Classification for Urban Area Land Cover, Remote Sensing

Chapter Contents (Back)
Classification. Remote Sensing. Urban Area. See also Urban Heat Islands, Surface Temperature, Remote Sensing. See also General Urban Area Detection, Built-Up Area Detection.

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

Heikkonen, J.[Jukka], Varfis, A.[Aristide], Kanellopoulos, I.[Ioannis],
A Method for Remote Sensing Based Classification of Urban Areas,
HTML Version. 9705

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

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

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.

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.

Zhu, Q.[Qiqi], 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.
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.[Qiqi], 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.
Feature extraction, Semantics, Visualization, Adaptation models, Probabilistic logic, Remote sensing, Encoding, Adaptive, scene classification BibRef

Zhu, Q.[Qiqi], 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.
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.
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

Zhong, Y.F.[Yan-Fei], Zhu, Q.[Qiqi], 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.
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

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

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.[Qiqi], 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

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.
evolutionary computation 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.
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.

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.
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.
geophysical image processing, geophysical techniques, image resolution, image segmentation, remote sensing, HCRF model, remote sensing 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.
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.

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

Walton, J.T.[Jeffrey T.],
Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forests, and Support Vector Regression,
PhEngRS(74), No. 10, October 2008, pp. 1213-1222.
WWW Link. 0804
Three machine learning subpixel estimation methods were applied to estimate urban cover and the resulting predictions were compared based on accuracy. 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

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

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

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

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

Hartfield, K., Landau, K., Leeuwen, W.,
Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat,
RS(3), No. 11, November 2011, pp. 2364-2383.
DOI Link 1203

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

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.

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

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
Maximum Likelihood Classification of High-Resolution Polarimetric SAR Images in Urban Area,
PDF File. 1106

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

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.

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.

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.
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

Wu, H.[Hao], Sun, Y.[Yurong], 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

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.
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

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

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

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

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

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

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

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

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

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.
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

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

Blaschke, T.[Thomas], Hay, G.J.[Geoffrey J.], Weng, Q.[Qihao], 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.
land use BibRef

Matasci, G.[Giona], Longbotham, N.[Nathan], Pacifici, F.[Fabio], Kanevski, M.[Mikhail], Tuia, D.[Devis],
Understanding angular effects in VHR imagery and their significance for urban land-cover model portability: A study of two multi-angle in-track image sequences,
PandRS(107), No. 1, 2015, pp. 99-111.
Elsevier DOI 1508
Image classification BibRef

Wu, W.[Wenjin], Guo, H.[Huadong], Li, X.[Xinwu], Ferro-Famil, L., Zhang, L.[Lu],
Urban Land Use Information Extraction Using the Ultrahigh-Resolution Chinese Airborne SAR Imagery,
GeoRS(53), No. 10, October 2015, pp. 5583-5599.
Gaussian distribution BibRef

Calegari, G.R.[Gloria Re], Carlino, E.[Emanuela], Peroni, D.[Diego], Celino, I.[Irene],
Extracting Urban Land Use from Linked Open Geospatial Data,
IJGI(4), No. 4, 2015, pp. 2109.
DOI Link 1511

Zhang, Q.[Qian], Qin, R.J.[Rong-Jun], Huang, X.[Xin], Fang, Y.[Yong], Liu, L.[Liang],
Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile,
RS(7), No. 12, 2015, pp. 15840.
DOI Link 1601
Dealing with high resolution for classification. BibRef

Comber, A.J.[Alexis J.], Harris, P.[Paul], Tsutsumida, N.[Narumasa],
Improving land cover classification using input variables derived from a geographically weighted principal components analysis,
PandRS(119), No. 1, 2016, pp. 347-360.
Elsevier DOI 1610
GWmodel BibRef

Momeni, R.[Rahman], Aplin, P.[Paul], Boyd, D.S.[Doreen S.],
Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach,
RS(8), No. 2, 2016, pp. 88.
DOI Link 1603

Ma, P., Lin, H.,
Robust Detection of Single and Double Persistent Scatterers in Urban Built Environments,
GeoRS(54), No. 4, April 2016, pp. 2124-2139.
Interferometry BibRef

Yang, Y.[Yetao], Wang, Y.[Yi], Wu, K.[Ke], Yu, X.[Xin],
Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City,
RS(8), No. 4, 2016, pp. 304.
DOI Link 1604

