首页|Findings from University of New South Wales Yields New Data on Machine Learning (Geotechnical Characterisation of Coal Spoil Piles Using High-resolution Optical and Multispectral Data: a Machine Learning Approach)
Findings from University of New South Wales Yields New Data on Machine Learning (Geotechnical Characterisation of Coal Spoil Piles Using High-resolution Optical and Multispectral Data: a Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Sydney, Australia, by NewsRx journal ists, research stated, “Geotechnical characterisation of spoil piles has traditi onally relied on the expertise of field specialists, which can be both hazardous and time-consuming. Although unmanned aerial vehicles (UAV) show promise as a r emote sensing tool in various applications; accurately segmenting and classifyin g very high-resolution remote sensing images of heterogeneous terrains, such as mining spoil piles with irregular morphologies, presents significant challenges. ”
SydneyAustraliaAustralia and New Zea landCyborgsEmerging TechnologiesMachine LearningRemote SensingUniversi ty of New South Wales
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