COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES

31 citations as recorded by crossref.

1. Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesi A. Yiğit et al. 10.51489/tuzal.1551019
2. Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors Y. Ouma et al. 10.3390/su142214934
3. Dual Path Attention Network (DPANet) for Intelligent Identification of Wenchuan Landslides X. Wang et al. 10.3390/rs15215213
4. Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve B. Mandal 10.1016/j.rineng.2024.102854
5. Tourism-Induced Urbanization in Phuket Island, Thailand (1987–2024): A Spatiotemporal Analysis S. Moukomla & W. Marome 10.3390/urbansci9030055
6. Exploring the impact of land use/land cover changes on the dynamics of Deepor wetland (a Ramsar site) in Assam, India using geospatial techniques and machine learning models T. Saha et al. 10.1007/s40808-024-01999-0
7. Assessment of the Accuracy of Various Machine Learning Algorithms for Classifying Urban Areas through Google Earth Engine: A Case Study of Kabul City, Afghanistan K. Ahmadi 10.24018/ejai.2024.3.3.40
8. Machine learning-based spatio-temporal assessment of land use/land cover change in Barishal district of Bangladesh between 1988 and 2024 W. Zaman & H. Real 10.1016/j.envc.2025.101168
9. Semi-automated mangrove mapping at National-Scale using Sentinel-2, Sentinel-1, and SRTM data with Google Earth Engine: A case study in Thailand S. Pinkeaw et al. 10.1016/j.ejrs.2024.07.001
10. Spatiotemporal Dynamic Changes and Prediction of Wild Fruit Forests in Emin County, Xinjiang, China, Based on Random Forest and PLUS Model Q. Sun et al. 10.3390/su16145925
11. Estimating the plausible projections of land use/land cover dynamics in Jhelum and Chenab River basins using satellite imageries and machine learning models in Google Earth Engine S. Zafar et al. 10.1080/10106049.2025.2491640
12. Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus Y. Ouma et al. 10.3390/su16041699
13. Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia T. Mengesha et al. 10.3390/land13030335
14. Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach Y. Ouma et al. 10.1080/22797254.2023.2173659
15. Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China R. Chen et al. 10.3390/rs15163958
16. Evaluación comparativa de los algoritmos de aprendizaje automático Support Vector Machine y Random Forest J. Garzón Barrero et al. 10.18359/rcin.6996
17. Eco-climatological modeling approach for exploring spatiotemporal dynamics of ecosystem service values in response to land use and land cover changes in Riyadh, Saudi Arabia A. Kafy & H. Altuwaijri 10.1007/s00704-024-05199-9
18. Assessing and Enhancing Predictive Efficacy of Machine Learning Models in Urban Land Dynamics: A Comparative Study Using Multi-Resolution Satellite Data M. Safabakhshpachehkenari & H. Tonooka 10.3390/rs15184495
19. Utilizing multitemporal indices and spectral bands of Sentinel-2 to enhance land use and land cover classification with random forest and support vector machine A. Arfa & M. Minaei 10.1016/j.asr.2024.08.062
20. Data mining techniques for LULC analysis using sparse labels and multisource data integration for the hilly terrain of Nilgiris district, Tamil Nadu, India R. Kumaraperumal et al. 10.1007/s12145-024-01586-y
21. Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation D. Gómez-Fernández et al. 10.1016/j.ecoinf.2024.102738
22. Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach N. Khan et al. 10.1007/s11356-024-33090-7
23. Support vector machine-based spatiotemporal land use land cover change analysis in a complex urban and rural landscape of Akaki river catchment, a Suburb of Addis Ababa, Ethiopia H. Tamirat et al. 10.1016/j.heliyon.2023.e22510
24. Extracting Urban Built-up Areas from Optical and Radar Data Fusion using Machine Learning Algorithms W. Woreket & G. Zeleke 10.1080/19479832.2024.2338736
25. Integration Sentinel-1 SAR data and machine learning for land subsidence in-depth analysis in the North Coast of Central Java, Indonesia A. Yananto et al. 10.1007/s12145-024-01413-4
26. Mapping Prosopis L. (Mesquites) Using Sentinel‐2 MSI Satellite Data, NDVI and SVI Spectral Indices with Maximum‐Likelihood and Random Forest Classifiers Y. Ouma et al. 10.1155/2023/8882730
27. Evaluation of machine learning algorithm capability for Bosten Lake Wetland classification based on multi-temporal Sentinel-2 data F. Xia & G. Lv 10.1016/j.ecoinf.2024.102839
28. Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery M. Ganjirad & H. Bagheri 10.1016/j.ecoinf.2024.102498
29. Land-Use and Land-Cover Changes in Cottbus City and Spree-Neisse District, Germany, in the Last Two Decades: A Study Using Remote Sensing Data and Google Earth Engine R. Ahmed et al. 10.3390/rs16152773
30. Relative performance evaluation of machine learning algorithms for land use classification using multispectral moderate resolution data T. Ramachandra et al. 10.1007/s42452-023-05496-4
31. Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning A. Shohan et al. 10.1007/s11356-024-33091-6