23.2.1 Land Use, General Problems

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

Bischof, H.[Horst], Schneider, W.[Werner], Pinz, A.[Axel],
Multispectral Classification of Landsat Images Using Neural Networks,
GeoRS(30), No. 3, 1992, pp. 482-490. BibRef 9200

Bischof, H.[Horst], Leonardis, A.[Ales],
Finding Optimal Neural Networks for Land Use Classification,
GeoRS(36), No. 1, 1998, pp. 337-341. BibRef 9800

Ji, C.Y.,
Land-Use Classification of Remotely Sensed Data Using Kohonen Self-Organizing Feature Map Neural Networks,
PhEngRS(66), No. 12, December 2000, pp. 1451-1460. Results are compared to those of the maximum-likelihood method and of the BP neural networks. 0101

Yuan, H., van der Wiele, C., Khorram, S.,
An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery,
RS(1), No. 3, September 2009, pp. 243-265.
DOI Link 1203

Manandhar, R., Odeh, I., Ancev, T.,
Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement,
RS(1), No. 3, September 2009, pp. 330-344.
DOI Link 1203

Clark, M., Aide, T.,
Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT) for Collecting Land-Use/Land-Cover Reference Data,
RS(3), No. 3, March 2011, pp. 601-620.
DOI Link 1203

Martínez, S., Mollicone, D.,
From Land Cover to Land Use: A Methodology to Assess Land Use from Remote Sensing Data,
RS(4), No. 4, April 2012, pp. 1024-1045.
DOI Link 1202

Kitada, K., Fukuyama, K.,
Land-Use and Land-Cover Mapping Using a Gradable Classification Method,
RS(4), No. 6, June 2012, pp. 1544-1558.
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,
PandRS(72), No. 1, August 2012, pp. 46-55.
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.
DOI Link 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

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

Qi, K.L.[Kun-Lun], Yang, C.[Chao], Hu, C.L.[Chu-Li], Shen, Y.L.[Yong-Lin], Shen, S.Y.[Sheng-Yu], Wu, H.Y.[Hua-Yi],
Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103

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.Z.[Shou-Zhi], 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

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

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

Pan, T.T.[Ting-Ting], Zhang, Y.[Yu], Su, F.Z.[Fen-Zhen], Lyne, V.[Vincent], Cheng, F.[Fei], Xiao, H.[Han],
Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization,
IJGI(10), No. 2, 2021, pp. xx-yy.
DOI Link 2103

Shuangao, W.[Wang], Padmanaban, R.[Rajchandar], Mbanze, A.A.[Aires A.], Silva, J.M.N.[João M. N.], Shamsudeen, M.[Mohamed], Cabral, P.[Pedro], Campos, F.S.[Felipe S.],
Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103

Pinto, N.[Nuno], Antunes, A.P.[António P.], Roca, J.[Josep],
A Cellular Automata Model for Integrated Simulation of Land Use and Transport Interactions,
IJGI(10), No. 3, 2021, pp. xx-yy.
DOI Link 2104

Bui, D.H.[Dang Hung], Mucsi, L.[László],
From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Pedrayes, O.D.[Oscar D.], Lema, D.G.[Darío G.], García, D.F.[Daniel F.], Usamentiaga, R.[Rubén], Alonso, Á.[Ángela],
Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Sanlang, S.[Siji], Cao, S.[Shisong], Du, M.Y.[Ming-Yi], Mo, Y.[You], Chen, Q.[Qiang], He, W.[Wen],
Integrating Aerial LiDAR and Very-High-Resolution Images for Urban Functional Zone Mapping,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Kim, D.H.[Do-Hyung], López, G.[Guzmán], Kiedanski, D.[Diego], Maduako, I.[Iyke], Ríos, B.[Braulio], Descoins, A.[Alan], Zurutuza, N.[Naroa], Arora, S.[Shilpa], Fabian, C.[Christopher],
Bias in Deep Neural Networks in Land Use Characterization for International Development,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

McCutchan, M.[Marvin], Comber, A.J.[Alexis J.], Giannopoulos, I.[Ioannis], Canestrini, M.[Manuela],
Semantic Boosting: Enhancing Deep Learning Based LULC Classification,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

