Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting originating from Warsaw, Poland, by Ne wsRx correspondents, research stated, “The article presents an analysis of the e ffectiveness of selected machine learning methods: Random Forest (RF), Extreme G radient Boosting (XGB), and Support Vector Machine (SVM) in the classification o f land use and cover in satellite images.” Our news correspondents obtained a quote from the research from Warsaw Universit y of Technology: “Several variants of each algorithm were tested, adopting diffe rent parameters typical for each of them. Each variant was classified multiple ( 20) times, using training samples of different sizes: from 100 pixels to 200,000 pixels. The tests were conducted independently on 3 Sentinel-2 satellite images , identifying 5 basic land cover classes: built-up areas, soil, forest, water, and low vegetation. Typical metrics were used for the accuracy assessment: Cohen’s kappa coefficie nt, overall accuracy (for whole images), as well as F-1 score, precision, and re call (for individual classes). The results obtained for different images were co nsistent and clearly indicated an increase in classification accuracy with the i ncrease in the size of the training sample.”
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