Xiang, D.L.[De-Liang], Tang, T.[Tao], Ban, Y.F.[Yi-Fang], Su, Y.[Yi], Kuang, G.Y.[Gang-Yao],
Unsupervised Polarimetric SAR Urban Area Classification Based on Model-Based Decomposition with Cross Scattering,
PandRS(116), No. 1, 2016, pp. 86-100.
Elsevier DOI 1604
Cross scattering matrix See also Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence. BibRef

Guan, D.D.[Dong-Dong], Xiang, D.L.[De-Liang], Tang, X.A.[Xiao-An], Kuang, G.Y.[Gang-Yao],
SAR Image Despeckling Based on Nonlocal Low-Rank Regularization,
GeoRS(57), No. 6, June 2019, pp. 3472-3489.
Synthetic aperture radar, Minimization, Image denoising, Speckle, Convex functions, Noise measurement, Optimization, weighted nuclear norm BibRef

Xiang, D.L.[De-Liang], Ban, Y.F.[Yi-Fang], Wang, W.[Wei], Su, Y.[Yi],
Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model,
GeoRS(55), No. 6, June 2017, pp. 3115-3131.
Atmospheric modeling, Covariance matrices, Estimation, Image edge detection, Image segmentation, Synthetic aperture radar, Urban areas, Edge detection, polarimetric synthetic aperture radar (PolSAR), simple linear iterative clustering (SLIC), spherically invariant random vector (SIRV), superpixel BibRef

Li, X.[Xueke], Wu, T.X.[Tai-Xia], Liu, K.[Kai], Li, Y.[Yao], Zhang, L.F.[Li-Fu],
Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification,
RS(8), No. 5, 2016, pp. 438.
DOI Link 1606

Karalas, K.[Konstantinos], Tsagkatakis, G.[Grigorios], Zervakis, M.[Michael], Tsakalides, P.[Panagiotis],
Land Classification Using Remotely Sensed Data: Going Multilabel,
GeoRS(54), No. 6, June 2016, pp. 3548-3563.
feature extraction BibRef

Kim, Y.M.[Yong-Min],
Generation of Land Cover Maps through the Fusion of Aerial Images and Airborne LiDAR Data in Urban Areas,
RS(8), No. 6, 2016, pp. 521.
DOI Link 1608

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,
RS(8), No. 9, 2016, pp. 716.
DOI Link 1610

Zhang, Q.[Qi], Xin, J.[Jinyuan], Yin, Y.[Yan], Wang, L.[Lili], 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,
RS(8), No. 9, 2016, pp. 754.
DOI Link 1610

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,
RS(8), No. 10, 2016, pp. 845.
DOI Link 1609

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
Urban land use BibRef

Schreyer, J.[Johannes], Lakes, T.[Tobia],
Deriving and Evaluating City-Wide Vegetation Heights from a TanDEM-X DEM,
RS(8), No. 11, 2016, pp. 940.
DOI Link 1612

Gervais, N.[Norman], Buyantuev, A.[Alexander], Gao, F.[Feng],
Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702

Lu, Y.[Yuhao], Coops, N.C.[Nicholas C.], Hermosilla, T.[Txomin],
Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data,
PandRS(126), No. 1, 2017, pp. 11-23.
Elsevier DOI 1704
Time series BibRef

Susaki, J.[Junichi], Kubota, S.[Seiya],
Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

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,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

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,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Grippa, T.[Taïs], Lennert, M.[Moritz], Beaumont, B.[Benjamin], Vanhuysse, S.[Sabine], Stephenne, N.[Nathalie], Wolff, E.[Eléonore],
An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Simwanda, M.[Matamyo], Murayama, Y.[Yuji],
Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia,
IJGI(6), No. 4, 2017, pp. xx-yy.
DOI Link 1705

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,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Tsou, J.[Jinyeu], 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,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Chen, T.[Tao], Zhang, X.[Xujia], Niu, R.[Ruiqing],
The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Degerickx, J.[Jeroen], Okujeni, A.[Akpona], Iordache, M.D.[Marian-Daniel], Hermy, M.[Martin], van der Linden, S.[Sebastian], Somers, B.[Ben],
A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Sun, X.F.[Xiao-Feng], Lin, X.G.[Xiang-Guo], Shen, S.[Shuhan], Hu, Z.Y.[Zhan-Yi],
High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field,
IJGI(6), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Liu, Y.X.[Yang-Xiaoyue], 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,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Zhong, Y.F.[Yan-Fei], Cao, Q.[Qiong], Zhao, J.[Ji], Ma, A.[Ailong], 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,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Ma, A.L.[Ai-Long], Zhong, Y.F.[Yan-Fei], He, D.[Da], Zhang, L.P.[Liang-Pei],
Multiobjective Subpixel Land-Cover Mapping,
GeoRS(56), No. 1, January 2018, pp. 422-435.
Graphical models, Hyperspectral imaging, Image resolution, Optimization methods, Multiobjective optimization, subpixel mapping (SPM) See also Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery. BibRef

