Techniques and Challenges of the Machine Learning Method for Land Use/Land Cover (LU/LC) Classification in Remote Sensing Using the Google Earth Engine
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Abstract
In order to accurately observe the globe, land use and land cover are crucial. Due to the proliferation of several global modifications associated with the existence of the planet, land use/land cover (LU/LC) classification is now regarded as a topic of highest significance in the natural environment and an important field to be researched by researchers. Google Earth provides satellite image dataset which contains high-resolution images; these images are used to analyze the land area. In order to address the dearth of review articles throughout the land use/land cover classification phase, we proposed a full evaluation, which might help researchers continue their work. Therefore, the purpose of this study is to investigate the methodical steps involved in classifying land use and land cover utilizing the Google Earth platform. The most widely used techniques researchers employ to achieve LU/LC classification using Google Earth Engine are examined in this work. The classification of land use and land cover for a specific region using time series was covered in this study, along with the many types of land use and land cover classes and the approach employed by Google Earth. The limits of the GEE tool and difficulties encountered during the process of classifying land use and cover have also been covered in this survey document. The importance of this review rests in inspiring future scholars to tackle the LU/LC analysis problem successfully, and this study offers researchers a road map for assessing land use/land cover classification.
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Christopher Potter, (2020). Changes in Growing Season Phenology Following Wildfires in Alaska. Remote Sensing in Earth Systems Sciences. https://doi.org/10.1007/s41976-020-00038
Ibrahim-Bathis K, Syed Ashfaq Ahmed,(2019) Geospa-tial Approach for Evaluating LULC Pattern in the Dodda-halla Watershed, Chitradurga District, India”, Remote Sensing in Earth Systems Sciences:108–119
Yinghuai Huang, Xiaoping Liu, Xia Li, Yuchao Yan, and Jinpei Ou, (2018) Comparing the Effects of Temporal Features Derived from Synthetic Time-Series NDVI on Fine Land Cover Classification, IEEE journal of selected topics in applied earth observations and remote sensing.
Nataliia Kussul, Mykola Lavreniuk, Sergii Skakun, and Andrii Shelestov, (2017). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data, IEEE geoscience and remote sensing letters.
R. Torres et al. (2012) “GMES Sentinel-1 mission,” Remote Sens. Environ. 9–24.
Nagy A, Feher J, Tamas J (2018). Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Compute Electron Agric. 41–49. https:// doi.org/10.1016/j.compag.2018.05.035.
Bikash Ranjan Parida, Avinash Kumar Ranjan, (2019) Wheat Acreage Mapping and Yield Prediction Using Land-sat-8 OLI Satellite Data: A Case Study in Sahibganj Prov-ince, Jharkhand (India)”, Remote Sensing in Earth Systems Sciences.96–107
Potter C (2015) Vegetation cover change in Yellow-stone National Park detected using Landsat satellite image analysis. J Biodivers Manage Forestry.4:3
Christopher Potter, (2019) Changes in Vegetation Cover of Yellowstone National Park Estimated from MODIS Greenness Trends, 2000 to 2018”, Remote Sensing in Earth Systems Sciences.147–160
NRSC (2006) Land use / land cover database on 1:50,000 scale, Natural Resources Census Project, LUCMD, LRUMG, RS & GIS AA, National Remote Sens-ing Centre, ISRO, Hyderabad.
Shivani Agarwal & Harini Nagendra (2020): Classifi-cation of Indian cities using Google Earth Engine, Journal of Land Use Science, DOI:0.1080/1747423X.2020.1720842
Ragettli, Silvan; Herberz, Timo; Siegfried, (2018) "An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia" Remote Sens. 10, no. 11: 1823. https://doi.org/10.3390/rs10111823
Himanshu Sharma, Vijay Kumar Joshi. (2023). An Efficient Load Balancing Approach For Resource Utilizations In Green Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 395–404. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2735
Shobita Shetty, Enschede, The Netherlands, March, (2019) Analysis of Machine Learning Classifiers for LULC Classification on Google Earth Engine (Ph.D. Thesis)
Fonji SF, Taff GN (2014) Using satellite data to moni-tor land-use land cover change in North-eastern Latvia. Springer plus 3(1):61. https://doi.org/10.1186/2193-1801-3-61
Usman B (2013) Satellite imagery land cover classifi-cation using K means clustering algorithm: computer vi-sion for environmental information extraction. Elixir Jour-nal of Computer Science and Eng: 18671–18675
Sánchez-Ruiz, Sergio & Moreno, Alvaro & Piles, Maria & Maselli, Fabio & Carrara, Arnaud & Running, Steven & Gilabert, M.A.. (2016). Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data. International Journal of Digital Earth. 10. 1-16. 10.1080/17538947.2016.1247301.
