Impervious Surface Detection, Urban Area Extraction

Chapter Contents (Back)
Impervious Surface. Urban Growth. Urban Change. Aerial Image Analysis. General Detection:
See also General Urban Area Detection, Built-Up Area Detection.
See also Urban Heat Islands, Remote Sensing.

Thomas, N.[Nancy], Hendrix, C.[Chad], Congalton, R.G.[Russell G.],
A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery,
PhEngRS(69), No. 9, September 2003, pp. 963-972. An overview of a project in which one-meter digital imagery was used to produce a map of pervious and impervious surfaces to be used by the city of Scottsdale, Arizona for storm-water runoff estimation is provided.
WWW Link. 0309

Yang, L.M.[Li-Min], Xian, G.[George], Klaver, J.M.[Jacqueline M.], Deal, B.[Brian],
Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data,
PhEngRS(69), No. 9, September 2003, pp. 1003-1010. An approach was developed to detect urban land-cover changes by quantifying temporal change of an impervious surface using Landsat and high-resolution imagery. Changes are at sub-pixel level.
WWW Link. 0309

Hodgson, M.E.[Michael E.], Jensen, J.R.[John R.], Tullis, J.A.[Jason A.], Riordan, K.D.[Kevin D.], Archer, C.M.[Clark M.],
Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness,
PhEngRS(69), No. 9, September 2003, pp. 973-980.
WWW Link. 0309
A comparative analysis of pixel-level (maximum-likelihood, ISODATA) and segment-level classifiers is presented for mapping urban parcel imperviousness from color aerial photography fused with lidar-derived cover height. BibRef

McMahon, G.[Gerard],
Consequences of Land-cover Misclassification in Models of Impervious Surface,
PhEngRS(73), No. 12, December 2007, pp. 1343-1354.
WWW Link. 0712
An assessment of the effects of land-cover misclassification on models that use the National Land Cover Data (NLCD) to predict impervious surface by evaluating the consequences on model predictions of adjusting land-cover within a watershed to reflect uncertainty assessment information. BibRef

Wu, C.S.[Chang-Shan], Yuan, F.[Fei],
Seasonal Sensitivity Analysis of Impervious Surface Estimation with Satellite Imagery,
PhEngRS(73), No. 12, December 2007, pp. 1393-1402.
WWW Link. 0712
A seasonal sensitivity analysis conducted for estimating impervious surface distribution using spectral mixture analysis and regression modeling. BibRef

Yuan, F.[Fei], Wu, C.S.[Chang-Shan], Bauer, M.E.[Marvin E.],
Comparison of Spectral Analysis Techniques for Impervious Surface Estimation Using Landsat Imagery,
PhEngRS(74), No. 8, August 2008, pp. 1045-1056.
WWW Link. 0804
Three common spectral analytical techniques (regression modeling, regression tree, and normalized spectral mixture analysis) for estimation of percent impervious surface area explored and compared in terms of model accuracy, factors that influence model performance, and cost of image processing. BibRef

Weng, Q., Hu, X.,
Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN,
GeoRS(46), No. 8, August 2008, pp. 2397-2406.

Zhou, Y.Y.[Yu-Yu], Wang, Y.Q.,
Extraction of Impervious Surface Areas from High Spatial Resolution Imagery by Multiple Agent Segmentation and Classification,
PhEngRS(74), No. 7, July 2008, pp. 857-868.
WWW Link. 0804
An algorithm of multiple agent segmentation and classification (MASC) to extract impervious surface areas from truecolor digital orthophoto and spatial resolution enhanced QuickBird-2 multispectral imagery. BibRef

Luo, L.[Li], Mountrakis, G.[Giorgos],
Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification,
PandRS(66), No. 5, September 2011, pp. 579-587.
Elsevier DOI 1110
Contextual classification; Hybrid classifiers; Impervious surfaces; Landsat ETM+; Partial classification BibRef