McCutchan, M.[Marvin], Giannopoulos, I.[Ioannis],
Encoding Geospatial Vector Data for Deep Learning: LULC as a Use Case,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206

Li, Q.W.[Qing-Wen], Yan, D.M.[Dong-Mei], Wu, W.[Wanrong],
Remote Sensing Image Scene Classification Based on Global Self-Attention Module,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112

Ostafin, K.[Krzysztof], Pietrzak, M.[Malgorzata], Kaim, D.[Dominik],
Impact of the Cartographer's Position and Topographic Accessibility on the Accuracy of Historical Land Use Information: Case of the Second Military Survey Maps of the Habsburg Empire,
IJGI(10), No. 12, 2021, pp. xx-yy.
DOI Link 2112

Nasiri, V.[Vahid], Deljouei, A.[Azade], Moradi, F.[Fardin], Sadeghi, S.M.M.[Seyed Mohammad Moein], Borz, S.A.[Stelian Alexandru],
Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Zheng, K.[Kang], Wang, H.Y.[Hai-Ying], Qin, F.[Fen], Han, Z.G.[Zhi-Gang],
A Land Use Classification Model Based on Conditional Random Fields and Attention Mechanism Convolutional Networks,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Zhang, X.D.[Xue-Dong], Wang, X.[Xuedi], Zhou, Z.[Zexu], Li, M.W.[Meng-Wei], Jing, C.F.[Chang-Feng],
Spatial Quantitative Model of Human Activity Disturbance Intensity and Land Use Intensity Based on GF-6 Image, Empirical Study in Southwest Mountainous County, China,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Stateczny, A.[Andrzej], Bolugallu, S.M.[Shanthi Mandekolu], Divakarachari, P.B.[Parameshachari Bidare], Ganesan, K.[Kavithaa], Muthu, J.R.[Jamuna Rani],
Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

Koko, A.F.[Auwalu Faisal], Han, Z.[Zexu], Wu, Y.[Yue], Abubakar, G.A.[Ghali Abdullahi], Bello, M.[Muhammed],
Spatiotemporal Land Use/Land Cover Mapping and Prediction Based on Hybrid Modeling Approach: A Case Study of Kano Metropolis, Nigeria (2020-2050),
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212

Zhang, J.[Junbo], Xu, S.F.[Shi-Feng], Sun, J.[Jun], Ou, D.H.[Ding-Hua], Wu, X.B.[Xiao-Bo], Wang, M.T.[Man-Tao],
Unsupervised Adversarial Domain Adaptation for Agricultural Land Extraction of Remote Sensing Images,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212

Schuh, L.A.[Leila A.], Santos, M.J.[Maria J.], Schaepman, M.E.[Michael E.], Furrer, R.[Reinhard],
An Empirical Bayesian Approach to Quantify Multi-Scale Spatial Structural Diversity in Remote Sensing Data,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301

Beroho, M.[Mohamed], Briak, H.[Hamza], Cherif, E.[El_Khalil], Boulahfa, I.[Imane], Ouallali, A.[Abdessalam], Mrabet, R.[Rachid], Kebede, F.[Fassil], Bernardino, A.[Alexandre], Aboumaria, K.[Khadija],
Future Scenarios of Land Use/Land Cover (LULC) Based on a CA-Markov Simulation Model: Case of a Mediterranean Watershed in Morocco,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303

Li, C.[Cheng], Zhao, J.[Jie], Hou, W.[Wei],
Nonlinear Effects of Landscape Patterns on Ecosystem Services at Multiple Scales Based on Gradient Boosting Decision Tree Models,
RS(15), No. 7, 2023, pp. 1919.
DOI Link 2304

Zhang, Y.H.[Yong-Hong], Zhao, H.J.[Hua-Jun], Ma, G.Y.[Guang-Yi], Xie, D.L.[Dong-Lin], Geng, S.[Sutong], Lu, H.Y.[Huan-Yu], Tian, W.[Wei], Sian, K.T.C.L.K.[Kenny Thiam Choy Lim Kam],
MAAFEU-Net: A Novel Land Use Classification Model Based on Mixed Attention Module and Adjustable Feature Enhancement Layer in Remote Sensing Images,
IJGI(12), No. 5, 2023, pp. xx-yy.
DOI Link 2306