Zhengyang Lin, Fang Chen, Zheng Niu, Bin Li, Bo Yu, Huicong Jia, Meimei Zhang,( 2018) An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data, Remote Sensing of Environment,Volume 211,Pages 376-387, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2018.04.027.
Lu, Shanlong & Jia, Li & Zhang, Lei & Wei, Yongping & Baig, Muhammad Hasan Ali & Zhai, Zhaokun & Meng, Ji-hua & Li, Xiaosong & Zhang, Guifang. (2017). Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product. Remote Sensing Letters. 8. 224-233. 10.1080/2150704X.2016.1260178.
Christopher Davies, Matthew Martinez, Catalina Fernández, Ana Flores, Anders Pedersen. Predicting Dropout Risk in Higher Education Using Machine Learning. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/170
N.A Wahap and Helmi Z.M. Shafri,(2020),Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia,IOP Conference Series: Earth and Environmental Science, https://doi.org/10.1088/1755-1315/540/1/012003
Toutin T. (2003) Geometric Correction of Remotely Sensed Images. In: Wulder M.A., Franklin S.E. (eds) Remote Sensing of Forest Environments. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0306-4_6
Kshirsagar, D. P. R. ., Patil, D. N. N. ., & Makarand L., M. . (2022). User Profile Based on Spreading Activation Ontology Recommendation. Research Journal of Computer Systems and Engineering, 3(1), 73–77. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/45
Xiao, Sa & Tian, Xinpeng & Liu, Qiang & Wen, Jianguang & Ma, Yushuang & Song, Zhenwei. (2018). A SEMI-EMPIRICAL TOPOGRAPHIC CORRECTION MODEL FOR MULTI-SOURCE SATELLITE IMAGES. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. IV-3. 225-232. 10.5194/isprs-annals-IV-3-225-2018.
Wagle, N.; Acharya, T.D.; Kolluru, V.; Huang, H.; Lee, D.H. Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods. Appl. Sci. 2020, 10, 8083. https://doi.org/10.3390/app10228083
Marie Delalay, Varun Tiwari, Alan D. Ziegler, Vik Gopal, and Paul Passy "Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: operational method and its implementation for a mountainous area of Nepal," Journal of Applied Remote Sensing 13(1), 014530 (28 March 2019). https://doi.org/10.1117/1.JRS.13.014530
Dr. Naveen Jain. (2020). Artificial Neural Network Models for Material Classification by Photon Scattering Analysis. International Journal of New Practices in Management and Engineering, 9(03), 01 - 04. https://doi.org/10.17762/ijnpme.v9i03.88
Kaspar Hurni, Andreas Heinimann, and Lukas Würsch (2017) Google Earth Engine Image Pre-processing Tool: Background and Methods, Centre for Development and Environment (CDE) University of Bern.
Yuhao Jin, Xiaoping Liu, Jing Yao, Xiaoxiang Zhang & Han Zhang (2020) Mapping the annual dynamics of cultivated land in typical area of the Middle-lower Yangtze plain using long time-series of Landsat images based on Google Earth Engine, International Journal of Remote Sensing, 41:4, 1625-1644, DOI: 10.1080/01431161.2019.1673917
Yulia Sokolova, Deep Learning for Emotion Recognition in Human-Computer Interaction , Machine Learning Applications Conference Proceedings, Vol 3 2023.
Kareem, R.S.A., Ramanjineyulu, A.G., Rajan, R. , MK Gupta, et al. (2021). Multilabel land cover aerial image classification using convolutional neural networks. Arab J Geosci 14, 1681 (2021). https://doi.org/10.1007/s12517-021-07791-z
A Jangid, MK Gupta ( 2021) Investigating the Effect of Lockdown During COVID-19 on Land Surface Temperature Using Machine Learning Technique by Google Earth Engine: Analysis of Rajasthan, India. Communication and Intelligent Systems, 2021
SR Dogiwal, PJ Desai, MK Gupta, V Goyal 2019, Plant Leaf Classification Using Supervised Classification Algorithm International Journal of Computer Systems vol 3 issue 12, 2019.