24.1.10 General Urban Area Detection, Built-Up Area Detection

Chapter Contents (Back)
Aerial Image Analysis. Urban Area Detection. Built-Up Area Detection.
See also Impervious Surface Detection, Urban Area Extraction. Growth analysis:
See also Urban Areas, Change and Growth.
See also Classification for Urban Area Land Cover, Remote Sensing.
See also Urban Heat Islands, Remote Sensing.
See also Night Time Image Analysis for Urban Area Detection, Change and Growth.

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Unsalan, C., Boyer, K.L.,
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IEEE Abstract. 0407
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Earlier:
Classifying land development in high resolution satellite images using straight line statistics,
ICPR02(I: 127-130).
IEEE DOI 0211
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Unsalan, C., Boyer, K.L.,
A Theoretical and Experimental Investigation of Graph Theoretical Measures for Land Development in Satellite Imagery,
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Earlier: ICPR04(II: 64-67).
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Earlier: ICPR04(III: 49-52).
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Unsalan, C.,
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Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory,
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Road Network Detection Using Probabilistic and Graph Theoretical Methods,
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Polarimetric and Interferometric Characterization of Coherent Scatterers in Urban Areas,
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Matikainen, L.[Leena], Hyyppä, J.[Juha], Engdahl, M.[Marcus],
Mapping Built-up Areas from Multitemporal Interferometric SAR Images: A Segment-based Approach,
PhEngRS(72), No. 6, June 2006, pp. 701-714.
WWW Link. 0610
BibRef
Earlier:
Extracting Built-Up Areas from Multitemporal Interferometric SAR Images,
PCV02(B: 170). 0305
Built-up areas were accurately detected from a multitemporal interferometric ERS dataset using a region-based classification approach, and a correlation between the building density and intensity/coherence in the image was found. BibRef

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

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

Zhong, P.[Ping], Wang, R.S.[Run-Sheng],
A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images,
GeoRS(45), No. 12, December 2007, pp. 3978-3988.
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Inglada, J.[Jordi],
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Object recognition; Man-made objects; Support vector machines; Geometrical moments BibRef

Inglada, J., Michel, J.,
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IEEE DOI 0903
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Xu, H.Q.[Han-Qiu],
Extraction of Urban Built-up Land Features from Landsat Imagery Using a Thematic-oriented Index Combination Technique,
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A new method to extract urban built-up land information from Landsat imagery based on three thematic indexes. BibRef

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Guo, L.[Li], Boukir, S.[Samia],
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Tomaszewska, M.A.[Monika A.], Henebry, G.M.[Geoffrey M.],
Urban-Rural Contrasts in Central-Eastern European Cities Using a MODIS 4 Micron Time Series,
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Gevaert, C.M.[Caroline M.], Persello, C.[Claudio], Sliuzas, R., Vosselman, G.[George],
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Rizaldy, A.[Aldino], Persello, C.[Claudio], Gevaert, C.M.[Caroline M.], Oude Elberink, S.[Sander], Vosselman, G.[George],
Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks,
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Regions Set in Stone: Delimiting and Categorizing Regions in Europe by Settlement Patterns Derived from EO-Data,
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Ma, X.L.[Xiao-Long], Tong, X.H.[Xiao-Hua], Liu, S.[Sicong], Luo, X.[Xin], Xie, H.[Huan], Li, C.M.[Cheng-Ming],
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Wang, R.[Run], Wan, B.[Bo], Guo, Q.H.[Qing-Hua], Hu, M.S.[Mao-Sheng], Zhou, S.P.[Shun-Ping],
Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data,
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Liao, C.[Cong], Dai, T.[Teqi], Cai, H.Y.[Hong-Yu], Zhang, W.X.[Wen-Xin],
Examining the Driving Factors Causing Rapid Urban Expansion in China: An Analysis Based on GlobeLand30 Data,
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Rimal, B.[Bhagawat], Zhang, L.[Lifu], Keshtkar, H.[Hamidreza], Wang, N.[Nan], Lin, Y.[Yi],
Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model,
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Rimal, B.[Bhagawat], Zhang, L.[Lifu], Keshtkar, H.[Hamidreza], Haack, B.N.[Barry N.], Rijal, S.[Sushila], Zhang, P.[Peng],
Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain,
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Esch, T.[Thomas], Heldens, W.[Wieke], Hirner, A.[Andreas], Keil, M.[Manfred], Marconcini, M.[Mattia], Roth, A.[Achim], Zeidler, J.[Julian], Dech, S.[Stefan], Strano, E.[Emanuele],
Breaking new ground in mapping human settlements from space: The Global Urban Footprint,
PandRS(134), No. Supplement C, 2017, pp. 30-42.
Elsevier DOI 1712
Global mapping, Human settlements, Urbanization, Automation, Processing, Image analysis, Texture, TerraSAR-X, TanDEM-X BibRef

