Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery-Reference-Cited by-同舟云学术

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Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery

Published:2022-06 Issue: Volume:5 Page:100052
ISSN:2666-0172
Container-title:Science of Remote Sensing
language:en
Short-container-title:Science of Remote Sensing

Author:

Jiang Ziyang^ORCID,Zheng Tongshu,Bergin Mike,Carlson David^ORCID

Funder

Department of Energy

Publisher

Elsevier BV

Reference41 articles.

1. High-resolution mapping of traffic related air pollution with google street view cars and incidence of cardiovascular events within neighborhoods in oakland, ca;Alexeeff;Environ. Health,2018

2. High-resolution air pollution mapping with google street view cars: exploiting big data;Apte;Environ. Sci. Technol.,2017

3. A geographically and temporally weighted regression model for ground-level pm2.5 estimation from satellite-derived 500 m resolution aod;Bai;Rem. Sens.,2016

4. Uncovering the structure of clinical eeg signals with self-supervised learning;Banville;J. Neural. Eng.,2021

5. Incorporating low-cost sensor measurements into high-resolution pm2.5 modeling at a large spatial scale;Bi;Environ. Sci. Technol.,2020

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1. A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow;Atmospheric Environment;2024-12

2. Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks;Frontiers in Environmental Science;2024-07-09

3. Designing electrodes and electrolytes for batteries by leveraging deep learning;Nano Research Energy;2024-06

4. Quantifying the contribution of environmental variables to cyclists’ exposure to PM2.5 using machine learning techniques;Heliyon;2024-01

5. Unmasking air quality: A novel image-based approach to align public perception with pollution levels;Environment International;2023-11

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