23.2.27 Classification for Urban Area Land Cover, Remote Sensing

Wharton, S.W.[Stephen W.],
A Contextual Classification Method for Recognizing Land Use Patterns in High Resolution Remotely Sensed Data,
PR(15), No. 4, 1982, pp. 317-324.
Elsevier DOI BibRef 8200

Heikkonen, J.[Jukka], Varfis, A.[Aristide],
Land Cover Land Use Classification of Urban Areas: A Remote-Sensing Approach,
PRAI(12), No. 4, June 1998, pp. 475-489. 9808
BibRef

Heikkonen, J.[Jukka], Varfis, A.[Aristide], Kanellopoulos, I.[Ioannis],
A Method for Remote Sensing Based Classification of Urban Areas,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Schneider, A.[Annemarie], Friedl, M.A.[Mark A.], McIver, D.M.[Douglas M.], Woodcock, C.E.[Curtis E.],
Mapping Urban Areas by Fusing Multiple Sources of Coarse Resolution Remotely Sensed Data,
PhEngRS(69), No. 12, December 2003, pp. 1377-1386.
WWW Link. 0401
The main objective of this research is to improve the understanding of the methodological, scale, and validation requirements for mapping urban land cover from the fusion of 1-km MODIS data with DMSP nighttime lights data set and gridded population density data. BibRef

Islam, Z., Metternicht, G.,
The Performance of Fuzzy Operators on Fuzzy Classification of Urban Land Covers,
PhEngRS(71), No. 1, January 2005, pp. 59-68. Evaluation of the performance of fuzzy operators for integrating fuzzy membership values associated with multiple spectral bands for mapping urban land covers.
WWW Link. 0509
BibRef

Pozzi, F.[Francesca], Small, C.[Christopher],
Analysis of Urban Land Cover and Population Density in the United States,
PhEngRS(71), No. 6, June 2005, pp. 719-726. Analysis of population density and vegetation distribution for several cities shows a strong correspondence in cities with high population density but considerable regional variability that precludes simple spectral classifications of land cover.
WWW Link. 0509
BibRef

Huang, X.[Xin], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
Classification of Very High Spatial Resolution Imagery Based on the Fusion of Edge and Multispectral Information,
PhEngRS(74), No. 12, December 2008, pp. 1585-1597.
WWW Link. 0804
A new algorithm to classify high spatial resolution remotely sensed imagery by integrating fuzzy edge information and multispectral features. BibRef

Huang, X.[Xin], Zhang, L.P.[Liang-Pei],
An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification from Urban Hyperspectral Imagery,
GeoRS(46), No. 12, December 2008, pp. 4173-4185.
IEEE DOI 0812
BibRef

Huang, X.[Xin], Zhang, L.P.[Liang-Pei],
An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery,
GeoRS(51), No. 1, January 2013, pp. 257-272.
IEEE DOI 1301
BibRef

Zhu, Q.Q.[Qi-Qi], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Li, D.,
Scene Classification Based on the Fully Sparse Semantic Topic Model,
GeoRS(55), No. 10, October 2017, pp. 5525-5538.
IEEE DOI 1710
BibRef
Earlier: A1, A2, A3, Only:
Scene Classification Based On The Semantic-feature Fusion Fully Sparse Topic Model For High Spatial Resolution Remote Sensing Imagery,
ISPRS16(B7: 451-457).
DOI Link 1610
feature extraction, optimisation, concave maximization, dense semantic representation, limited training samples, BibRef

Zhu, Q.Q.[Qi-Qi], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Li, D.,
Adaptive Deep Sparse Semantic Modeling Framework for High Spatial Resolution Image Scene Classification,
GeoRS(56), No. 10, October 2018, pp. 6180-6195.
IEEE DOI 1810
Feature extraction, Semantics, Visualization, Adaptation models, Probabilistic logic, Remote sensing, Encoding, Adaptive, scene classification BibRef

Zhu, Q.Q.[Qi-Qi], Zhong, Y.F.[Yan-Fei], Wu, S., Zhang, L.P.[Liang-Pei], Li, D.,
Scene Classification Based on the Sparse Homogeneous-Heterogeneous Topic Feature Model,
GeoRS(56), No. 5, May 2018, pp. 2689-2703.
IEEE DOI 1805
Feature extraction, Image segmentation, Remote sensing, Roads, Semantics, Training, Visualization, Geographical, scene understanding BibRef

Zhao, B.[Bei], Zhong, Y.F.[Yan-Fei], Xia, G.S.[Gui-Song], Zhang, L.P.[Liang-Pei],
Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery,
GeoRS(54), No. 4, April 2016, pp. 2108-2123.
IEEE DOI 1604
Buildings BibRef

Zhao, B.[Bei], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Huang, B.[Bo],
The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification,
RS(8), No. 2, 2016, pp. 157.
DOI Link 1603
BibRef

Zhong, Y.F.[Yan-Fei], Zhu, Q.Q.[Qi-Qi], Zhang, L.P.[Liang-Pei],
Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery,
GeoRS(53), No. 11, November 2015, pp. 6207-6222.
IEEE DOI 1509
feature extraction BibRef

Zhong, Y.F.[Yan-Fei], Han, X.B.[Xiao-Bing], Zhang, L.P.[Liang-Pei],
Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery,
PandRS(138), 2018, pp. 281-294.
Elsevier DOI 1804
Geospatial object detection, High spatial resolution (HSR) remote sensing imagery, Position-sensitive balancing BibRef

Hu, J.W.[Jing-Wen], Xia, G.S.[Gui-Song], Hu, F.[Fan], Zhang, L.P.[Liang-Pei],
A Comparative Study of Sampling Analysis in the Scene Classification of Optical High-Spatial Resolution Remote Sensing Imagery,
RS(7), No. 11, 2015, pp. 14988.
DOI Link 1512
BibRef

Hu, F.[Fan], Xia, G.S.[Gui-Song], Hu, J.W.[Jing-Wen], Zhong, Y.F.[Yan-Fei], Xu, K.[Kan],
Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery,
RS(8), No. 7, 2016, pp. 555.
DOI Link 1608
BibRef

Zhao, B.[Bei], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery,
PandRS(116), No. 1, 2016, pp. 73-85.
Elsevier DOI 1604
Scene classification BibRef

Zhu, Q.Q.[Qi-Qi], Zhong, Y.F.[Yan-Fei], Liu, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Li, D.R.[De-Ren],
A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Zhong, Y.F.[Yan-Fei], Zhao, B.[Bei], Zhang, L.P.[Liang-Pei],
Multiagent Object-Based Classifier for High Spatial Resolution Imagery,
GeoRS(52), No. 2, February 2014, pp. 841-857.
IEEE DOI 1402
evolutionary computation BibRef

Chen, D.Y.[Ding-Yuan], Zhong, Y.F.[Yan-Fei], Ma, A.[Ailong], Zhang, L.P.[Liang-Pei],
Blurry dense object extraction based on buffer parsing network for high-resolution satellite remote sensing imagery,
PandRS(207), 2024, pp. 122-140.
Elsevier DOI Code:
WWW Link. 2401
Blurry dense object extraction, Dense boundary separation, Blurry boundary refinement, Buffer parsing architecture, High-resolution remote sensing imagery BibRef

Zhong, Y.F.[Yan-Fei], Zhao, J., Zhang, L.P.[Liang-Pei],
A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery,
GeoRS(52), No. 11, November 2014, pp. 7023-7037.
IEEE DOI 1407
Context modeling BibRef

Zhao, Y.D.[Yin-Di], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang], Huang, B.[Bo],
Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features,
GeoRS(45), No. 5, May 2007, pp. 1458-1468.
IEEE DOI 0704
BibRef

