Supporting Information for Incorporating image inpainting with machine learning for pollution source identification under spatial data sparsity and uncertainty

The Supporting Information (SI) includes the following parts: Model calibration and verification results; Technical details regarding the used methods; and calculation hypothesis of empirical testing

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-06
DOI
https://doi.org/10.5281/zenodo.15607907
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Supporting Information for Incorporating image inpainting with machine learning for pollution source identification under spatial data sparsity and uncertainty

Zenodo (CERN European Organization for Nuclear Research)
Remote-Sensing Image Classification
article

Supporting Information for Incorporating image inpainting with machine learning for pollution source identification under spatial data sparsity and uncertainty

article en

Abstract

The Supporting Information (SI) includes the following parts: Model calibration and verification results; Technical details regarding the used methods; and calculation hypothesis of empirical testing

Zenodo (CERN European Organization for Nuclear Research)
Climate action
Openalex Percentile: Top 43%
Remote-Sensing Image Classification
DOIOpen Access PDFOpenAlex
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Supporting Information for Incorporating image inpainting with machine learning for pollution source identification under spatial data sparsity and uncertainty · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS