首页|Investigators at University of the Western Cape Describe Findings in Machine Lea rning (Remote Sensing-based Land Use Land Cover Classification for the Heuningne s Catchment, Cape Agulhas, South Africa)
Investigators at University of the Western Cape Describe Findings in Machine Lea rning (Remote Sensing-based Land Use Land Cover Classification for the Heuningne s Catchment, Cape Agulhas, South Africa)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting out of Cape Town, South Africa, by NewsRx editors, research stated, “The primary objective of this study was to eva luate the effectiveness of Sentinel 2 and machine-learning technique for classif ying seasonal land use land cover (LULC) changes on an annual basis, in the Heun ingnes Catchment in Cape Agulhas, South Africa. The study focused on July 2017, October 2017, March 2018, and July 2018, representing both dry and wet seasons w ithin the Catchment.” Our news journalists obtained a quote from the research from the University of t he Western Cape, “The study also assessed the rainfall and temperature variation s and how they link with these short-term changes in LULC. The classification re sults revealed a consistent increase in the extent of bare rock and soil cover f rom October 2017 to July 2018. The wet seasons of July 2017 and July 2018 exhibi ted the highest percentage of vegetation cover. The overall accuracy of the SVM classification ranged between 55 % and 75 %, with the wet seasons demonstrating higher overall accuracies of 75 %. The p erformance of SVM was evaluated using kappa statistics, which indicated a modera te to substantial level of agreement ranging from 0.43 to 0.69.”
Cape TownSouth AfricaAfricaCyborgsEmerging TechnologiesMachine LearningRemote SensingUniversity of the Wes tern Cape
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