Lu, D.S.[Deng-Sheng], Li, G.Y.[Gui-Ying], Moran, E.[Emilio], Batistella, M.[Mateus], Freitas, C.C.[Corina C.],
Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban-rural landscape in the Brazilian Amazon,
PandRS(66), No. 6, November 2011, pp. 798-808.
Elsevier DOI 1112
Landsat TM; ALOS PALSAR L-band; RADARSAT-2 C-band; Wavelet-merging technique; Spectral mixture analysis; Impervious surface BibRef

Demarchi, L.[Luca], Canters, F.[Frank], Chan, J.C.W.[Jonathan Cheung-Wai], van de Voorde, T.,
Multiple Endmember Unmixing of CHRIS/Proba Imagery for Mapping Impervious Surfaces in Urban and Suburban Environments,
GeoRS(50), No. 9, September 2012, pp. 3409-3424.

Demarchi, L.[Luca], Chan, J.C.W.[Jonathan Cheung-Wai], Ma, J.L.[Jiang-Lin], Canters, F.[Frank],
Mapping impervious surfaces from superresolution enhanced CHRIS/Proba imagery using multiple endmember unmixing,
PandRS(72), No. 1, August 2012, pp. 99-112.
Elsevier DOI 1209
Angular hyperspectral imagery; CHRIS/Proba; Superresolution; Multiple endmember unmixing; Impervious surface mapping; Urban areas BibRef

Yang, F.[Fan], Matsushita, B.[Bunkei], Fukushima, T.[Takehiko], Yang, W.[Wei],
Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan,
PandRS(72), No. 1, August 2012, pp. 90-98.
Elsevier DOI 1209
Impervious surface area; Temporal mixture analysis; MODIS; NDVI time-series BibRef

Parece, T.E.[Tammy E.], Campbell, J.B.[James B.],
Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography,
RS(5), No. 10, 2013, pp. 4942-4960.
DOI Link 1311

Deng, C.B.[Cheng-Bin], Wu, C.S.[Chang-Shan],
The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques,
PandRS(86), No. 1, 2013, pp. 100-110.
Elsevier DOI 1312
Impervious surface BibRef

Liu, X.P.[Xiao-Ping], Hu, G.H.[Guo-Hua], Ai, B.[Bin], Li, X.[Xia], Shi, Q.[Qian],
A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area,
RS(7), No. 12, 2015, pp. 15863.
DOI Link 1601

Kaspersen, P.S.[Per Skougaard], Fensholt, R.[Rasmus], Drews, M.[Martin],
Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities,
RS(7), No. 6, 2015, pp. 8224.
DOI Link 1507

Deng, C.B.[Cheng-Bin],
Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis,
RS(7), No. 7, 2015, pp. 9205.
DOI Link 1506

Guo, W.[Wei], Lu, D.S.[Deng-Sheng], Wu, Y.[Yanlan], Zhang, J.X.[Ji-Xian],
Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data,
RS(7), No. 9, 2015, pp. 12459.
DOI Link 1511

Tsutsumida, N.[Narumasa], Comber, A.J.[Alexis J.], Barrett, K.[Kirsten], Saizen, I.[Izuru], Rustiadi, E.[Ernan],
Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas,
RS(8), No. 2, 2016, pp. 143.
DOI Link 1603

Gallo, K.[Kevin], Xian, G.[George],
Changes in satellite-derived impervious surface area at US historical climatology network stations,
PandRS(120), No. 1, 2016, pp. 77-83.
Elsevier DOI 1610
Impervious surface area BibRef

Zhang, L.[Lei], Weng, Q.H.[Qi-Hao],
Annual Dynamics of Impervious Surface in the Pearl River Delta, China, from 1988 to 2013, Using Time Series Landsat Imagery,
PandRS(113), No. 1, 2016, pp. 86-96.
Elsevier DOI 1602
Landsat BibRef

Li, L.[Longwei], Lu, D.S.[Deng-Sheng], Kuang, W.H.[Wen-Hui],
Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis,
RS(8), No. 3, 2016, pp. 265.
DOI Link 1604

Lefebvre, A.[Antoine], Sannier, C.[Christophe], Corpetti, T.[Thomas],
Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree,
RS(8), No. 7, 2016, pp. 606.
DOI Link 1608

