On the public citation graph

High-Resolution Ground No2 Estimation Using Deep Learning with Sentinel-2 and Sentinel-5p Data

0 signed · 30 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-981-16-1773-7_9openalex-snapshot
Deep Learning Techniques for Air Pollution Prediction Using Remote Sensing Data
inferredcites →https://doi.org/10.1007/s11356-021-17442-1openalex-snapshot
A systematic literature review of deep learning neural network for time series air quality forecasting
inferredcites →https://doi.org/10.1007/s40572-015-0069-5openalex-snapshot
Socioeconomic Disparities and Air Pollution Exposure: a Global Review
inferredcites →https://doi.org/10.1007/s40726-020-00159-zopenalex-snapshot
Deep Learning for Air Quality Forecasts: a Review
inferredcites →https://doi.org/10.1016/j.atmosenv.2006.03.016openalex-snapshot
Satellite remote sensing of particulate matter and air quality assessment over global cities
inferredcites →https://doi.org/10.1016/j.envint.2012.04.008openalex-snapshot
Associations between short-term exposure to nitrogen dioxide and mortality in 17 Chinese cities: The China Air Pollution and Health Effects Study (CAPES)
inferredcites →https://doi.org/10.1016/j.envpol.2017.03.079openalex-snapshot
Development of PM2.5 and NO2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China
inferredcites →https://doi.org/10.1016/j.isprsjprs.2019.04.015openalex-snapshot
Deep learning in remote sensing applications: A meta-analysis and review
inferredcites →https://doi.org/10.1016/j.jag.2021.102516openalex-snapshot
Tree-based ensemble deep learning model for spatiotemporal surface ozone (O3) prediction and interpretation
inferredcites →https://doi.org/10.1016/j.jenvman.2021.112510openalex-snapshot
Sensors and systems for air quality assessment monitoring and management: A review
inferredcites →https://doi.org/10.1016/j.rse.2021.112351openalex-snapshot
Estimating nitrous oxide (N2O) emissions for the Los Angeles Megacity using mountaintop remote sensing observations
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.145145openalex-snapshot
Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations
inferredcites →https://doi.org/10.1038/274855a0openalex-snapshot
The origin of ozone in the troposphere
inferredcites →https://doi.org/10.1088/1748-9326/acf7d5openalex-snapshot
Intraurban NO2 hotspot detection across multiple air quality products
inferredcites →https://doi.org/10.1109/igarss.2018.8519036openalex-snapshot
Deep Learning for Ground-Level PM2.5 Prediction from Satellite Remote Sensing Data
inferredcites →https://doi.org/10.1109/jstars.2023.3244719openalex-snapshot
NeSNet: A Deep Network for Estimating Near-Surface Pollutant Concentrations
inferredcites →https://doi.org/10.1109/piers53385.2021.9694752openalex-snapshot
Estimating Ground-level Nitrogen Dioxide Concentration from Satellite Data
inferredcites →https://doi.org/10.1109/tgrs.2022.3160827openalex-snapshot
Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing
inferredcites →https://doi.org/10.1109/tgrs.2024.3358397openalex-snapshot
Geo-STO3Net: A Deep Neural Network Integrating Geographical Spatiotemporal Information for Surface Ozone Estimation
inferredcites →https://doi.org/10.1126/sciadv.abf4491openalex-snapshot
PM 2.5 polluters disproportionately and systemically affect people of color in the United States
inferredcites →https://doi.org/10.1164/rccm.201303-0609ocopenalex-snapshot
Spatial Analysis of Air Pollution and Mortality in California
inferredcites →https://doi.org/10.2139/ssrn.4280883openalex-snapshot
A Coupled Deep Learning Model for Estimating Surface NO2 Levels from Remote Sensing Data: 15-Year Study Over the Contiguous United States
inferredcites →https://doi.org/10.3390/ijerph21111534openalex-snapshot
Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri
inferredcites →https://doi.org/10.3390/rs11182096openalex-snapshot
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data
inferredcites →https://doi.org/10.3390/rs14071735openalex-snapshot
Estimation of Ground PM2.5 Concentrations in Pakistan Using Convolutional Neural Network and Multi-Pollutant Satellite Images
inferredcites →https://doi.org/10.3390/rs14122807openalex-snapshot
An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China
inferredcites →https://doi.org/10.4018/ijiit.2019100105openalex-snapshot
Deep Learning-based Framework for Smart Sustainable Cities
inferredcites →https://doi.org/10.48550/arxiv.2204.05917openalex-snapshot
Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network
inferredcites →https://doi.org/10.5194/acp-20-625-2020openalex-snapshot
A very high-resolution assessment and modelling of urban air quality
inferredcites →https://doi.org/10.5194/amt-13-4601-2020openalex-snapshot
High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.