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Titlebook: Remote Sensing Intelligent Interpretation for Mine Geological Environment; From Land Use and La Weitao Chen,Xianju Li,Lizhe Wang Book 2022

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楼主: trace-mineral
发表于 2025-3-23 11:10:38 | 显示全部楼层
on the fine-scale classification method of mining land cover based on semantic segmentation..The book is a valuable reference both for scholars, practitioners and as well as graduate students who are intereste978-981-19-3741-5978-981-19-3739-2
发表于 2025-3-23 16:21:59 | 显示全部楼层
Mine Remote Sensing Scene Classification Using Deep Learning, and F1-Score values for the models of ResNet50, DenseNet121, ResNext50, and Efficientnet were 58.5% and 59.3%, 61.2% and 61.9%, 60.2% and 60.8%, and 61.4% and 62.5%, respectively. Results showed that the accuracies of the algorithms were not very good, owing to the high inter-class similarity of the constructed dataset.
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发表于 2025-3-24 09:13:55 | 显示全部楼层
Book 2022ffects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of “target detection→scene classification→semantic segmentation." .Taking China’s Hubei Province as an example, this book focuses on the following four aspects: 1.
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发表于 2025-3-24 17:46:54 | 显示全部楼层
Mine Remote Sensing Dataset Construction for Multi-level Tasks,ea. For each dataset, the remote sensing data source, construction method, classification scheme, the partition of training, validation, and test sets, and visual presentation were described in detail.
发表于 2025-3-24 21:36:47 | 显示全部楼层
发表于 2025-3-25 01:25:32 | 显示全部楼层
c mine remote sensing dataset focusing on the multi-level taThis book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets foc
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