Shao, Z.F.[Zhen-Feng], Fu, H.Y.[Hu-Yan], Fu, P.[Peng], Yin, L.[Li],
Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level,
RS(8), No. 11, 2016, pp. 945.
DOI Link 1612

Guo, W.[Wei], Lu, D.S.[Deng-Sheng], Kuang, W.H.[Wen-Hui],
Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Wang, P.[Panshi], Huang, C.Q.[Cheng-Quan], de Colstoun, E.C.B.[Eric C. Brown],
Mapping 2000-2010 Impervious Surface Change in India Using Global Land Survey Landsat Data,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Silavi, T.[Tolue], Hakimpour, F.[Farshad], Claramunt, C.[Christophe], Nourian, F.[Farshad],
The Legibility and Permeability of Cities: Examining the Role of Spatial Data and Metrics,
IJGI(6), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Liu, S.[Shuai], Gu, G.H.[Guang-Hua],
Improving the Impervious Surface Estimation from Hyperspectral Images Using a Spectral-Spatial Feature Sparse Representation and Post-Processing Approach,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706

Tang, F.[Fei], Xu, H.Q.[Han-Qiu],
Impervious Surface Information Extraction Based on Hyperspectral Remote Sensing Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Jia, Y.Q.[Yu-Qiu], Tang, L.[Lina], Wang, L.[Lin],
Influence of Ecological Factors on Estimation of Impervious Surface Area Using Landsat 8 Imagery,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Sun, Z.C.[Zhong-Chang], Wang, C.Z.[Cui-Zhen], Guo, H.D.[Hua-Dong], Shang, R.R.[Ran-Ran],
A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Shi, L.F.[Ling-Fei], Ling, F.[Feng], Ge, Y.[Yong], Foody, G.M.[Giles M.], Li, X.D.[Xiao-Dong], Wang, L.H.[Li-Hui], Zhang, Y.H.[Yi-Hang], Du, Y.[Yun],
Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712

See also Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model. BibRef

Zhang, X.[Xiya], Li, P.J.[Pei-Jun],
A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis,
PandRS(135), No. Supplement C, 2018, pp. 93-111.
Elsevier DOI 1712
TVANUI, Urban extent, DMSP/OLS, NDVI, LST BibRef

Zhang, J.[Jun], Li, P.J.[Pei-Jun], Mazher, A.[Abeer], Liu, J.[Jing],
Impervious surface extraction with very high resolution imagery in urban areas: Reducing tree obscuring effect,

Chen, Y.H.[Yue-Hong], Ge, Y.[Yong], An, R.[Ru], Chen, Y.[Yu],
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

See also Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery. BibRef

Zhang, H.S.[Hong-Sheng], Lin, H.[Hui], Wang, Y.P.[Yun-Peng],
A new scheme for urban impervious surface classification from SAR images,
PandRS(139), 2018, pp. 103-118.
Elsevier DOI 1804
SAR, Impervious surface, H/A/Alpha, VIS, Radsarsat-2 BibRef

Huang, M.[Min], Chen, N.C.[Neng-Cheng], Du, W.Y.[Wen-Ying], Chen, Z.Q.[Ze-Qiang], Gong, J.Y.[Jian-Ya],
DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806

Zhuo, L.[Li], Shi, Q.L.[Qing-Li], Tao, H.Y.[Hai-Yan], Zheng, J.[Jing], Li, Q.P.[Qiu-Ping],
An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data,
PandRS(142), 2018, pp. 64-77.
Elsevier DOI 1807
Impervious surface area, Temporal mixture analysis, Nonnegative matrix factorization, DMSP-OLS BibRef

Luo, H.[Hui], Wang, L.[Le], Wu, C.[Chen], Zhang, L.[Lei],
An Improved Method for Impervious Surface Mapping Incorporating LiDAR Data and High-Resolution Imagery at Different Acquisition Times,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Tian, Y.G.[Yu-Gang], Chen, H.[Hui], Song, Q.J.[Qing-Ju], Zheng, K.[Kun],
A Novel Index for Impervious Surface Area Mapping: Development and Validation,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Wickham, J., Herold, N., Stehman, S.V., Homer, C.G., Xian, G., Claggett, P.,
Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA,
PandRS(146), 2018, pp. 151-160.
Elsevier DOI 1812
MAUP, Mean Absolute Deviation (MAD), Mean Deviation (MD), Regression, Water quality BibRef

