首页|Researchers’ Work from Wuhan University Focuses on Machine Learning (Performance Assessment of Machine Learning Algorithms for Mapping of Land Use/land Cover Us ing Remote Sensing Data)
Researchers’ Work from Wuhan University Focuses on Machine Learning (Performance Assessment of Machine Learning Algorithms for Mapping of Land Use/land Cover Us ing Remote Sensing Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning are discuss ed in a new report. According to news reporting from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “The rapid increase in populatio n accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Wuhan University , “Hence, continues monitoring of LULC changes gained a significant importance f or management of natural resources and assessing the climate change impacts. Rec ently, application of machine learning algorithms on RS (remote sensing) data fo r rapid and accurate mapping of LULC gained significant importance due to growin g need of LULC estimation for ecosystem services, natural resource management an d environmental management. Hence, it is crucial to access and compare the perfo rmance of different machine learning classifiers for accurate mapping of LULC. T he primary objective of this study was to compare the performance of CART (Class ification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machi ne) for LULC estimation by processing RS data on Google Earth Engine (GEE). In t otal four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According t o results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved m aximum accuracy of 89.8% with highest Kappa of 0.84 and CART showe d maximum overall accuracy of 89.7% with Kappa value of 0.79.”
WuhanPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningRemote SensingW uhan University
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