Maleki, J.[Jamshid], Hakimpour, F.[Farshad], Masoumi, Z.[Zohreh],
A Parcel-Level Model for Ranking and Allocating Urban Land-Uses,
IJGI(6), No. 9, 2017, pp. xx-yy.
DOI Link 1710

Zhang, X.Y.[Xiu-Yuan], Du, S.H.[Shi-Hong], Wang, Q.[Qiao],
Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data,
PandRS(132), No. 1, 2017, pp. 170-184.
Elsevier DOI 1710
Urban functional zone BibRef

Zhang, Y.[Yuan], Li, Q.Z.[Qiang-Zi], Huang, H.P.[Hui-Ping], Wu, W.[Wei], Du, X.[Xin], Wang, H.Y.[Hong-Yan],
The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Chen, W.[Wei], Huang, H.P.[Hui-Ping], Dong, J.W.[Jin-Wei], Zhang, Y.[Yuan], Tian, Y.C.[Yi-Chen], Yang, Z.Q.[Zhi-Qi],
Social functional mapping of urban green space using remote sensing and social sensing data,
PandRS(146), 2018, pp. 436-452.
Elsevier DOI 1812
Remote sensing, Social sensing, Urban green space, Social functional mapping BibRef

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,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Xia, Z.[Zelong], Li, H.[Hao], Chen, Y.[Yuehong],
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.
DOI Link 1712

Huang, X.[Xin], Chen, H.[Huijun], 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,
IJGI(7), No. 1, 2018, pp. xx-yy.
DOI Link 1801

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,
IJGI(7), No. 1, 2018, pp. xx-yy.
DOI Link 1801

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,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

Tu, W.[Wei], Hu, Z.W.[Zhong-Wen], Li, L.[Lefei], Cao, J.Z.[Jin-Zhou], Jiang, J.C.[Jin-Cheng], Li, Q.P.[Qiu-Ping], Li, Q.Q.[Qing-Quan],
Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

Zhang, X.Y.[Xiu-Yuan], Du, S.H.[Shi-Hong], Wang, Q.[Qiao], Zhou, W.[Weiqi],
Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

Du, S.J.[Shou-Ji], Du, S.H.[Shi-Hong], Liu, B.[Bo], Zhang, X.Y.[Xiu-Yuan],
Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909

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,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

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,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805

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,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806

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.
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,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806

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,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808

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,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808

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,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Mossoux, S.[Sophie], Kervyn, M.[Matthieu], Soulé, H.[Hamid], Canters, F.[Frank],
Mapping Population Distribution from High Resolution Remotely Sensed Imagery in a Data Poor Setting,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Sha, Z.Y.[Zong-Yao], Ali, Y.Y.[Yah-Ya], Wang, Y.W.[Yu-Wei], Chen, J.P.[Jiang-Ping], Tan, X.C.[Xi-Cheng], Li, R.[Ruren],
Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811

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,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Huang, C.[Conghong], Yang, J.[Jun], Jiang, P.[Peng],
Assessing Impacts of Urban Form on Landscape Structure of Urban Green Spaces in China Using Landsat Images Based on Google Earth Engine,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Feng, Y.[Yanlei], Qi, Y.[Yi],
Modeling Patterns of Land Use in Chinese Cities Using an Integrated Cellular Automata Model,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Song, J.C.[Jin-Chao], Lin, T.[Tao], Li, X.[Xinhu], Prishchepov, A.V.[Alexander V.],
Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Xiao, P.F.[Peng-Feng], Zhang, X.L.[Xue-Liang], Zhang, H.M.[Hong-Min], Hu, R.[Rui], Feng, X.Z.[Xue-Zhi],
Multiscale Optimized Segmentation of Urban Green Cover in High Resolution Remote Sensing Image,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Zhang, X.Y.[Xiao-Yi], Li, W.W.[Wen-Wen], Zhang, F.[Feng], Liu, R.[Renyi], Du, Z.H.[Zhen-Hong],
Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Faridatul, M.I.[Mst Ilme], Wu, B.[Bo],
Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link 1901

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,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

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,
RS(11), No. 1, 2019, pp. xx-yy.
DOI Link 1901

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,
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901

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,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902

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,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903

Kranjcic, N.[Nikola], Medak, D.[Damir], Župan, R.[Robert], Rezo, M.[Milan],
Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