Zhao, J.[Ji], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Detail-Preserving Smoothing Classifier Based on Conditional Random Fields for High Spatial Resolution Remote Sensing Imagery,
GeoRS(53), No. 5, May 2015, pp. 2440-2452.
IEEE DOI 1502
geophysical image processing BibRef

Lv, P.Y.[Peng-Yuan], Zhong, Y.F.[Yan-Fei], Zhao, J.[Ji], Zhang, L.P.[Liang-Pei],
Unsupervised Change Detection Based on Hybrid Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery,
GeoRS(56), No. 7, July 2018, pp. 4002-4015.
IEEE DOI 1807
geophysical image processing, geophysical techniques, image resolution, image segmentation, remote sensing, HCRF model, remote sensing BibRef

Shi, S.[Sunan], Zhong, Y.F.[Yan-Fei], Zhao, J.[Ji], Lv, P.Y.[Peng-Yuan], Liu, Y.H.[Yin-He], Zhang, L.P.[Liang-Pei],
Land-Use/Land-Cover Change Detection Based on Class-Prior Object-Oriented Conditional Random Field Framework for High Spatial Resolution Remote Sensing Imagery,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Task analysis, Object oriented modeling, Remote sensing, Image segmentation, Spatial resolution, Manuals, Analytical models, remote sensing BibRef

Zhu, Q.Q.[Qi-Qi], Guo, X.[Xi], Deng, W.H.[Wei-Huan], Shi, S.[Sunan], Guan, Q.F.[Qing-Feng], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Li, D.R.[De-Ren],
Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery,
PandRS(184), 2022, pp. 63-78.
Elsevier DOI 2202
Change detection, Semantic change detection, Remote sensing, Imbalanced sample, Siamese network, Change mask BibRef

Zhao, J.[Ji], Zhong, Y.F.[Yan-Fei], Shu, H., Zhang, L.P.[Liang-Pei],
High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields,
IP(25), No. 9, September 2016, pp. 4033-4045.
IEEE DOI 1609
geophysical image processing BibRef

Zhang, L.P.[Liang-Pei], Zhao, Y.D.[Yin-Di], Huang, B.[Bo], Li, P.X.[Ping-Xiang],
Texture Feature Fusion with Neighborhood-Oscillating Tabu Search for High Resolution Image Classification,
PhEngRS(74), No. 3, March 2008, pp. 323-332.
WWW Link. 0803
Neighborhood-Oscillating tabu search integrates different types of texture features to improve classifi cation performance of high-resolution imagery. BibRef

Wu, S.S.[Shuo-Sheng], Xu, B.[Bing], Wang, L.[Le],
Urban Land-use Classification Using Variogram-based Analysis with an Aerial Photograph,
PhEngRS(72), No. 7, July 2006, pp. 813-822.
WWW Link. 0610
A variogram-based texture analysis was tested for classifying detailed urban land-use classes, such as mobile home, singlefamily house, multi-family house, industrial, and commercial, from a digital color infrared aerial photograph. BibRef

van de Voorde, T.[Tim], de Genst, W.[William], Canters, F.[Frank],
Improving Pixel-based VHR Land-cover Classifications of Urban Areas with Post-classification Techniques,
PhEngRS(73), No. 9, September 2007, pp. 1017-1028.
WWW Link. 0709
Three post-classification techniques were applied to improve the accuracy and the structural coherence of an urban land-cover map derived from a soft pixel-based classification. BibRef

Bellens, R., Gautama, S., Martinez-Fonte, L., Philips, W., Chan, J.C.W., Canters, F.[Frank],
Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles,
GeoRS(46), No. 10, October 2008, pp. 2803-2813.
IEEE DOI 0810
BibRef

Chan, J.C.W.[Jonathan Cheung-Wai], Bellens, R.[Rik], Canters, F.[Frank], Gautama, S.[Sidharta],
An Assessment of Geometric Activity Features for Per-pixel Classification of Urban Man-made Objects using Very High Resolution Satellite Imagery,
PhEngRS(75), No. 4, April 2009, pp. 397-412.
WWW Link. 0903
The results of using geometric activity features based on ridge-based modeling and morphological profi les for the classification of urban man-made objects from an Ikonos image. BibRef

Xu, B.[Bing], Gong, P.[Peng],
Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data,
PhEngRS(73), No. 8, August 2007, pp. 955-965.
WWW Link. 0709
Land-use and land-cover classification in an urban rural fringe of the San Francisco Bay Area using EO-1 Hyperion imagery is compared with that using EO-1 ALI imagery, and the application of a computationally efficient segmentation-based feature reduction approach. BibRef

Myint, S.W.[Soe W.], Wentz, E.A.[Elizabeth A.], Purkis, S.J.[Sam J.],
Employing Spatial Metrics in Urban Land-use/Landcover Mapping: Comparing the Getis and Geary Indices,
PhEngRS(73), No. 12, December 2007, pp. 1403-1417.
WWW Link. 0712
The effectiveness of Getis index (Gi) in comparison to a measure of spatial autocorrelation (Geary's C) in classifying landuse / land-cover classes in a high resolution imagery and the impact of distance threshold used in Getis index with regards to the classification accuracy. BibRef

Huang, H.[Heng], Legarsky, J.[Justin], Othman, M.[Maslina],
Land-cover Classification Using Radarsat and Landsat Imagery for St. Louis, Missouri,
PhEngRS(73), No. 1, January 2007, pp. 37-44.
WWW Link. 0704
An investigation of the classification accuracy of merging satellite imagery from Radarsat and Landsat missions. BibRef

Aytekin, Ö.[Örsan], Ulusoy, I.[Ilkay],
Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology,
PRL(32), No. 13, 1 October 2011, pp. 1618-1625.
Elsevier DOI 1109
Image segmentation; Differential morphological profile (DMP); Very high resolution (VHR) images; Mathematical morphology Morphology to get scale. BibRef

Miyazaki, H., Iwao, K., Shibasaki, R.,
Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer,
RS(3), No. 6, June 2011, pp. 1177-1187.
DOI Link 1203
BibRef

d'Oleire-Oltmanns, S., Coenradie, B., Kleinschmit, B.,
An Object-Based Classification Approach for Mapping Migrant Housing in the Mega-Urban Area of the Pearl River Delta (China),
RS(3), No. 8, August 2011, pp. 1710-1723.
DOI Link 1203
BibRef

Matikainen, L., Karila, K.,
Segment-Based Land Cover Mapping of a Suburban Area: Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points,
RS(3), No. 8, August 2011, pp. 1777-1804.
DOI Link 1203
BibRef

Moskal, L., Styers, D., Halabisky, M.,
Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data,
RS(3), No. 10, October 2011, pp. 2243-2262.
DOI Link 1203
BibRef

Novack, T., Esch, T., Kux, H., Stilla, U.,
Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification,
RS(3), No. 10, October 2011, pp. 2263-2282.
DOI Link 1203
BibRef

Hofmann, P., Strobl, J., Nazarkulova, A.,
Mapping Green Spaces in Bishkek: How Reliable can Spatial Analysis Be?,
RS(3), No. 6, June 2011, pp. 1088-1103.
DOI Link 1203
BibRef

Longbotham, N., Chaapel, C., Bleiler, L., Padwick, C., Emery, W.J., Pacifici, F.,
Very High Resolution Multiangle Urban Classification Analysis,
GeoRS(50), No. 4, April 2012, pp. 1155-1170.
IEEE DOI 1204
BibRef

Salehi, B., Zhang, Y., Zhong, M., Dey, V.,
Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data,
RS(4), No. 8, August 2012, pp. 2256-2276.
DOI Link 1209
BibRef

