23.2.27 Classification for Urban Area Land Cover, Remote Sensing

Chapter Contents (Back)
Classification. Land Cover, Urban. Remote Sensing. Urban Area. Urban Land Cover.
See also Urban Heat Islands, Remote Sensing.
See also General Urban Area Detection, Built-Up Area Detection.
See also Urban Green Space Mapping, Parks, Detection, Analysis.

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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

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Huang, X.[Xin], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
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Huang, X.[Xin], Zhang, L.P.[Liang-Pei],
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Huang, X.[Xin], Zhang, L.P.[Liang-Pei],
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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,
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IEEE DOI 1710
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Scene Classification Based On The Semantic-feature Fusion Fully Sparse Topic Model For High Spatial Resolution Remote Sensing Imagery,
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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,
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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,
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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,
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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,
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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,
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IEEE DOI 0704
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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.
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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.
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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,
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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,
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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
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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
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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
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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
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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
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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
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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.
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Ogashawara, I., Bastos, V.,
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LiDAR; Landsat; Fusion; Land cover; Large-area assessment; Mapping accuracy; Managed clearings BibRef

Pan, G., Qi, G., Wu, Z., Zhang, D., Li, S.,
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IEEE DOI 1303
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IEEE DOI 1304
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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],
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PandRS(83), No. 1, 2013, pp. 40-49.
Elsevier DOI 1308
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Belgiu, M.[Mariana], Dragut, L.[Lucian], Strobl, J.[Josef],
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Elsevier DOI 1402
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Meganem, I., Deliot, P., Briottet, X., Deville, Y., Hosseini, S.,
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Wieland, M.[Marc], Pittore, M.[Massimiliano],
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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,
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Urban land cover BibRef

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Yang, J.X.[Jin-Xin], Wong, M.S.[Man Sing], Menenti, M.[Massimo], Nichol, J.[Janet],
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IEEE DOI 1506
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PandRS(107), No. 1, 2015, pp. 99-111.
Elsevier DOI 1508
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PandRS(119), No. 1, 2016, pp. 347-360.
Elsevier DOI 1610
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Momeni, R.[Rahman], Aplin, P.[Paul], Boyd, D.S.[Doreen S.],
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Robust Detection of Single and Double Persistent Scatterers in Urban Built Environments,
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Yang, Y.[Yetao], Wang, Y.[Yi], Wu, K.[Ke], Yu, X.[Xin],
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Xiang, D.L.[De-Liang], Tang, T.[Tao], Ban, Y.F.[Yi-Fang], Su, Y.[Yi], Kuang, G.Y.[Gang-Yao],
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IEEE DOI 1906
Synthetic aperture radar, Minimization, Image denoising, Speckle, Convex functions, Noise measurement, Optimization, weighted nuclear norm BibRef

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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

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Karalas, K.[Konstantinos], Tsagkatakis, G.[Grigorios], Zervakis, M.[Michael], Tsakalides, P.[Panagiotis],
Land Classification Using Remotely Sensed Data: Going Multilabel,
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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,
RS(8), No. 9, 2016, pp. 754.
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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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,
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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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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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High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field,
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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],
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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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Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery,
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An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China,
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Huang, X.[Xin], Chen, H.J.[Hui-Jun], Gong, J.Y.[Jian-Ya],
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Elsevier DOI 1712
Multi-angle, Urban classification, High spatial resolution, Scene classification BibRef

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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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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

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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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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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Garg, A., Singh, D.,
Development of an Efficient Contextual Algorithm for Discrimination of Tall Vegetation and Urban for PALSAR Data,
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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],
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Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics,
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Ex Post Impact Assessment of Master Plans: The Case of Shenzhen in Shaping a Polycentric Urban Structure,
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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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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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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],
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Feng, Q.L.[Quan-Long], Zhu, D.[Dehai], Yang, J.Y.[Jian-Yu], Li, B.G.[Bao-Guo],
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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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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],
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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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PandRS(154), 2019, pp. 202-215.
Elsevier DOI 1907
Annual land cover change detection, Spatio-temporal land cover filter, Urban area BibRef

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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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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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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.
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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,
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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.
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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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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,
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Gašparovic, M.[Mateo], Dobrinic, D.[Dino],
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],
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DOI Link 2007
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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,
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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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RS(12), No. 17, 2020, pp. xx-yy.
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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.
DOI Link 2009
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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.
DOI Link 2009
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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.
DOI Link 2010
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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],
Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data,
RS(12), No. 21, 2020, pp. xx-yy.
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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
BibRef

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
BibRef

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.[Zihan], 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


Nizalapur, V., Vyas, A.,
Texture Analysis for Land Use Land Cover (LULC) Classification In Parts Of Ahmedabad, Gujarat,
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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).
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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).
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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).
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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,
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Springer DOI 1711
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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
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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
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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).
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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).
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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).
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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).
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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,
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Yao, W., Poleswki, P., Krzystek, P.,
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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).
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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).
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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).
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Kumar, U., Milesi, C., Nemani, R.R., Kumar Raja, S., Ganguly, S., Wang, W.,
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buildings, vehicles, trees and roads without RGB. BibRef

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Bechtel, B., Langkamp, T., Böhner, J., Daneke, C., Ossenbrügge, J., Schempp, S.,
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Guan, H., Yu, J., Li, J., Luo, L.,
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Hermosilla, T.[Txomin], Ruiz, L.A.[Luis A.], Recio, J.A., Cambra López, M.,
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Urban Green Space Mapping, Parks, Detection, Analysis .


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