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

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A Spatial Analysis of the Relationship between Vegetation and Poverty,
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DOI Link 1804
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Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery,
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Derivation of the Orientation Parameters in Built-Up Areas: With Application to Model-Based Decomposition,
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IEEE DOI 1808
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Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China,
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DOI Link 1811
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Delineation of Built-Up Areas from Very High-Resolution Satellite Imagery Using Multi-Scale Textures and Spatial Dependence,
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Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia,
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Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery,
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Delimitating Urban Commercial Central Districts by Combining Kernel Density Estimation and Road Intersections: A Case Study in Nanjing City, China,
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Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk,
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Urban Mapping Accuracy Enhancement in High-Rise Built-Up Areas Deployed by 3D-Orthorectification Correction from WorldView-3 and LiDAR Imageries,
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Karunarathne, A.[Ananda], Lee, G.[Gunhak],
Estimating Hilly Areas Population Using a Dasymetric Mapping Approach: A Case of Sri Lanka's Highest Mountain Range,
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Roni, R.[Rezaul], Jia, P.[Peng],
An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh,
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Ma, D.[Ding], Guo, R.Z.[Ren-Zhong], Zheng, Y.[Ye], Zhao, Z.G.[Zhi-Gang], He, F.N.[Fang-Ning], Zhu, W.[Wei],
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Masot, A.N.[Ana Nieto], Alonso, G.C.[Gema Cárdenas], Moriche, Á.E.[Ángela Engelmo],
Spatial Analysis of the Rural-Urban Structure of the Spanish Municipalities,
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Li, Q.M.[Qian-Ming], Zheng, B.[Bohong], Tu, B.[Bing], Yang, Y.S.[Yu-Sheng], Wang, Z.Y.[Zhi-Yuan], Jiang, W.[Wei], Yao, K.[Kai], Yang, J.W.[Jia-Wei],
Refining Urban Built-Up Area via Multi-Source Data Fusion for the Analysis of Dongting Lake Eco-Economic Zone Spatiotemporal Expansion,
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Cabrera-Barona, P.F.[Pablo F.], Bayón, M.[Manuel], Durán, G.[Gustavo], Bonilla, A.[Alejandra], Mejía, V.[Verónica],
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Bartsch, A.[Annett], Pointner, G.[Georg], Ingeman-Nielsen, T.[Thomas], Lu, W.J.[Wen-Jun],
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Elsevier DOI 2008
Built-up region segmentation, Semantic segmentation, Domain adaptation, Weakly-supervised adaptation, Deep learning BibRef

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

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

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

He, X.[Xiong], Yuan, X.D.[Xiao-Die], Zhang, D.[Dahao], Zhang, R.R.[Rong-Rong], Li, M.[Ming], Zhou, C.[Chunshan],
Delineation of Urban Agglomeration Boundary Based on Multisource Big Data Fusion: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area (GBA),
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The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas,
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Impervious Surface Detection, Urban Area Extraction .


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