EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 596 (2024) E3S Web Conf., 596 (2024) 01003 References
Open Access
Issue
E3S Web Conf.
Volume 596, 2024
International Conference on Civil, Materials, and Environment for Sustainability (ICCMES 2024)
Article Number 01003
Number of page(s) 7
Section Civil, Materials and Environment for Sustainability ICCMES 2024
DOI https://doi.org/10.1051/e3sconf/202459601003
Published online 22 November 2024
  1. W. Feng, F. Guan, C. Sun, and W. Xu, Feature-Differencing-Based Self-Supervised Pre-Training for Land-Use/Land-Cover Change Detection in High-Resolution Remote Sensing Images. Land. 13(7), 927 (2024). https://www.mdpi.com/2073-445X/13/7/927 [CrossRef] [Google Scholar]
  2. A. Patel, D. Vyas, N. Chaudhari, R. Patel, K. Patel, and D. Mehta, Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city. Results in Engineering. 21, 101788 (2024). https://doi.org/10.1016/j.rineng.2024.101788 [CrossRef] [Google Scholar]
  3. B. Ayana, F. Senbeta, and A. Seyoum, Analyses of LULC dynamics in a socio-ecological system of the Bale Mountains Eco Region of Southeast Ethiopia. Environmental Monitoring and Assessment. 196(7), 644 (2024) [CrossRef] [PubMed] [Google Scholar]
  4. I. Chathuranika, B. Khaniya, K. Neupane, K. M. Rustamjonovich, and U. Rathnayake, Implementation of water-saving agro-technologies and irrigation methods in agriculture of Uzbekistan on a large scale as an urgent issue. Sustainable Water Resources Management. 8(5), 155 (2022) [CrossRef] [Google Scholar]
  5. Z. Zhao, F. Islam, L. A. Waseem, A. Tariq, M. Nawaz, I. U. Islam, T. Bibi, N. U. Rehman, W. Ahmad, R. W. Aslam, D. Raza, and W. A. Hatamleh, Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification. Rangeland Ecology & Management. 92, 129–137 (2024). https://doi.org/10.1016/j.rama.2023.10.007 [CrossRef] [Google Scholar]
  6. S. Davari, S. Eslamian, and M. B. Huda, Impact of Changing Climate on Land Use and Land Cover: A Comprehensive Review. Handbook of Climate Change Impacts on River Basin Management. 319–341 (2024) [Google Scholar]
  7. A. Tariq, J. Yan, A. S. Gagnon, M. Riaz Khan, and F. Mumtaz, Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science. 26(3), 302–320 (2023) [CrossRef] [Google Scholar]
  8. E. R. Sujatha and V. Sridhar, Spatial Prediction of Erosion Risk of a small mountainous watershed using RUSLE: A case-study of the Palar sub- watershed in Kodaikanal, South India. Water. 10(11), 1608 (2018) [CrossRef] [Google Scholar]
  9. R. Mary, R. Nasir, A. Alam, A. Tariq, R. Nawaz, S. Javied, Q. uz Zaman, F. Islam, and S. N. Khan, Exploring hazard quotient, cancer risk, and health risks of toxic metals of the Mehmood Booti and Lakhodair landfill groundwaters, Pakistan. Environmental Nanotechnology, Monitoring & Management. 20, 100838 (2023) [CrossRef] [Google Scholar]
  10. G. Deng, H. Jiang, S. Zhu, Y. Wen, C. He, X. Wang, L. Sheng, Y. Guo, and Y. Cao, Projecting the response of ecological risk to land use/land cover change in ecologically fragile regions. Science of The Total Environment. 914, 169908 (2024). https://doi.org/10.1016/j.scitotenv.2024.169908 [CrossRef] [Google Scholar]
  11. M. Saponaro and E. Tarantino. LULC Classification Performance of Supervised and Unsupervised Algorithms on UAV-Orthomosaics. in International Conference on Computational Science and Its Applications. (2022) [Google Scholar]
  12. 1R. Peacock and M. Missouri, Accuracy assessment of supervised and unsupervised classification using Landsat imagery of Little Rock, Arkansas. Master of Science thesis. (2014) [Google Scholar]
  13. J. Aryal, C. Sitaula, and A. C. Frery, Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia. Scientific Reports. 13(1), 13510 (2023) [CrossRef] [PubMed] [Google Scholar]
  14. X. Han, J. Pan, and A. T. Devlin, Remote sensing study of wetlands in the Pearl River Delta during 1995–2015 with the support vector machine method. Frontiers of Earth Science. 12(3), 521–531 (2018). 10.1007/s11707-017-0672-x [CrossRef] [Google Scholar]
  15. P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, Random Forests for land cover classification. Pattern Recognition Letters. 27(4), 294–300 (2006). https://doi.org/10.1016/j.patrec.2005.08.011 [CrossRef] [Google Scholar]
  16. C. Avcı, M. Budak, N. Yağmur, and F. Balçık, Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences. 8(1), 1–10 (2023) [CrossRef] [Google Scholar]
  17. G. Abebe, D. Getachew, and A. Ewunetu, Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences. 4(1), 30 (2021) [Google Scholar]
  18. D. Tewabe and T. Fentahun, Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science. 6(1), 1778998 (2020) [CrossRef] [Google Scholar]
  19. R. R. Julia L. Classifying Images in ArcGIS for Desktop 10.4. (2016) Available from: https://community.esri.com/t5/esri-technical-support-blog/classifying-images-in-arcgis-for-desktop-10-4/ba-p/901086. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.