Huang, F.H.[Feng-Hua], Yu, Y.[Ying], Feng, T.H.[Ting-Hao],
Automatic extraction of impervious surfaces from high resolution remote sensing images based on deep learning,
JVCIR(58), 2019, pp. 453-461.
Elsevier DOI 1901
High-resolution remote sensing images, Impervious surfaces extraction, Bilateral filtering, Improved watershed algorithm BibRef

Huang, F.H.[Feng-Hua], Yu, Y.[Ying], Feng, T.H.[Ting-Hao],
Automatic extraction of urban impervious surfaces based on deep learning and multi-source remote sensing data,
JVCIR(60), 2019, pp. 16-27.
Elsevier DOI 1903
Multi-source remote sensing data, Deep learning, Extraction of urban impervious surface, ELM classifier, Fuzzy C means clustering BibRef

Huang, F.H.[Feng-Hua], Yu, Y.[Ying], Feng, T.H.[Ting-Hao],
Hyperspectral remote sensing image change detection based on tensor and deep learning,
JVCIR(58), 2019, pp. 233-244.
Elsevier DOI 1901
Tensor model, Deep learning, Support tensor machine, Hyperspectral remote sensing images, Change detection BibRef

Huang, F.H.[Feng-Hua], Yu, Y.[Ying], Feng, T.H.[Ting-Hao],
Automatic building change image quality assessment in high resolution remote sensing based on deep learning,
JVCIR(63), 2019, pp. 102585.
Elsevier DOI 1909
Deep belief network, Building change detection, Morphological building index, Morphological shadow index, Quality Assessment BibRef

Wang, Y.L.[Yu-Liang], Su, H.[Huiyi], Li, M.[Mingshi],
An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902

Fu, Y.Y.[Yong-Yong], Liu, K.[Kunkun], Shen, Z.Q.[Zhang-Quan], Deng, J.S.[Jin-Song], Gan, M.[Muye], Liu, X.G.[Xin-Guo], Lu, D.M.[Dong-Ming], Wang, K.[Ke],
Mapping Impervious Surfaces in Town-Rural Transition Belts Using China's GF-2 Imagery and Object-Based Deep CNNs,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902

Wang, B.B.[Bei-Bei], Chen, Z.J.[Zhen-Jie], Zhu, A.X.[A-Xing], Hao, Y.Z.[Yu-Zhu], Xu, C.Q.[Chang-Qing],
Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

Kawakubo, F.[Fernando], Morato, R.[Rúbia], Martins, M.[Marcos], Mataveli, G.[Guilherme], Nepomuceno, P.[Pablo], Martines, M.[Marcos],
Quantification and Analysis of Impervious Surface Area in the Metropolitan Region of São Paulo, Brazil,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905

Xu, H.Y.[Hanze-Yu], Wei, Y.C.[Yu-Chun], Liu, C.[Chong], Li, X.[Xiao], Fang, H.[Hong],
A Scheme for the Long-Term Monitoring of Impervious-Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909

Bian, J.H.[Jin-Hu], Li, A.[Ainong], Zuo, J.Q.[Jia-Qi], Lei, G.B.[Guang-Bin], Zhang, Z.J.[Zheng-Jian], Nan, X.[Xi],
Estimating 2009-2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link 1910

Xian, G.[George], Shi, H.[Hua], Anderson, C.[Cody], Wu, Z.T.[Zhuo-Ting],
Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911

Li, W.L.[Wen-Liang],
Mapping Urban Impervious Surfaces by Using Spectral Mixture Analysis and Spectral Indices,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001

Li, W.L.[Wen-Liang],
Improving Urban Impervious Surfaces Mapping through Integrating Statistical Methods and Spectral Mixture Analysis,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Zhang, H.[Hua], Gorelick, S.M.[Steven M.], Zimba, P.V.[Paul V.],
Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002