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,
PandRS(150), 2019, pp. 59-69.
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.[Liwei], She, J.F.[Jiang-Feng],
An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903

Zhao, W.Z.[Wen-Zhi], Bo, Y.C.[Yan-Chen], Chen, J.[Jiage], Tiede, D.[Dirk], Blaschke, T.[Thomas], Emery, W.J.[William J.],
Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM),
PandRS(151), 2019, pp. 237-250.
Elsevier DOI 1904
Semantic classification, Urban scene recognition, Deep learning, High-resolution imagery, OpenStreetMap (OSM), Data fusion BibRef

Zhang, A.[Aizhu], Zhang, S.[Shuang], Sun, G.Y.[Gen-Yun], Li, F.[Feng], Fu, H.[Hang], Zhao, Y.H.[Yun-Hua], Huang, H.[Hui], Cheng, J.[Ji], Wang, Z.J.[Zhen-Jie],
Mapping of Coastal Cities Using Optimized Spectral-Spatial Features Based Multi-Scale Superpixel Classification,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905

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.[Zhili], Chen, X.[Xin],
City-Level Comparison of Urban Land-Cover Configurations from 2000-2015 across 65 Countries within the Global Belt and Road,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907

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,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Mears, M.[Meghann], Brindley, P.[Paul],
Measuring Urban Greenspace Distribution Equity: The Importance of Appropriate Methodological Approaches,
IJGI(8), No. 6, 2019, pp. xx-yy.
DOI Link 1908

Mugiraneza, T.[Theodomir], Nascetti, A.[Andrea], Ban, Y.[Yifang],
WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Ilehag, R.[Rebecca], Schenk, A.[Andreas], Huang, Y.[Yilin], Hinz, S.[Stefan],
KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Zhou, Q.[Qian], Zhao, X.[Xiang], Wu, D.H.[Dong-Hai], Tang, R.[Rongyun], Du, X.Z.[Xiao-Zheng], Wang, H.[Haoyu], Zhao, J.[Jiacheng], Xu, P.[Peipei], 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

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

Shi, Y.[Yan], Qi, Z.[Zhixin], 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

Khademi, S., Norouzi, M., Hashemi, M.,
Sustainable Land Use Evaluation Based On Preservative Approach,
DOI Link 1912
The sustainable development approach emphasizes the efficient use of land. BibRef

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,
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,
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Springer DOI 1706

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,

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

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

Li, F.[Feng], Han, L.[Liu], Liujun, Z.[Zhu], Yinyou, H.[Huang], Song, G.[Guo],
Urban Vegetation Mapping Based On The Hj-1 Ndvi Reconstrction,
ISPRS16(B8: 867-871).
DOI Link 1610

Roychowdhury, K.,
Comparison Between Spectral, Spatial And Polarimetric Classification Of Urban And Periurban Landcover Using Temporal Sentinel: 1 Images,
ISPRS16(B7: 789-796).
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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

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

Manzke, N.[Nina], Kada, M.[Martin], Kastler, T.[Thomas], Xu, S.[Shaojuan], 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).
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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

Peng, F.F.[Fei-Fei], Gong, J.Y.[Jian-Ya], Wang, L.[Le], Wu, H.[Huayi], Yang, J.[Jiansi],
Impact Of Building Heights On 3d Urban Density Estimation From Spaceborne Stereo Imagery,
ISPRS16(B3: 677-683).
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Volpi, M.[Michele], Ferrari, V.[Vittorio],
Semantic segmentation of urban scenes by learning local class interactions,
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Kumar, U., Milesi, C., Nemani, R.R., Basu, S.,
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DOI Link 1508

Kumar, U., Milesi, C., Nemani, R.R., Kumar Raja, S., Ganguly, S., Wang, W.,
Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data,
DOI Link 1508

Sakurada, K.[Ken], Okatani, T.[Takayuki], Kitani, K.M.[Kris M.],
Massive City-Scale Surface Condition Analysis Using Ground and Aerial Imagery,
ACCV14(I: 49-64).
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Badawy, H.M., Moussa, A., El-Sheimy, N.,
Automatic Classification of coarse density LiDAR data in urban area,
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buildings, vehicles, trees and roads without RGB. BibRef

Kouchi, H.S.[H. Salimi], Sahebi, M.R., Abkar, A.A., Valadan Zoej, M.J.,
Fractional Vegetation Cover Estimation In Urban Environments,
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Tompalski, P., Wezyk, P.,
LIDAR and VHRS Data for Assessing Living Quality in Cities: An Approach Based on 3D Spatial Indices,
DOI Link 1209

