Land Use, General Problems

Chapter Contents (Back)
Land Use. Clearly an overlaping subset of Land Cover.
See also Subpixel Target, Subpixel Land Use, Tiny Objects.
See also Sentinel-1, -2, -3 for Land Cover, Remote Sensing.

Bischof, H.[Horst], Schneider, W.[Werner], Pinz, A.[Axel],
Multispectral Classification of Landsat Images Using Neural Networks,
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Bischof, H.[Horst], Leonardis, A.[Ales],
Finding Optimal Neural Networks for Land Use Classification,
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Ji, C.Y.,
Land-Use Classification of Remotely Sensed Data Using Kohonen Self-Organizing Feature Map Neural Networks,
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Yuan, H., van der Wiele, C., Khorram, S.,
An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery,
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Manandhar, R., Odeh, I., Ancev, T.,
Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement,
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Clark, M., Aide, T.,
Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT) for Collecting Land-Use/Land-Cover Reference Data,
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Martínez, S., Mollicone, D.,
From Land Cover to Land Use: A Methodology to Assess Land Use from Remote Sensing Data,
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DOI Link 1202

Kitada, K., Fukuyama, K.,
Land-Use and Land-Cover Mapping Using a Gradable Classification Method,
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DOI Link 1208

Jiao, L.M.[Li-Min], Liu, Y.L.[Yao-Lin], Li, H.L.[Hong-Liang],
Characterizing land-use classes in remote sensing imagery by shape metrics,
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Elsevier DOI 1209
Land-use; Image segmentation; Landscape metrics; Shape metrics; Image classification BibRef

Jiao, L.M., Liu, Y.L.,
Analyzing the Shape Characteristics of Land Use Classes in Remote Sensing Imagery,
AnnalsPRS(I-7), No. 2012, pp. 135-140.
HTML Version. 1209

Chen, Y.[Yanlei], Gong, P.[Peng],
Clustering based on eigenspace transformation: CBEST for efficient classification,
PandRS(83), No. 1, 2013, pp. 64-80.
Elsevier DOI 1308
Land cover/use mapping BibRef

Li, Y.Z.[Yi-Zhan], Zhu, X.F.[Xiu-Fang], Pan, Y.Z.[Yao-Zhong], Gu, J.Y.[Jian-Yu], Zhao, A.Z.[An-Zhou], Liu, X.F.[Xian-Feng],
A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery,
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DOI Link 1410

Chen, S.Z.[Shi-Zhi], Tian, Y.L.[Ying-Li],
Pyramid of Spatial Relatons for Scene-Level Land Use Classification,
GeoRS(53), No. 4, April 2015, pp. 1947-1957.
data structures BibRef

Pereira, D.R.[Danillo Roberto], Papa, J.P.[João Paulo],
A new approach to contextual learning using interval arithmetic and its applications for land-use classification,
PRL(83, Part 2), No. 1, 2016, pp. 188-194.
Elsevier DOI 1609
Sliding Window BibRef

Fan, J., Chen, T., Lu, S.,
Unsupervised Feature Learning for Land-Use Scene Recognition,
GeoRS(55), No. 4, April 2017, pp. 2250-2261.
geophysical techniques BibRef

Chen, Y.B.[Yang-Bo], Dou, P.[Peng], Yang, X.J.[Xiao-Jun],
Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711

Zhang, B.[Bin], Wang, C.P.[Cun-Peng], Shen, Y.L.[Yong-Lin], Liu, Y.Y.[Yue-Yan],
Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Wang, Q.[Qing], Sun, H.[Hua], Li, R.P.[Ruo-Pu], Wang, G.X.[Guang-Xing],
A new stochastic simulation algorithm for image-based classification: Feature-space indicator simulation,
PandRS(152), 2019, pp. 145-165.
Elsevier DOI 1905
Remote sensing, Image classification, Feature space, Geostatistics, Stochastic simulation, Land use and land cover BibRef

Ray, R.L.[Ram L.], Ibironke, A.[Ademola], Kommalapati, R.[Raghava], Fares, A.[Ali],
Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S.,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Hou, W.[Wan], Hou, X.Y.[Xi-Yong],
Data Fusion and Accuracy Analysis of Multi-Source Land Use/Land Cover Datasets along Coastal Areas of the Maritime Silk Road,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912

Talukdar, S.[Swapan], Singha, P.[Pankaj], Mahato, S.[Susanta], Shahfahad, Pal, S.[Swades], Liou, Y.A.[Yuei-An], Rahman, A.[Atiqur],
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations: A Review,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

Su, M.[Mo], Guo, R.Z.[Ren-Zhong], Chen, B.[Bin], Hong, W.Y.[Wu-Yang], Wang, J.Q.[Jia-Qi], Feng, Y.M.[Yi-Mei], Xu, B.[Bing],
Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Tian, Y.[Ye], Chen, C.[Chenru], Chen, X.Y.[Xin-Yi], Zhang, Q.Q.[Qian-Qian], Sun, R.Z.[Rui-Zhi],
Research on real-time analysis technology of urban land use based on support vector machine,
PRL(133), 2020, pp. 320-326.
Elsevier DOI 2005
Support vector machine, Data processing, Data analysis, Web mining, Text analysis BibRef