Soheili Majd, M.[Maryam], Simonetto, E.[Elisabeth], Polidori, L.[Laurent],
Maximum Likelihood Classification of Single Highresolution Polarimetric SAR Images in Urban Areas,
PFG(2012), No. 4, 2012, pp. 395-407.
WWW Link. 1211
BibRef
Earlier:
Maximum Likelihood Classification of High-Resolution Polarimetric SAR Images in Urban Area,
HighRes11(xx-yy).
PDF File. 1106
BibRef

Ogashawara, I., Bastos, V.,
A Quantitative Approach for Analyzing the Relationship between Urban Heat Islands and Land Cover,
RS(4), No. 11, November 2012, pp. 3596-3618.
DOI Link 1211
BibRef

Singh, K.K.[Kunwar K.], Vogler, J.B.[John B.], Shoemaker, D.A.[Douglas A.], Meentemeyer, R.K.[Ross K.],
LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy,
PandRS(74), No. 1, November 2012, pp. 110-121.
Elsevier DOI 1212
LiDAR; Landsat; Fusion; Land cover; Large-area assessment; Mapping accuracy; Managed clearings BibRef

Pan, G., Qi, G., Wu, Z., Zhang, D., Li, S.,
Land-Use Classification Using Taxi GPS Traces,
ITS(14), No. 1, March 2013, pp. 113-123.
IEEE DOI 1303
BibRef

Ban, Y., Jacob, A.,
Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping,
GeoRS(51), No. 4, April 2013, pp. 1998-2006.
IEEE DOI 1304
BibRef

Shen, L.[Luou], Lu, C.X.[Chen-Xi], Zhao, F.[Fang], Liu, W.M.[Wei-Ming],
Discrete Fourier Transformation for Seasonal-Factor Pattern Classification and Assignment,
ITS(14), No. 2, 2013, pp. 511-516.
IEEE DOI 1307
DFT; land use characteristic; urban area; Roads BibRef

Johnson, B.[Brian], Xie, Z.X.[Zhi-Xiao],
Classifying a high resolution image of an urban area using super-object information,
PandRS(83), No. 1, 2013, pp. 40-49.
Elsevier DOI 1308
Segmentation BibRef

Kohli, D.[Divyani], Warwadekar, P.[Pankaj], Kerle, N.[Norman], Sliuzas, R.[Richard], Stein, A.[Alfred],
Transferability of Object-Oriented Image Analysis Methods for Slum Identification,
RS(5), No. 9, 2013, pp. 4209-4228.
DOI Link 1310
BibRef

Wu, H.[Hao], Sun, Y.R.[Yu-Rong], Shi, W.Z.[Wen-Zhong], Chen, X.L.[Xiao-Ling], Fu, D.J.[Dong-Jie],
Examining the Satellite-Detected Urban Land Use Spatial Patterns Using Multidimensional Fractal Dimension Indices,
RS(5), No. 10, 2013, pp. 5152-5172.
DOI Link 1311
BibRef

Belgiu, M.[Mariana], Dragut, L.[Lucian], Strobl, J.[Josef],
Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery,
PandRS(87), No. 1, 2014, pp. 205-215.
Elsevier DOI 1402
Land Cover BibRef

Meganem, I., Deliot, P., Briottet, X., Deville, Y., Hosseini, S.,
Linear-Quadratic Mixing Model for Reflectances in Urban Environments,
GeoRS(52), No. 1, January 2014, pp. 544-558.
IEEE DOI 1402
geophysical image processing BibRef

Li, C.C.[Cong-Cong], Wang, J.[Jie], Wang, L.[Lei], Hu, L.Y.[Luan-Yun], Gong, P.[Peng],
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery,
RS(6), No. 2, 2014, pp. 964-983.
DOI Link 1403
BibRef

Carlei, V.[Vittorio], Nuccio, M.[Massimiliano],
Mapping industrial patterns in spatial agglomeration: A SOM approach to Italian industrial districts,
PRL(40), No. 1, 2014, pp. 1-10.
Elsevier DOI 1403
Self-organizing maps BibRef

Huang, X.[Xin], Lu, Q.K.[Qi-Kai], Zhang, L.P.[Liang-Pei],
A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas,
PandRS(90), No. 1, 2014, pp. 36-48.
Elsevier DOI 1404
High spatial resolution BibRef

Wieland, M.[Marc], Pittore, M.[Massimiliano],
Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images,
RS(6), No. 4, 2014, pp. 2912-2939.
DOI Link 1405
BibRef

Zhou, W.Q.[Wei-Qi], Cadenasso, M.L.[Mary. L.], Schwarz, K.[Kirsten], Pickett, S.T.A.[Steward T.A.],
Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification,
RS(6), No. 4, 2014, pp. 3369-3386.
DOI Link 1405
BibRef

Kotthaus, S.[Simone], Smith, T.E.L.[Thomas E.L.], Wooster, M.J.[Martin J.], Grimmond, C.S.B.,
Derivation of an urban materials spectral library through emittance and reflectance spectroscopy,
PandRS(94), No. 1, 2014, pp. 194-212.
Elsevier DOI 1407
Spectral library BibRef

Okujeni, A.[Akpona], van der Linden, S.[Sebastian], Jakimow, B.[Benjamin], Rabe, A.[Andreas], Verrelst, J.[Jochem], Hostert, P.[Patrick],
A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover,
RS(6), No. 7, 2014, pp. 6324-6346.
DOI Link 1408
BibRef

Galletti, C.S.[Christopher S.], Myint, S.W.[Soe W.],
Land-Use Mapping in a Mixed Urban-Agricultural Arid Landscape Using Object-Based Image Analysis: A Case Study from Maricopa, Arizona,
RS(6), No. 7, 2014, pp. 6089-6110.
DOI Link 1408
BibRef

Haberman, D.[Daniel], Gillies, L.[Laura], Canter, A.[Aryeh], Rinner, V.[Valentine], Pancrazi, L.[Laetitia], Martellozzo, F.[Federico],
The Potential of Urban Agriculture in Montréal: A Quantitative Assessment,
IJGI(3), No. 3, 2014, pp. 1101-1117.
DOI Link 1410
BibRef

Du, P.J.[Pei-Jun], Liu, P.[Pei], Xia, J.[Junshi], Feng, L.[Li], Liu, S.[Sicong], Tan, K.[Kun], Cheng, L.[Liang],
Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples,
RS(6), No. 10, 2014, pp. 9458-9474.
DOI Link 1411
BibRef

Rahman, M.M.[Mir Mustafizur], Hay, G.J.[Geoffrey J.], Couloigner, I.[Isabelle], Hemachandran, B.[Bharanidharan],
Transforming Image-Objects into Multiscale Fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines,
RS(6), No. 10, 2014, pp. 9435-9457.
DOI Link 1411
BibRef

O'Neil-Dunne, J.[Jarlath], MacFaden, S.[Sean], Royar, A.[Anna],
A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion,
RS(6), No. 12, 2014, pp. 12837-12865.
DOI Link 1412
BibRef

Rau, J.Y.[Jiann-Yeou], Jhan, J.P.[Jyun-Ping], Hsu, Y.C.[Ya-Ching],
Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment,
GeoRS(53), No. 3, March 2015, pp. 1304-1319.
IEEE DOI 1412
feature extraction BibRef

Ðuric, N.[Nataša], Pehani, P.[Peter], Oštir, K.[Krištof],
Application of In-Segment Multiple Sampling in Object-Based Classification,
RS(6), No. 12, 2014, pp. 12138-12165.
DOI Link 1412
Urban area. BibRef

Feng, Q.L.[Quan-Long], Liu, J.T.[Jian-Tao], Gong, J.H.[Jian-Hua],
UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis,
RS(7), No. 1, 2015, pp. 1074-1094.
DOI Link 1502

See also Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier: The Case of Yuyao, China. BibRef

Li, M.M.[Meng-Meng], Bijker, W.[Wietske], Stein, A.[Alfred],
Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover,
PandRS(102), No. 1, 2015, pp. 48-61.
Elsevier DOI 1503
Urban land cover BibRef

