Deep Learning Methods for Inferring Industrial CO<sub>2</sub> Hotspots from Co-Emitted NO<sub>2</sub> Plumes

Deep Learning Methods for Inferring Industrial CO<sub>2</sub> Hotspots from Co-Emitted NO<sub>2</sub> Plumes

The “top-down” global stocktake (GST) requires the processing of vast volumes of hyperspectral data to derive emission information, placing greater demands on data processing efficiency. Deep learning, leveraging its strengths in the automated and rapid analysis of image datasets, holds significant...

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Bibliographic Details
Main Authors: Erchang Sun, Shichao Wu, Xianhua Wang, Hanhan Ye, Hailiang Shi, Yuan An, Chao Li
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Remote Sensing
Subjects:
CO<sub>2</sub> hotspots
NO<sub>2</sub> plumes
global stocktake
CNN
deep learning
Online Access:https://www.mdpi.com/2072-4292/17/7/1167
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