Local PM<sub>2.5</sub> Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology

Local PM<sub>2.5</sub> Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology

Satellite-based rapid sweeping screening of localized PM<sub>2.5</sub> hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM&...

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Bibliographic Details
Main Authors: Tongshu Zheng, Michael Bergin, Guoyin Wang, David Carlson
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
fine particulate matter
machine learning
remote sensing
computer vision
satellite imagery
convolutional neural network (CNN)
Online Access:https://www.mdpi.com/2072-4292/13/7/1356

Internet

https://www.mdpi.com/2072-4292/13/7/1356

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