Luan, C.X.[Chao-Xu], Liu, R.Z.[Ren-Zhi], Sun, J.[Jing], Su, S.R.[Shang-Ren], Shen, Z.Y.[Zhen-Yao],
An Improved Future Land-Use Simulation Model with Dynamically Nested Ecological Spatial Constraints,
RS(15), No. 11, 2023, pp. 2921.
DOI Link 2306

Yu, X.R.[Xin-Ran], Xiao, J.T.[Jiang-Tao], Huang, K.[Ke], Li, Y.Y.[Yuan-Yuan], Lin, Y.[Yang], Qi, G.[Gang], Liu, T.[Tao], Ren, P.[Ping],
Simulation of Land Use Based on Multiple Models in the Western Sichuan Plateau,
RS(15), No. 14, 2023, pp. 3629.
DOI Link 2307

Zhang, P.F.[Peng-Fei], Wu, Y.J.[Yi-Jin], Li, C.[Chang], Li, R.H.[Ren-Hua], Yao, H.[He], Zhang, Y.[Yong], Zhang, G.[Genlin], Li, D.H.[De-Hua],
National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308

Macarringue, L.S.[Lucrêncio Silvestre], Bolfe, É.L.[Édson Luis], Duverger, S.G.[Soltan Galano], Sano, E.E.[Edson Eyji], Caldas, M.M.[Marcellus Marques], Ferreira, M.C.[Marcos César], Junior, J.Z.[Jurandir Zullo], Matias, L.F.[Lindon Fonseca],
Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning,
IJGI(12), No. 8, 2023, pp. 342.
DOI Link 2309

Guo, N.[Ningbo], Jiang, M.Y.[Ming-Yong], Gao, L.[Lijing], Li, K.[Kaitao], Zheng, F.J.[Feng-Jie], Chen, X.N.[Xiang-Ning], Wang, M.D.[Ming-Dong],
HFCC-Net: A Dual-Branch Hybrid Framework of CNN and CapsNet for Land-Use Scene Classification,
RS(15), No. 20, 2023, pp. 5044.
DOI Link 2310

Zhong, Y.Q.[Yu-Qing], Zhang, X.X.[Xiao-Xiang], Yang, Y.F.[Yan-Fei], Xue, M.H.[Ming-Hui],
Optimization and Simulation of Mountain City Land Use Based on MOP-PLUS Model: A Case Study of Caijia Cluster, Chongqing,
IJGI(12), No. 11, 2023, pp. xx-yy.
DOI Link 2312

Li, W.B.[Wang-Bin], Sun, K.M.[Kai-Min], Li, W.Z.[Wen-Zhuo], Huang, X.[Xiao], Wei, J.J.[Jin-Jiang], Chen, Y.[Yepei], Cui, W.[Wei], Chen, X.[Xueyu], Lv, X.W.[Xian-Wei],
Assisted learning for land use classification: The important role of semantic correlation between heterogeneous images,
PandRS(208), 2024, pp. 158-175.
Elsevier DOI Code:
WWW Link. 2402
Land use classification, Knowledge distillation, Heterogeneous images, Semantic correlation BibRef

Bhungeni, O.[Orlando], Ramjatan, A.[Ashadevi], Gebreslasie, M.[Michael],
Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal,
RS(16), No. 12, 2024, pp. 2219.
DOI Link 2406

Alcaras, E., Amoroso, P.P., Parente, C., Prezioso, G.,
Remotely Sensed Image Fast Classification and Smart Thematic Map Production,
DOI Link 2201

Yang, C., Rottensteiner, F.[Franz], Heipke, C.[Christian],
CNN-based Multi-scale Hierarchical Land Use Classification for The Verification of Geospatial Databases,
ISPRS21(B2-2021: 495-502).
DOI Link 2201

Yassine, H., Tout, K., Jaber, M.,
Improving LULC Classification From Satellite Imagery Using Deep Learning - Eurosat Dataset,
ISPRS21(B3-2021: 369-376).
DOI Link 2201

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.
DOI Link 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 General Issue, Land Use, Land Cover continues in
Habitat Analysis .

Last update:Jul 13, 2024 at 15:27:21