Sun, J.[Jing], Wang, H.[Hong], Song, Z.L.[Zheng-Lin], Lu, J.B.[Jin-Bo], Meng, P.Y.[Peng-Yu], Qin, S.H.[Shu-Hong],
Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Chang, S.[Shouzhi], Wang, Z.M.[Zong-Ming], Mao, D.H.[De-Hua], Guan, K.[Kehan], Jia, M.M.[Ming-Ming], Chen, C.[Chaoqun],
Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Qian, Z.[Zhen], Liu, X.[Xintao], Tao, F.[Fei], Zhou, T.[Tong],
Identification of Urban Functional Areas by Coupling Satellite Images and Taxi GPS Trajectories,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Vali, A.[Ava], Comai, S.[Sara], Matteucci, M.[Matteo],
Deep Learning for Land Use and Land Cover Classification based on Hyperspectral and Multispectral Earth Observation Data: A Review,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Müller, I.[Inken], Taubenböck, H.[Hannes], Kuffer, M.[Monika], Wurm, M.[Michael],
Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Anugraha, A.S.[Adindha Surya], Chu, H.J.[Hone-Jay], Ali, M.Z.[Muhammad Zeeshan],
Social Sensing for Urban Land Use Identification,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009

Andrade, R.[Renato], Alves, A.[Ana], Bento, C.[Carlos],
POI Mining for Land Use Classification: A Case Study,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009

Tassi, A.[Andrea], Vizzari, M.[Marco],
Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
Land Use-Land Cover. BibRef

Rajendran, G.B.[Ganesh B.], Kumarasamy, U.M.[Uma M.], Zarro, C.[Chiara], Divakarachari, P.B.[Parameshachari B.], Ullo, S.L.[Silvia L.],
Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Smaczynski, M.[Maciej], Medynska-Gulij, B.[Beata], Halik, L.[Lukasz],
The Land Use Mapping Techniques (Including the Areas Used by Pedestrians) Based on Low-Level Aerial Imagery,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link 2012

Huang, Z.[Zihao], Du, H.Q.[Hua-Qiang], Li, X.J.[Xue-Jian], Zhang, M.[Meng], Mao, F.J.[Fang-Jie], Zhu, D.[Di'en], He, S.B.[Shao-Bai], Liu, H.[Hua],
Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link 2012

Li, X.T.[Xiao-Ting], Hu, T.Y.[Teng-Yun], Gong, P.[Peng], Du, S.H.[Shi-Hong], Chen, B.[Bin], Li, X.C.[Xue-Cao], Dai, Q.[Qi],
Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Rawal, D., Chhabra, A., Pandya, M., Vyas, A.,
Land Use and Land Cover Mapping - A Case Study of Ahmedabad District,
DOI Link 2012

Bergado, J.R., Persello, C., Stein, A.,
Land Use Classification Using Deep Multitask Networks,
DOI Link 2012

Guliyeva, S.H.,
Land Cover-Land Use Monitoring for Agriculture Features Classification,
DOI Link 2012

Mohd Kamal, N.A., Razak, K.A., Rambat, S.,
Land Use/land Cover Assessment in a Seismically Active Region In Kundasang, Sabah,
DOI Link 1912

Men, J., Fang, L., Liu, Y., Sun, Y.,
Land Use Classification Based On Multi-structure Convolution Neural Network Features Cascading,
DOI Link 1912

Yang, C., Rottensteiner, F., Heipke, C.,
Towards Better Classification of Land Cover and Land Use Based On Convolutional Neural Networks,
DOI Link 1912

Jamali, A., Abdul Rahman, A.,
Evaluation of Advanced Data Mining Algorithms in Land Use/land Cover Mapping,
DOI Link 1912

Nguyen, H.T.T., Doan, T.M., Radeloff, V.,
Applying Random Forest Classification to Map Land Use/land Cover Using Landsat 8 OLI,
DOI Link 1805

Mansor, S.B., Pormanafi, S., Mahmud, A.R.B., Pirasteh, S.,
Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms,
AnnalsPRS(I-2), No. 2012, pp. 229-234.
HTML Version. 1209

Heremans, S.[Stien], Orshoven, J.V.[Jos Vand_],
Effect of the learning algorithm on the accuracy of sub-pixel land use classifications with multilayer perceptrons,

Ma, S.[Shifa], He, J.H.[Jian-Hua], Liu, F.[Feng],
Land-use Spatial Optimization Model Based On Particle Swarm Optimization,
VCGVA09(xx-yy). 0910
Particle Swarm Optimization PSO, Land-Use Spatial Allocation, Spatial Modeling, GIS BibRef

Hefnawy, A.A.,
A High Accuracy Land Use/Cover Retrieval System,
PDF File. 0906

Pan, C.H.[Chun-Hong], Wu, G.[Gang], Prinet, V.[Veronique], Yang, Q.[Qing], Ma, S.D.[Song-De],
A Band-Weighted Landuse Classification Method for Multispectral Images,
CVPR05(I: 96-102).

Mathieu, S., Berthod, M., Leymarie, P.,
Determination of proportions and entropy of land use mixing in pixels of a multispectral satellite image,

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Habitat Analysis .

Last update:Mar 3, 2021 at 15:01:44