qian-Ming], Zheng, B.[Bohong], Tu, B.[Bing], Yang, Y.S.[Yu-Sheng], Wang, Z.Y.[Zhi-Yuan], Jiang, W.[Wei], Yao, K.[Kai], Yang, J.W.[Jia-Wei],
Refining Urban Built-Up Area via Multi-Source Data Fusion for the Analysis of Dongting Lake Eco-Economic Zone Spatiotemporal Expansion,
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Comparative Assessment of the Built-Up Area Expansion Based on Corine Land Cover and Landsat Datasets: A Case Study of a Post-Socialist City,
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Cabrera-Barona, P.F.[Pablo F.], Bayón, M.[Manuel], Durán, G.[Gustavo], Bonilla, A.[Alejandra], Mejía, V.[Verónica],
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Zhao, L.[Lu], Ren, H.Y.[Hong-Yan], Cui, C.[Cheng], Huang, Y.H.[Yao-Huan],
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Bartsch, A.[Annett], Pointner, G.[Georg], Ingeman-Nielsen, T.[Thomas], Lu, W.J.[Wen-Jun],
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Elsevier DOI 2008
Built-up region segmentation, Semantic segmentation, Domain adaptation, Weakly-supervised adaptation, Deep learning BibRef

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Sentinel-2, TanDEM-X, Urban morphology, Built-up height estimation, Built-up density estimation, Regression models BibRef

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Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy,
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Henriques, C.[Cristina], Domingues, A.[Alexandre], Pereira, M.[Margarida],
What Is Urban after All? A Critical Review of Measuring and Mapping Urban Typologies in Portugal,
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Mukherjee, A., Kumar, A.A., Ramachandran, P.,
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IEEE DOI 2101
Indexes, Earth, Artificial satellites, Remote sensing, Satellites, Support vector machines, Estimation, urban built-up extraction BibRef

Kotaridis, I.[Ioannis], Lazaridou, M.[Maria],
Remote sensing image segmentation advances: A meta-analysis,
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Elsevier DOI 2102
BibRef
Earlier:
Object-based Image Analysis of Different Spatial Resolution Satellite Imageries In Urban and Suburban Environment,
ISPRS20(B3:105-112).
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Image segmentation, Remote sensing, Semantic segmentation, Meta-analysis, Review BibRef

He, X.[Xiong], Yuan, X.D.[Xiao-Die], Zhang, D.[Dahao], Zhang, R.R.[Rong-Rong], Li, M.[Ming], Zhou, C.[Chunshan],
Delineation of Urban Agglomeration Boundary Based on Multisource Big Data Fusion: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area (GBA),
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Yuan, X.D.[Xiao-Die], Dai, X.J.[Xiang-Jun], Zou, Z.[Zeduo], He, X.[Xiong], Sun, Y.C.[Yu-Cong], Zhou, C.S.[Chun-Shan],
Multi-Source Data-Driven Extraction of Urban Residential Space: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration,
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Li, F.[Feng], Liu, X.Y.[Xiao-Yang], Liao, S.[Shunbao], Jia, P.[Peng],
The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas,
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Impervious Surface Detection, Urban Area Extraction .