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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.
IEEE DOI
1502
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.
IEEE DOI
1704
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Campos-Taberner, M.[Manuel],
Javier-García-Haro, F.[Francisco],
Martínez, B.[Beatriz],
Sánchez-Ruiz, S.[Sergio],
Moreno-Martínez, Á.[Álvaro],
Camps-Valls, G.[Gustau],
Amparo-Gilabert, M.[María],
Land use classification over smallholding areas in the European
Common Agricultural Policy framework,
PandRS(197), 2023, pp. 320-334.
Elsevier DOI
2303
Land use (LU) monitoring, Common Agricultural Policy (CAP),
Classification, Sentinel-2, Deep Learning, kNDVI
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Rawal, D.,
Chhabra, A.,
Pandya, M.,
Vyas, A.,
Land Use and Land Cover Mapping - A Case Study of Ahmedabad District,
ISPRS20(B3:189-193).
DOI Link
2012
BibRef
Bergado, J.R.,
Persello, C.,
Stein, A.,
Land Use Classification Using Deep Multitask Networks,
ISPRS20(B3:17-21).
DOI Link
2012
BibRef
Guliyeva, S.H.,
Land Cover-Land Use Monitoring for Agriculture Features
Classification,
ISPRS20(B3:61-65).
DOI Link
2012
BibRef
Mohd Kamal, N.A.,
Razak, K.A.,
Rambat, S.,
Land Use/land Cover Assessment in a Seismically Active Region In
Kundasang, Sabah,
GGT19(433-440).
DOI Link
1912
BibRef
Men, J.,
Fang, L.,
Liu, Y.,
Sun, Y.,
Land Use Classification Based On Multi-structure Convolution Neural
Network Features Cascading,
PIA19(163-167).
DOI Link
1912
BibRef
Yang, C.,
Rottensteiner, F.,
Heipke, C.,
Towards Better Classification of Land Cover and Land Use Based On
Convolutional Neural Networks,
Semantics3D19(139-146).
DOI Link
1912
BibRef
Jamali, A.,
Abdul Rahman, A.,
Evaluation of Advanced Data Mining Algorithms in Land Use/land Cover
Mapping,
GGT19(283-289).
DOI Link
1912
BibRef
Nguyen, H.T.T.,
Doan, T.M.,
Radeloff, V.,
Applying Random Forest Classification to Map Land Use/land Cover Using
Landsat 8 OLI,
Gi4DM18(363-367).
DOI Link
1805
BibRef
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
BibRef
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,
MultiTemp11(193-196).
IEEE DOI
1109
BibRef
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,
HighRes09(xx-yy).
PDF File.
0906
BibRef
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).
IEEE DOI
0507
BibRef
Mathieu, S.,
Berthod, M.,
Leymarie, P.,
Determination of proportions and entropy of land use mixing in pixels
of a multispectral satellite image,
ICPR94(A:798-800).
IEEE DOI
9410
BibRef
Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Habitat Analysis .