A benchmark dataset for binary segmentation and quantification of dust emissions from unsealed roads

HIGHLIGHTS

• who: Asanka De Silva from the (UNIVERSITY) have published the article: A benchmark dataset for binary segmentation and quantification of dust emissions from unsealed roads, in the Journal: (JOURNAL)
• how: The authors conducted a comprehensive analysis of the dataset for multiple state-of-the-art ML algorithms and the results showed that the accuracy of segmentation of dust by different ML models increases from the pioneering vanilla Unet to more advanced architectures such as DeepLabV3.

SUMMARY

The AP-42 dust model developed by the United_States Environmental Protection Agency (USEPA) estimates the . . .

 



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