Chhetri, S.K.[Sachin Kumar], Kayastha, P.[Prabin],
Manifestation of an Analytic Hierarchy Process (AHP) Model on Fire Potential Zonation Mapping in Kathmandu Metropolitan City, Nepal,
IJGI(4), No. 1, 2015, pp. 400-417.
DOI Link 1504
BibRef

Su, G.W.[Gui-Wu], Qi, W.H.[Wen-Hua], Zhang, S.L.[Su-Ling], Sim, T.[Timothy], Liu, X.S.[Xin-Sheng], Sun, R.[Rui], Sun, L.[Lei], Jin, Y.F.[Yi-Fan],
An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China,
RS(7), No. 3, 2015, pp. 2543-2601.
DOI Link 1504
BibRef

Blaschke, T.[Thomas], Hay, G.J.[Geoffrey J.], Weng, Q.H.[Qi-Hao], Resch, B.[Bernd],
Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems: An Overview,
RS(3), No. 8, August 2011, pp. 1743-1776.
DOI Link Award, Remote Sensing, Review, Second. 2015. BibRef 1108

Yang, J.X.[Jin-Xin], Wong, M.S.[Man Sing], Menenti, M.[Massimo], Nichol, J.[Janet],
Modeling the effective emissivity of the urban canopy using sky view factor,
PandRS(105), No. 1, 2015, pp. 211-219.
Elsevier DOI 1506
Urban geometry BibRef

Yang, J.X.[Jin-Xin], Wong, M.S.[Man Sing], Menenti, M.[Massimo], Nichol, J.[Janet],
Study of the geometry effect on land surface temperature retrieval in urban environment,
PandRS(109), No. 1, 2015, pp. 77-87.
Elsevier DOI 1512
Urban surface temperature BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei], Guo, L.[Lei], Liu, Z.B.[Zhen-Bao], Bu, S.H.[Shu-Hui], Ren, J.C.[Jin-Chang],
Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images,
GeoRS(53), No. 8, August 2015, pp. 4238-4249.
IEEE DOI 1506
land use BibRef

Matasci, G.[Giona], Longbotham, N.[Nathan], Pacifici, F.[Fabio], Kanevski, M.[Mikhail], Tuia, D.[Devis],
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Elsevier DOI 1508
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PandRS(119), No. 1, 2016, pp. 347-360.
Elsevier DOI 1610
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Yang, Y.[Yetao], Wang, Y.[Yi], Wu, K.[Ke], Yu, X.[Xin],
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Synthetic aperture radar, Minimization, Image denoising, Speckle, Convex functions, Noise measurement, Optimization, weighted nuclear norm BibRef

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Karalas, K.[Konstantinos], Tsagkatakis, G.[Grigorios], Zervakis, M.[Michael], Tsakalides, P.[Panagiotis],
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Distributed Training and Inference of Deep Learning Models for Multi-Modal Land Cover Classification,
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Giannopoulos, M.[Michalis], Tsagkatakis, G.[Grigorios], Tsakalides, P.[Panagiotis],
4D U-Nets for Multi-Temporal Remote Sensing Data Classification,
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Kim, Y.M.[Yong-Min],
Generation of Land Cover Maps through the Fusion of Aerial Images and Airborne LiDAR Data in Urban Areas,
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Deilami, K.[Kaveh], Kamruzzaman, M., Hayes, J.F.[John Francis],
Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia,
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Zhang, Q.[Qi], Xin, J.Y.[Jin-Yuan], Yin, Y.[Yan], Wang, L.L.[Li-Li], Wang, Y.[Yuesi],
The Variations and Trends of MODIS C5 & C6 Products' Errors in the Recent Decade over the Background and Urban Areas of North China,
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DOI Link 1610
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Zheng, X.Y.[Xin-Yu], Wang, Y.[Yang], Gan, M.[Muye], Zhang, J.[Jing], Teng, L.M.[Long-Mei], Wang, K.[Ke], Shen, Z.Q.[Zhang-Quan], Zhang, L.[Ling],
Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region,
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Li, M.M.[Meng-Meng], Stein, A.[Alfred], Bijker, W.[Wietske], Zhan, Q.M.[Qing-Ming],
Urban land use extraction from Very High Resolution remote sensing imagery using a Bayesian network,
PandRS(122), No. 1, 2016, pp. 192-205.
Elsevier DOI 1612
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Schreyer, J.[Johannes], Lakes, T.[Tobia],
Deriving and Evaluating City-Wide Vegetation Heights from a TanDEM-X DEM,
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Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data,
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Lu, Y.H.[Yu-Hao], Coops, N.C.[Nicholas C.], Hermosilla, T.[Txomin],
Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data,
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Elsevier DOI 1704
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Susaki, J.[Junichi], Kubota, S.[Seiya],
Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data,
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Li, H.[Hui], Wang, C.Z.[Cui-Zhen], Zhong, C.[Cheng], Su, A.J.[Ai-Jun], Xiong, C.R.[Cheng-Ren], Wang, J.G.[Jin-Ge], Liu, J.Q.[Jun-Qi],
Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index,
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Li, H.[Hui], Wang, C.Z.[Cui-Zhen], Zhong, C.[Cheng], Zhang, Z.[Zhi], Liu, Q.B.[Qing-Bin],
Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters,
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Grippa, T.[Taïs], Lennert, M.[Moritz], Beaumont, B.[Benjamin], Vanhuysse, S.[Sabine], Stephenne, N.[Nathalie], Wolff, E.[Eléonore],
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Simwanda, M.[Matamyo], Murayama, Y.J.[Yu-Ji],
Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia,
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Kuffer, M.[Monika], Pfeffer, K.[Karin], Sliuzas, R.[Richard], Baud, I.[Isa], van Maarseveen, M.[Martin],
Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai,
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Tsou, J.Y.[Jin-Yeu], Gao, Y.F.[Yan-Fei], Zhang, Y.Z.[Yuan-Zhi], Genyun, S.[Sun], Ren, J.C.[Jin-Chang], Li, Y.[Yu],
Evaluating Urban Land Carrying Capacity Based on the Ecological Sensitivity Analysis: A Case Study in Hangzhou, China,
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Chen, T.[Tao], Zhang, X.[Xujia], Niu, R.Q.[Rui-Qing],
The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA,
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Degerickx, J.[Jeroen], Okujeni, A.[Akpona], Iordache, M.D.[Marian-Daniel], Hermy, M.[Martin], van der Linden, S.[Sebastian], Somers, B.[Ben],
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Liu, Y.X.Y.[Yang-Xiao-Yue], Yang, Y.P.[Ya-Ping], Jing, W.L.[Wen-Long], Yao, L.[Ling], Yue, X.F.[Xia-Fang], Zhao, X.D.[Xiao-Dan],
A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL,
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Zhong, Y.F.[Yan-Fei], Cao, Q.[Qiong], Zhao, J.[Ji], Ma, A.L.[Ai-Long], Zhao, B.[Bei], Zhang, L.P.[Liang-Pei],
Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data,
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Maleki, J.[Jamshid], Hakimpour, F.[Farshad], Masoumi, Z.[Zohreh],
A Parcel-Level Model for Ranking and Allocating Urban Land-Uses,
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Duque, J.C.[Juan C.], Patino, J.E.[Jorge E.], Betancourt, A.[Alejandro],
Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery,
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Xia, Z.L.[Ze-Long], Li, H.[Hao], Chen, Y.H.[Yue-Hong],
An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China,
IJGI(6), No. 11, 2017, pp. xx-yy.
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Huang, X.[Xin], Chen, H.J.[Hui-Jun], Gong, J.Y.[Jian-Ya],
Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery,
PandRS(135), No. Supplement C, 2018, pp. 127-141.
Elsevier DOI 1712
Multi-angle, Urban classification, High spatial resolution, Scene classification BibRef