Liang, Z.[Ze], Wang, Y.Y.[Yue-Yao], Sun, F.Y.[Fu-Yue], Jiang, H.[Hong], Huang, J.[Jiao], Shen, J.S.[Jia-Shu], Wei, F.[Feili], Li, S.C.[Shuang-Cheng],
Exploring the Combined Effect of Urbanization and Climate Variability on Urban Vegetation: A Multi-Perspective Study Based on More than 3000 Cities in China,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Zhao, J.[Jing], Tsutsumida, N.[Narumasa],
Mapping Fragmented Impervious Surface Areas Overlooked by Global Land-Cover Products in the Liping County, Guizhou Province, China,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Dong, X.G.[Xue-Gang], Meng, Z.G.[Zhi-Guo], Wang, Y.Z.[Yong-Zhi], Zhang, Y.Z.[Yuan-Zhi], Sun, H.T.[Hao-Teng], Wang, Q.S.[Qing-Shuai],
Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101

Zhang, L.[Lihao], Tian, Y.G.[Yu-Gang], Liu, Q.W.[Qing-Wei],
A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101

Liao, W.[Wenyue], Deng, Y.B.[Ying-Bin], Li, M.[Miao], Sun, M.[Meiwei], Yang, J.[Ji], Xu, J.H.[Jian-Hui],
Extraction and Analysis of Finer Impervious Surface Classes in Urban Area,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Tang, Y.[Yun], Shao, Z.F.[Zhen-Feng], Huang, X.[Xiao], Cai, B.[Bowen],
Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105

Chen, R.[Rui], Li, X.D.[Xiao-Dong], Zhang, Y.H.[Yi-Hang], Zhou, P.[Pu], Wang, Y.[Yalan], Shi, L.F.[Ling-Fei], Jiang, L.[Lai], Ling, F.[Feng], Du, Y.[Yun],
Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Shrestha, B.[Binita], Stephen, H.[Haroon], Ahmad, S.[Sajjad],
Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Parekh, J.R.[Jash R.], Poortinga, A.[Ate], Bhandari, B.[Biplov], Mayer, T.[Timothy], Saah, D.[David], Chishtie, F.[Farrukh],
Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Shen, J.Q.[Jia-Qi], Shuai, Y.M.[Yan-Min], Li, P.X.[Pei-Xian], Cao, Y.X.[Yu-Xi], Ma, X.W.[Xian-Wei],
Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Cavalli, R.M.[Rosa Maria],
Capability of Remote Sensing Images to Distinguish the Urban Surface Materials: A Case Study of Venice City,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110

Wang, S.S.[Shan-Shan], Pu, Y.X.[Ying-Xia], Li, S.F.[Sheng-Feng], Li, R.[Runjie], Li, M.[Maohua],
Spatio-Temporal Analysis of Impervious Surface Expansion in the Qinhuai River Basin, China, 1988-2017,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112

Sun, G.[Genyun], Cheng, J.[Ji], Zhang, A.[Aizhu], Jia, X.P.[Xiu-Ping], Yao, Y.J.[Yan-Juan], Jiao, Z.J.[Zhi-Jun],
Hierarchical fusion of optical and dual-polarized SAR on impervious surface mapping at city scale,
PandRS(184), 2022, pp. 264-278.
Elsevier DOI 2202
Impervious surface, Fusion, Dual-polarized SAR, Shadow, Sentienl-1/2 BibRef

Ouyang, L.[Linke], Wu, C.Y.[Cai-Yan], Li, J.X.[Jun-Xiang], Liu, Y.H.[Yu-Han], Wang, M.[Meng], Han, J.[Ji], Song, C.H.[Cong-He], Yu, Q.[Qian], Haase, D.[Dagmar],
Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205

Santarsiero, V.[Valentina], Nolè, G.[Gabriele], Lanorte, A.[Antonio], Tucci, B.[Biagio], Cillis, G.[Giuseppe], Murgante, B.[Beniamino],
Remote Sensing and Spatial Analysis for Land-Take Assessment in Basilicata Region (Southern Italy),
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
Conversion of natural to artificial land cover. BibRef