Shekhar, S.,
Detecting Slums From Quick Bird Data In Pune Using An Object Oriented Approach,
DOI Link 1209

Bechtel, B., Langkamp, T., Böhner, J., Daneke, C., Ossenbrügge, J., Schempp, S.,
Classification and Modelling of Urban Micro-Climates Using Multisensoral and Multitemporal Remote Sensing Data,
DOI Link 1209

Guan, H., Yu, J., Li, J., Luo, L.,
Random Forests-based Feature Selection For Land-Use Classification Using Lidar Data And Orthoimagery,
DOI Link 1209

Shi, L.,
The Low Backscattering Targets Classification In Urban Areas,
AnnalsPRS(I-7), No. 2012, pp. 171-176.
HTML Version. 1209

Walde, I., Hese, S., Berger, C., Schmullius, C.,
Graph-based Urban Land Use Mapping From High Resolution Satellite Images,
AnnalsPRS(I-4), No. 2012, pp. 119-124.
HTML Version. 1209

Jiang, L., Gu, J., Chen, X., You, Y., Tang, Q.,
A Study Of Urban Intensive Land Evaluating System,
AnnalsPRS(I-4), No. 2012, pp. 19-22.
HTML Version. 1209

Elsharkawy, A., Elhabiby, M., El-sheimy, N.,
New Combined Pixel/object-based Technique For Efficient Urban Classsification Using Worldview-2 Data,
DOI Link 1209

Bekkari, A.[Aissam], Idbraim, S.[Soufiane], Elhassouny, A.[Azeddine], Mammass, D.[Driss], El yassa, M.[Mostafa], Ducrot, D.[Danielle],
SVM and Haralick Features for Classification of High Resolution Satellite Images from Urban Areas,
Springer DOI 1208

Le Bris, A., Robert-Sainte, P.,
Classification of Roof Materials for Rainwater Pollution Modelization,
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Hese, S., Voltersen, M., Lindner, M., Berger, C.,
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Hermosilla, T.[Txomin], Ruiz, L.A.[Luis A.], Recio, J.A., Balsa-Barreiro, J.,
Land-use Mapping of Valencia City Area from Aerial Images and LiDAR Data,
WWW Link. Remote Sensing, Classification, Urban areas 1204

Hermosilla, T.[Txomin], Ruiz, L.A.[Luis A.], Recio, J.A., Cambra López, M.,
Efficiency of Context-Based Attributes for Land Use Classification of Urban Environments,
PDF File. 1106

Kux, H.J.H., Novack, T., Ferreira, R., Oliveira, D.A.,
Urban Land Cover Classification Using Optical VHR Data and the Knowledge-Based System Interimage,
PDF File. 1007

Novack, T., Kux, H.J.H., Feitosa, R.Q., Costa, G.A.,
Per Block Urban Land Use Interpretation Using Optical VHR Data and the Knowledge-Based System Interimage,
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Cui, H.S.[Hai-Shan], Qian, H.S.[Huai-Sui], Qian, L.X.[Le-Xiang], Li, Y.[Ying],
Remote Sensing Experts Classification System Applying in the Land Use Classification in Guangzhou City,

Mavrantza, O.D., Argialas, D.P.,
Identification of Urban Features Using Object-Oriented Image Analysis,
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Mavrantza, O.D., Charou, E., Stefouli, M.,
Object-oriented image analysis of land cover for multi-temporal monitoring. Case study: Zakynthos Island, Greece,
PDF File. 0607

Yokota, S., Takeuchi, K.,
Study on the relationship between landscape characteristics of fragmented urban green spaces and distribution of urban butterflies - Application of object-based satellite image analysis,
PDF File. 0607

Kux, H., Araújo, E.,
Multi-temporal object-oriented classifications and analysis of Quickbird scenes at a metropolitan area in Brazil (Belo Horizonte, Minas Gerais State),
PDF File. 0607

Kux, H., Pinho, C.,
Object-oriented analysis of high-resolution satellite images for intra-urban land cover classification: case study in São José dos campos, São Paulo State, Brazil,
PDF File. 0607

Pesaresi, M.[Martino],
Textural Classification of Very High-resolution Satellite Imagery: Empirical Estimation of the Relationship Between Window Size and Detection Accuracy in Urban Environment,
IEEE DOI BibRef 9900

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Change Detection, Urban Area Land Cover, Temporal Analysis .

Last update:Dec 7, 2019 at 17:16:29