Georg, I.[Isabel], Blaschke, T.[Thomas], Taubenböck, H.[Hannes],
Are We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors,
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Zhou, T.[Tao], Zhao, M.F.[Mei-Fang], Sun, C.L.[Chuan-Liang], Pan, J.J.[Jian-Jun],
Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region,
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Johnson, B.A.[Brian A.], Jozdani, S.E.[Shahab E.],
Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling,
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Jia, Y.X.[Yuan-Xin], Ge, Y.[Yong], Ling, F.[Feng], Guo, X.[Xian], Wang, J.H.[Jiang-Hao], Wang, L.[Le], Chen, Y.H.[Yue-Hong], Li, X.D.[Xiao-Dong],
Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data,
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Audebert, N.[Nicolas], Le Saux, B.[Bertrand], Lefèvre, S.[Sébastien],
Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks,
PandRS(140), 2018, pp. 20-32.
Elsevier DOI 1805
Deep learning, Remote sensing, Semantic mapping, Data fusion BibRef

Baker, F.[Fraser], Smith, C.L.[Claire L.], Cavan, G.[Gina],
A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis,
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Roupioz, L.[Laure], Nerry, F.[Françoise], Colin, J.[Jérôme],
Correction for the Impact of the Surface Characteristics on the Estimation of the Effective Emissivity at Fine Resolution in Urban Areas,
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DOI Link 1806
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Garg, A., Singh, D.,
Development of an Efficient Contextual Algorithm for Discrimination of Tall Vegetation and Urban for PALSAR Data,
GeoRS(56), No. 6, June 2018, pp. 3413-3420.
IEEE DOI 1806
Backscatter, Entropy, Fractals, Scattering, Silicon, Vegetation, Vegetation mapping, Classification, entropy, texture BibRef

Chen, J.[Jike], Du, P.J.[Pei-Jun], Wu, C.[Changshan], Xia, J.[Junshi], Chanussot, J.[Jocelyn],
Mapping Urban Land Cover of a Large Area Using Multiple Sensors Multiple Features,
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Grippa, T.[Taïs], Georganos, S.[Stefanos], Zarougui, S.[Soukaina], Bognounou, P.[Pauline], Diboulo, E.[Eric], Forget, Y.[Yann], Lennert, M.[Moritz], Vanhuysse, S.[Sabine], Mboga, N.[Nicholus], Wolff, E.[Eléonore],
Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics,
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Xie, X.P.[Xiao-Ping], Hou, W.[Wei], Herold, H.[Hendrik],
Ex Post Impact Assessment of Master Plans: The Case of Shenzhen in Shaping a Polycentric Urban Structure,
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Zhang, N.Y.[Ning-Yu], Deng, S.M.[Shu-Min], Chen, H.J.[Hua-Jun], Chen, X.[Xi], Chen, J.Y.[Jiao-Yan], Li, X.Q.[Xiao-Qian], Zhang, Y.[Yiyi],
Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis,
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Mapping Population Distribution from High Resolution Remotely Sensed Imagery in a Data Poor Setting,
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Cao, R.[Rui], Zhu, J.S.[Jia-Song], Tu, W.[Wei], Li, Q.Q.[Qing-Quan], Cao, J.Z.[Jin-Zhou], Liu, B.Z.[Bo-Zhi], Zhang, Q.[Qian], Qiu, G.P.[Guo-Ping],
Integrating Aerial and Street View Images for Urban Land Use Classification,
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Feng, Y.[Yanlei], Qi, Y.[Yi],
Modeling Patterns of Land Use in Chinese Cities Using an Integrated Cellular Automata Model,
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Faridatul, M.I.[Mst Ilme], Wu, B.[Bo],
Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices,
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Ren, Q.A.[Qi-Ang], He, C.Y.[Chun-Yang], Huang, Q.X.[Qing-Xu], Zhou, Y.Y.[Yu-Yu],
Urbanization Impacts on Vegetation Phenology in China,
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Luo, N.X.[Nian-Xue], Wan, T.L.[Tai-Li], Hao, H.X.[Huai-Xu], Lu, Q.K.[Qi-Kai],
Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas,
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Feng, Q.L.[Quan-Long], Zhu, D.[Dehai], Yang, J.Y.[Jian-Yu], Li, B.G.[Bao-Guo],
Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network,
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Nduati, E.[Eunice], Sofue, Y.[Yuki], Matniyaz, A.[Akbar], Park, J.G.[Jong Geol], Yang, W.[Wei], Kondoh, A.[Akihiko],
Cropland Mapping Using Fusion of Multi-Sensor Data in a Complex Urban/Peri-Urban Area,
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Alganci, U.[Ugur],
Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods,
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Kranjcic, N.[Nikola], Medak, D.[Damir], Župan, R.[Robert], Rezo, M.[Milan],
Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns,
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Wurm, M.[Michael], Stark, T.[Thomas], Zhu, X.X.[Xiao Xiang], Weigand, M.[Matthias], Taubenböck, H.[Hannes],
Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks,
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Elsevier DOI 1903
Slums, FCN, Convolutional neural networks, Deep learning, Transfer learning BibRef

Ge, P.P.[Pan-Pan], He, J.[Jun], Zhang, S.H.[Shu-Hua], Zhang, L.W.[Li-Wei], She, J.F.[Jiang-Feng],
An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification,
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Zhao, W.Z.[Wen-Zhi], Bo, Y.C.[Yan-Chen], Chen, J.G.[Jia-Ge], Tiede, D.[Dirk], Blaschke, T.[Thomas], Emery, W.J.[William J.],
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Elsevier DOI 1904
Semantic classification, Urban scene recognition, Deep learning, High-resolution imagery, OpenStreetMap (OSM), Data fusion BibRef

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Mapping of Coastal Cities Using Optimized Spectral-Spatial Features Based Multi-Scale Superpixel Classification,
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Liu, S.[Shishi], Su, H.[Hang], Cao, G.F.[Guo-Feng], Wang, S.Q.[Shan-Qin], Guan, Q.F.[Qing-Feng],
Learning from data: A post classification method for annual land cover analysis in urban areas,
PandRS(154), 2019, pp. 202-215.
Elsevier DOI 1907
Annual land cover change detection, Spatio-temporal land cover filter, Urban area BibRef