Mao, T.M.[Tao-Min], Fan, Y.[Yewen], Zhi, S.[Shuang], Tang, J.S.[Jin-Shan],
A Morphological Feature-Oriented Algorithm for Extracting Impervious Surface Areas Obscured by Vegetation in Collaboration with OSM Road Networks in Urban Areas,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206

Su, S.S.[Shan-Shan], Tian, J.[Jia], Dong, X.Y.[Xin-Yu], Tian, Q.J.[Qing-Jiu], Wang, N.[Ning], Xi, Y.B.[Yan-Biao],
An Impervious Surface Spectral Index on Multispectral Imagery Using Visible and Near-Infrared Bands,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208

Strand, G.H.[Geir-Harald],
Accuracy of the Copernicus High-Resolution Layer Imperviousness Density (HRL IMD) Assessed by Point Sampling within Pixels,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208

Xu, T.Y.[Tian-Yu], Li, E.[Erzhu], Samat, A.[Alim], Li, Z.Q.[Zhi-Qing], Liu, W.[Wei], Zhang, L.P.[Lian-Peng],
Estimating Large-Scale Interannual Dynamic Impervious Surface Percentages Based on Regional Divisions,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208

Gong, Y.[Yali], Xie, H.[Huan], Jin, Y.M.[Yan-Min], Tong, X.H.[Xiao-Hua],
Assessing Multi-Temporal Global Urban Land-Cover Products Using Spatio-Temporal Stratified Sampling,
IJGI(11), No. 8, 2022, pp. xx-yy.
DOI Link 2209

Gong, Y.[Yali], Xie, H.[Huan], Tong, X.H.[Xiao-Hua], Jin, Y.M.[Yan-Min], Xv, X., Wang, Q.,
Area Estimation of Multi-temporal Global Impervious Land Cover Based On Stratified Random Sampling,
DOI Link 2012

Liu, H.[He], Li, X.M.[Xue-Ming], Li, S.B.[Song-Bo], Tian, S.Z.[Shen-Zhen], Gong, Y.[Yilu], Guan, Y.Y.[Ying-Ying], Sun, H.[He],
Agglomeration Externalities, Network Externalities and Urban High-Quality Development: A Case Study of Urban Agglomeration in the Middle Reaches of the Yangtze River,
IJGI(11), No. 11, 2022, pp. xx-yy.
DOI Link 2212

Alsubhi, Y.[Yazeed], Qureshi, S.[Salman], Assiri, M.E.[Mazen E.], Siddiqui, M.H.[Muhammad Haroon],
Quantifying the Impact of Dust Sources on Urban Physical Growth and Vegetation Status: A Case Study of Saudi Arabia,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212

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Liu, J.T.[Jian-Tao], Li, Y.[Yexiang], Zhang, Y.[Yan], Liu, X.Q.[Xiao-Qian],
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Chang, R.[Ruichun], Hou, D.[Dong], Chen, Z.[Zhe], Chen, L.[Ling],
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Wu, W.[Wenfu], Guo, S.[Songjing], Shao, Z.F.[Zhen-Feng], Li, D.R.[De-Ren],
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Inácio, D.[Diogo], Oliveira, H.[Henrique], Oliveira, P.[Pedro], Correia, P.[Paulo],
A Low-Cost Deep Learning System to Characterize Asphalt Surface Deterioration,
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Langenkamp, J.P.[Jan-Philipp], Rienow, A.[Andreas],
Exploring the Use of Orthophotos in Google Earth Engine for Very High-Resolution Mapping of Impervious Surfaces: A Data Fusion Approach in Wuppertal, Germany,
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Liu, J.T.[Jian-Tao], Zhang, Y.[Yan], Liu, C.T.[Chun-Ting], Liu, X.Q.[Xiao-Qian],
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Xu, S.J.[Shao-Juan], Fina, S.[Stefan],
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Large-scale mapping, Detection of urban land changes, Cloud computing, Imperviousness, Spectral-temporal metrics, Spectral unmixing BibRef

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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Urban Areas, Change and Growth .

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