Pan, T.[Tao], Kuang, W.H.[Wen-Hui], Hamdi, R.[Rafiq], Zhang, C.[Chi], Zhang, S.[Shu], Li, Z.L.[Zhi-Li], Chen, X.[Xin],
City-Level Comparison of Urban Land-Cover Configurations from 2000-2015 across 65 Countries within the Global Belt and Road,
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Jozdani, S.E.[Shahab Eddin], Johnson, B.A.[Brian Alan], Chen, D.M.[Dong-Mei],
Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification,
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DOI Link 1908
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Mugiraneza, T.[Theodomir], Nascetti, A.[Andrea], Ban, Y.F.[Yi-Fang],
WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices,
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DOI Link 1909
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Ilehag, R.[Rebecca], Schenk, A.[Andreas], Huang, Y.L.[Yi-Lin], Hinz, S.[Stefan],
KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials,
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DOI Link 1909
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Zhou, Q.[Qian], Zhao, X.[Xiang], Wu, D.H.[Dong-Hai], Tang, R.Y.[Rong-Yun], Du, X.Z.[Xiao-Zheng], Wang, H.Y.[Hao-Yu], Zhao, J.C.[Jia-Cheng], Xu, P.P.[Pei-Pei], Peng, Y.F.[Yi-Feng],
Impact of Urbanization and Climate on Vegetation Coverage in the Beijing-Tianjin-Hebei Region of China,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
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Yang, F.S.[Feng-Shuo], Wang, Z.H.[Zhi-Hua], Yang, X.M.[Xiao-Mei], Liu, Y.M.[Yue-Ming], Liu, B.[Bin], Wang, J.[Jun], Kang, J.[Junmei],
Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
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Shi, Y.[Yan], Qi, Z.X.[Zhi-Xin], Liu, X.P.[Xiao-Ping], Niu, N.[Ning], Zhang, H.[Hui],
Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
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Ullah, H.[Hidayat], Wan, W.G.[Wang-Gen], Haidery, S.A.[Saqib Ali], Khan, N.U.[Naimat Ullah], Ebrahimpour, Z.[Zeinab], Luo, T.H.[Tian-Hang],
Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data,
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Nie, Z.[Zhen], Chan, K.K.Y.[Karen Kie Yan], Xu, B.[Bing],
Preliminary Evaluation of the Consistency of Landsat 8 and Sentinel-2 Time Series Products in An Urban Area: An Example in Beijing, China,
RS(11), No. 24, 2019, pp. xx-yy.
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Fu, C.[Cheng], Song, X.P.[Xiao-Peng], Stewart, K.[Kathleen],
Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change,
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Masoumi, Z.[Zohreh], van Genderen, J.[John], Maleki, J.[Jamshid],
Fire Risk Assessment in Dense Urban Areas Using Information Fusion Techniques,
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Richards, D.R.[Daniel R.], Belcher, R.N.[Richard N.],
Global Changes in Urban Vegetation Cover,
RS(12), No. 1, 2019, pp. xx-yy.
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Liu, C.[Chun], Zeng, D.D.[Dou-Dou], Wu, H.B.[Hang-Bin], Wang, Y.[Yin], Jia, S.J.[Shou-Jun], Xin, L.[Liang],
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Zhong, Y.[Yang], Lin, A.[Aiwen], He, L.J.[Li-Jie], Zhou, Z.[Zhigao], Yuan, M.[Moxi],
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Xiao, C.L.[Chang-Lin], Qin, R.[Rongjun], Ling, X.[Xiao],
Urban Land-Cover Classification Using Side-View Information from Oblique Images,
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A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover,
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Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery,
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Dobrinic, D.[Dino], Medak, D., Gašparovic, M.[Mateo],
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Pilant, A.[Andrew], Endres, K.[Keith], Rosenbaum, D.[Daniel], Gundersen, G.[Gillian],
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Luo, X.[Xin], Tong, X.H.[Xiao-Hua], Hu, Z.W.[Zhong-Wen], Wu, G.F.[Guo-Feng],
Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy,
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Coleman, R.W.[Red Willow], Stavros, N.[Natasha], Yadav, V.[Vineet], Parazoo, N.[Nicholas],
A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity,
RS(12), No. 15, 2020, pp. xx-yy.
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Mao, W.[Wanliu], Lu, D.B.[De-Bin], Hou, L.[Li], Liu, X.[Xue], Yue, W.Z.[Wen-Ze],
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Mugiraneza, T.[Theodomir], Nascetti, A.[Andrea], Ban, Y.F.[Yi-Fang],
Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing,
RS(12), No. 18, 2020, pp. xx-yy.
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Liu, J.T.[Jian-Tao], Feng, Q.L.[Quan-Long], Wang, Y.[Ying], Batsaikhan, B.[Bayartungalag], Gong, J.H.[Jian-Hua], Li, Y.[Yi], Liu, C.T.[Chun-Ting], Ma, Y.[Yin],
Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework,
IJGI(9), No. 9, 2020, pp. xx-yy.
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Huang, Z.[Zhou], Qi, H.J.[Hou-Ji], Kang, C.G.[Chao-Gui], Su, Y.L.[Yue-Long], Liu, Y.[Yu],
An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data,
RS(12), No. 19, 2020, pp. xx-yy.
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Dong, X.Y.[Xuan-Yan], Xu, Y.[Yue], Huang, L.P.[Le-Ping], Liu, Z.G.[Zhi-Gang], Xu, Y.[Yi], Zhang, K.Y.[Kang-Yong], Hu, Z.W.[Zhong-Wen], Wu, G.F.[Guo-Feng],
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RS(12), No. 21, 2020, pp. xx-yy.
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Yanru, H.[Hou], Masoudi, M.[Mahyar], Chadala, A.[Agnieszka], Olszewska-Guizzo, A.[Agnieszka],
Visual Quality Assessment of Urban Scenes with the Contemplative Landscape Model: Evidence from a Compact City Downtown Core,
RS(12), No. 21, 2020, pp. xx-yy.
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Xu, F.[Fei], Somers, B.[Ben],
Unmixing-based Sentinel-2 downscaling for urban land cover mapping,
PandRS(171), 2021, pp. 133-154.
Elsevier DOI 2012
Image fusion, Sentinel-2, Urban land cover mapping, Spectral mixture analysis BibRef

Moniruzzaman, M.[Md], Thakur, P.K.[Praveen K.], Kumar, P.[Pramod], Alam, M.A.[Md. Ashraful], Garg, V.[Vaibhav], Rousta, I.[Iman], Olafsson, H.[Haraldur],
Decadal Urban Land Use/Land Cover Changes and Its Impact on Surface Runoff Potential for the Dhaka City and Surroundings Using Remote Sensing,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
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Yao, Y.[Yuan], Leung, Y.[Yee], Fung, T.[Tung], Shao, Z.F.[Zhen-Feng], Lu, J.[Jie], Meng, D.Y.[De-Yu], Ying, H.C.[Han-Chi], Zhou, Y.[Yu],
Continuous Multi-Angle Remote Sensing and Its Application in Urban Land Cover Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
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Carneiro, E.[Eduilson], Lopes, W.[Wilza], Espindola, G.[Giovana],
Urban Land Mapping Based on Remote Sensing Time Series in the Google Earth Engine Platform: A Case Study of the Teresina-Timon Conurbation Area in Brazil,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Tao, Y.Y.[Yuan-Yuan], Wang, Q.X.[Qian-Xin],
Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
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Banzhaf, E.[Ellen], Wu, W.[Wanben], Luo, X.Y.[Xiang-Yu], Knopp, J.[Julius],
Integrated Mapping of Spatial Urban Dynamics: A European-Chinese Exploration. Part 1: Methodology for Automatic Land Cover Classification Tailored towards Spatial Allocation of Ecosystem Services Features,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Teillet, C.[Claire], Pillot, B.[Benjamin], Catry, T.[Thibault], Demagistri, L.[Laurent], Lyszczarz, D.[Dominique], Lang, M.[Marc], Couteron, P.[Pierre], Barbier, N.[Nicolas], Kouassi, A.A.[Arsène Adou], Gunther, Q.[Quentin], Dessay, N.[Nadine],
Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
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Stumpf, T.[Tyler], Bigman, D.P.[Daniel P.], Day, D.J.[Dominic J.],
Mapping Complex Land Use Histories and Urban Renewal Using Ground Penetrating Radar: A Case Study from Fort Stanwix,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
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Rahimzad, M.[Maryam], Homayouni, S.[Saeid], Naeini, A.A.[Amin Alizadeh], Nadi, S.[Saeed],
An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE),
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
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He, J.[Jialyu], Li, X.[Xia], Liu, P.H.[Peng-Hua], Wu, X.X.[Xin-Xin], Zhang, J.B.[Jin-Bao], Zhang, D.C.[Da-Chuan], Liu, X.J.[Xiao-Juan], Yao, Y.[Yao],
Accurate Estimation of the Proportion of Mixed Land Use at the Street-Block Level by Integrating High Spatial Resolution Images and Geospatial Big Data,
GeoRS(59), No. 8, August 2021, pp. 6357-6370.
IEEE DOI 2108
Big Data, Feature extraction, Geospatial analysis, Remote sensing, Deep learning, Urban areas, Spatial resolution, Deep learning, remote sensing BibRef

Chen, B.[Bin], Tu, Y.[Ying], Song, Y.M.[Yi-Meng], Theobald, D.M.[David M.], Zhang, T.[Tao], Ren, Z.H.[Zhe-Hao], Li, X.C.[Xue-Cao], Yang, J.[Jun], Wang, J.[Jie], Wang, X.[Xi], Gong, P.[Peng], Bai, Y.Q.[Yu-Qi], Xu, B.[Bing],
Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America,
PandRS(178), 2021, pp. 203-218.
Elsevier DOI 2108
Land use classification, Block-level mapping, Geospatial big data, Ensemble learning, NAIP, Sentinel-1/2 BibRef

Cruz-Ramos, C.[Clara], Garcia-Salgado, B.P.[Beatriz P.], Reyes-Reyes, R.[Rogelio], Ponomaryov, V.[Volodymyr], Sadovnychiy, S.[Sergiy],
Gabor Features Extraction and Land-Cover Classification of Urban Hyperspectral Images for Remote Sensing Applications,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
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Su, Y.[Yu], Zhong, Y.F.[Yan-Fei], Zhu, Q.Q.[Qi-Qi], Zhao, J.[Ji],
Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets,
PandRS(179), 2021, pp. 50-65.
Elsevier DOI 2108
Urban scene understanding, Points of interest, High-resolution remote sensing imagery, Urban planning BibRef

Kuras, A.[Agnieszka], Brell, M.[Maximilian], Rizzi, J.[Jonathan], Burud, I.[Ingunn],
Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Bühler, M.M.[Michael Max], Sebald, C.[Christoph], Rechid, D.[Diana], Baier, E.[Eberhard], Michalski, A.[Alexander], Rothstein, B.[Benno], Nübel, K.[Konrad], Metzner, M.[Martin], Schwieger, V.[Volker], Harrs, J.A.[Jan-Albrecht], Jacob, D.[Daniela], Köhler, L.[Lothar], in het Panhuis, G.[Gunnar], Tejeda, R.C.R.[Raymundo C. Rodríguez], Herrmann, M.[Michael], Buziek, G.[Gerd],
Application of Copernicus Data for Climate-Relevant Urban Planning Using the Example of Water, Heat, and Vegetation,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Khikmah, F.[Fithrothul], Sebald, C.[Christoph], Metzner, M.[Martin], Schwieger, V.[Volker],
Modelling Vegetation Health and Its Relation to Climate Conditions Using Copernicus Data in the City of Constance,
RS(16), No. 4, 2024, pp. 691.
DOI Link 2402
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Cai, Z.[Zhi], Sun, G.[Gongyu], Su, X.[Xing], Li, T.[Tong], Guo, L.M.[Li-Min], Ding, Z.M.[Zhi-Ming],
Visual Analysis of Land Use Characteristics Around Urban Rail Transit Stations,
ITS(22), No. 10, October 2021, pp. 6221-6231.
IEEE DOI 2110
Rails, Data visualization, Visualization, Public transportation, Urban areas, Correlation, Visual analysis, land use, skyline query BibRef

Yan, Y.X.[Yu-Xiang], Yu, X.W.[Xian-Wen], Long, F.Y.[Feng-Yang], Dong, Y.F.[Yan-Feng],
A Multi-Criteria Evaluation of the Urban Ecological Environment in Shanghai Based on Remote Sensing,
IJGI(10), No. 10, 2021, pp. xx-yy.
DOI Link 2110
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Tu, Y.[Ying], Chen, B.[Bin], Lang, W.[Wei], Chen, T.T.[Ting-Ting], Li, M.[Miao], Zhang, T.[Tao], Xu, B.[Bing],
Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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He, D.[Da], Shi, Q.[Qian], Liu, X.P.[Xiao-Ping], Zhong, Y.F.[Yan-Fei], Zhang, X.[Xinchang],
Deep Subpixel Mapping Based on Semantic Information Modulated Network for Urban Land Use Mapping,
GeoRS(59), No. 12, December 2021, pp. 10628-10646.
IEEE DOI 2112
Semantics, Remote sensing, Image restoration, Spatial resolution, Training, Superresolution, Data models, Deep learning, urban land use mapping BibRef

Huang, X.[Xin], Li, S.[Shuang], Li, J.Y.[Jia-Yi], Jia, X.P.[Xiu-Ping], Li, J.[Jun], Zhu, X.X.[Xiao Xiang], Benediktsson, J.A.[Jón Atli],
A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas,
GeoRS(59), No. 12, December 2021, pp. 10266-10285.
IEEE DOI 2112
Satellites, Feature extraction, Tensors, Remote sensing, Urban areas, Convolutional neural networks, Streaming media, tensor BibRef

Ling, J.[Jing], Zhang, H.S.[Hong-Sheng], Lin, Y.[Yinyi],
Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Cheng, L.X.[Lu-Xiao], Feng, R.[Ruyi], Wang, L.[Lizhe],
Fractal Characteristic Analysis of Urban Land-Cover Spatial Patterns with Spatiotemporal Remote Sensing Images in Shenzhen City (1988-2015),
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Guan, J.Y.[Jing-Yun], Yao, J.Q.[Jun-Qiang], Li, M.[Moyan], Zheng, J.H.[Jiang-Hua],
Assessing the Spatiotemporal Evolution of Anthropogenic Impacts on Remotely Sensed Vegetation Dynamics in Xinjiang, China,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Ji, C.[Chaonan], Jilge, M.[Marianne], Heiden, U.[Uta], Stellmes, M.[Marion], Feilhauer, H.[Hannes],
Sampling Robustness in Gradient Analysis of Urban Material Mixtures,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Systematics, Urban areas, Robustness, Spatial resolution, Principal component analysis, Loading, Ecosystems, urban mapping BibRef

Georganos, S.[Stefanos], Abascal, A.[Angela], Kuffer, M.[Monika], Wang, J.[Jiong], Owusu, M.[Maxwell], Wolff, E.[Eléonore], Vanhuysse, S.[Sabine],
Is It All the Same? Mapping and Characterizing Deprived Urban Areas Using WorldView-3 Superspectral Imagery. A Case Study in Nairobi, Kenya,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Petrushevsky, N.[Naomi], Manzoni, M.[Marco], Monti-Guarnieri, A.[Andrea],
Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Hofierka, J.[Jaroslav], Onacillová, K.[Katarína],
Estimating Visible Band Albedo from Aerial Orthophotographs in Urban Areas,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Deng, Y.W.[Ya-Wen], Jiang, W.G.[Wei-Guo], Wu, Z.F.[Zhi-Feng], Ling, Z.Y.[Zi-Yan], Peng, K.F.[Kai-Feng], Deng, Y.[Yue],
Assessing Surface Water Losses and Gains under Rapid Urbanization for SDG 6.6.1 Using Long-Term Landsat Imagery in the Guangdong-Hong Kong-Macao Greater Bay Area, China,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Huang, F.[Fang], Peng, S.Y.[Shu-Ying], Chen, S.Y.[Sheng-Yi], Cao, H.X.[Hong-Xia], Ma, N.[Ning],
VO-LVV: A Novel Urban Regional Living Vegetation Volume Quantitative Estimation Model Based on the Voxel Measurement Method and an Octree Data Structure,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Dong, S.W.[Shi-Wei], Guo, H.[Hui], Chen, Z.Y.[Zi-Yue], Pan, Y.C.[Yu-Chun], Gao, B.B.[Bing-Bo],
Spatial Stratification Method for the Sampling Design of LULC Classification Accuracy Assessment: A Case Study in Beijing, China,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Yang, L.Q.[Liu-Qing], Yu, K.Y.[Kun-Yong], Ai, J.W.[Jing-Wen], Liu, Y.F.[Yan-Fen], Yang, W.[Wufa], Liu, J.[Jian],
Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Lu, W.[Wei], Li, Y.C.[Yue-Chen], Zhao, R.[Rongkun], Wang, Y.[Yue],
Using Remote Sensing to Identify Urban Fringe Areas and Their Spatial Pattern of Educational Resources: A Case Study of the Chengdu-Chongqing Economic Circle,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Elamin, A.[Ahmed], El-Rabbany, A.[Ahmed],
UAV-Based Multi-Sensor Data Fusion for Urban Land Cover Mapping Using a Deep Convolutional Neural Network,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Shan, Y.F.[Yun-Feng], Dai, X.A.[Xiao-Ai], Li, W.[Weile], Yang, Z.C.[Zhi-Chong], Wang, Y.L.[You-Lin], Qu, G.[Ge], Liu, W.X.[Wen-Xin], Ren, J.S.[Jia-Shun], Li, C.[Cheng], Liang, S.[Shuneng], Zeng, B.Y.[Bin-Yang],
Detecting Spatial-Temporal Changes of Urban Environment Quality by Remote Sensing-Based Ecological Indices: A Case Study in Panzhihua City, Sichuan Province, China,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Liu, W.[Wei], Yang, J.[Jie], Gong, Y.[Yue], Cheng, Q.[Qi],
An Evaluation of Urban Renewal Based on Inclusive Development Theory: The Case of Wuhan, China,
IJGI(11), No. 11, 2022, pp. xx-yy.
DOI Link 2212
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Zhao, D.J.[Dan-Jing], Ji, L.[Linna], Yang, F.B.[Feng-Bao], Liu, X.X.[Xiao-Xia],
A Possibility-Based Method for Urban Land Cover Classification Using Airborne Lidar Data,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Chai, B.[Baohui], Li, P.J.[Pei-Jun],
An ensemble method for monitoring land cover changes in urban areas using dense Landsat time series data,
PandRS(195), 2023, pp. 29-42.
Elsevier DOI 2301
Land cover change monitoring, Urbanization, Time series analysis, Spatio-temporal analysis BibRef

Ji, Y.Y.[Ying-Ying], Zhan, W.F.[Wen-Feng], Du, H.L.[Hui-Lin], Wang, S.S.[Sha-Sha], Li, L.[Long], Xiao, J.F.[Jing-Feng], Liu, Z.H.[Zi-Han], Huang, F.[Fan], Jin, J.X.[Jia-Xin],
Urban-rural gradient in vegetation phenology changes of over 1500 cities across China jointly regulated by urbanization and climate change,
PandRS(205), 2023, pp. 367-384.
Elsevier DOI 2311
Urban vegetation phenology, Long-term changes, Urban-rural gradient, Urbanization, Climate change, Enhanced vegetation index BibRef

Traore, M., Ndepete, C.P., Zaguy-Guerembo, R.L., Pour, A.B.,
Assessment of Land Use/land Cover Change Mapping In Bangui City Using Remote Sensing and GIS Techniques,
ISPRS20(B3:1651-1656).
DOI Link 2012
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Amarsaikhan, D.,
Advanced Classification of Optical and Sar Images for Urban Land Cover Mapping,
ISPRS20(B3:1417-1421).
DOI Link 2012
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Tilak, T., Braun, A., Chandler, D., David, N., Galopin, S., Lombard, A., Michaud, M., Parisel, C., Porte, M., Robert, M.,
Very High Resolution Land Cover Mapping of Urban Areas At Global Scale With Convolutional Neural Networks,
ISPRS20(B3:201-208).
DOI Link 2012
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Uçar, Z., Akay, A.E., Bilici, E.,
Towards Green Smart Cities: Importance of Urban Forestry and Urban Vegetation,
SmartCityApp20(399-403).
DOI Link 2012
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Alvarado-Robles, G.[Gilberto], Terol-Villalobos, I.R.[Ivan R.], Garduño-Ramon, M.A.[Marco A.], Morales-Hernandez, L.A.[Luis A.],
Segmentation of Green Areas Using Bivariate Histograms Based in Hue-Saturation Type Color Spaces,
CIAP17(II:277-287).
Springer DOI 1711
Urban area vegetation. BibRef

Salberg, A.B.[Arnt-Børre], Trier, Ø.D.[Øivind Due], Kampffmeyer, M.[Michael],
Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks,
SCIA17(II: 193-204).
Springer DOI 1706
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Kampffmeyer, M.[Michael], Salberg, A.B.[Arnt-Børre], Jenssen, R.[Robert],
Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks,
SatStreet16(680-688)
IEEE DOI 1612
BibRef

Zeng, Y., Huang, W., Jin, W., Li, S.,
Multi-agent Based Simulation Of Optimal Urban Land Use Allocation In The Middle Reaches Of The Yangtze River, China,
ISPRS16(B8: 1089-1092).
DOI Link 1610
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Willkomm, M., Dannenberg, P.,
Monitoring Land Use Dynamics Of Peri-urban Agricultutre In Central Kenya With Rapideye Satellite Imagery,
ISPRS16(B8: 1079-1081).
DOI Link 1610
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Li, F.[Feng], Han, L.[Liu], Zhu, L.J.[Liu-Jun], Huang, Y.Y.[Yin-You], Song, G.[Guo],
Urban Vegetation Mapping Based On The Hj-1 Ndvi Reconstrction,
ISPRS16(B8: 867-871).
DOI Link 1610
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Roychowdhury, K.,
Comparison Between Spectral, Spatial And Polarimetric Classification Of Urban And Periurban Landcover Using Temporal Sentinel: 1 Images,
ISPRS16(B7: 789-796).
DOI Link 1610
Ths 4: Tandem-x BibRef

Zhang, Y., Qin, K., Zeng, C., Zhang, E.B., Yue, M.X., Tong, X.,
A Data Field Method For Urban Remotely Sensed Imagery Classification Considering Spatial Correlation,
ISPRS16(B7: 431-435).
DOI Link 1610
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Yao, W., Poleswki, P., Krzystek, P.,
Classification Of Urban Aerial Data Based On Pixel Labelling With Deep Convolutional Neural Networks And Logistic Regression,
ISPRS16(B7: 405-410).
DOI Link 1610
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Manzke, N.[Nina], Kada, M.[Martin], Kastler, T.[Thomas], Xu, S.J.[Shao-Juan], de Lange, N.[Norbert], Ehlers, M.[Manfred],
The Urbis Project: Identification And Characterization Of Potential Urban Development Areas As A Web-based Service,
ISPRS16(B4: 227-233).
DOI Link 1610
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Zou, X.L.[Xiao-Liang], Zhao, G.H.[Gui-Hua], Li, J.[Jonathan], Yang, Y.X.[Yuan-Xi], Fang, Y.[Yong],
Object Based Image Analysis Combining High Spatial Resolution Imagery And Laser Point Clouds For Urban Land Cover,
ISPRS16(B3: 733-739).
DOI Link 1610
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Peng, F.F.[Fei-Fei], Gong, J.Y.[Jian-Ya], Wang, L.[Le], Wu, H.Y.[Hua-Yi], Yang, J.[Jiansi],
Impact Of Building Heights On 3d Urban Density Estimation From Spaceborne Stereo Imagery,
ISPRS16(B3: 677-683).
DOI Link 1610
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Volpi, M.[Michele], Ferrari, V.[Vittorio],
Semantic segmentation of urban scenes by learning local class interactions,
EarthObserv15(1-9)
IEEE DOI 1510
Image segmentation BibRef

Kumar, U., Milesi, C., Nemani, R.R., Basu, S.,
Multi-sensor multi-resolution image fusion for improved vegetation and urban area classification,
IWIDF15(51-58).
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Urban Green Space Mapping, Parks, Detection, Analysis .