support information of Incorporating Image Inpainting with Machine Learning for Pollution Source Identification under Spatial Data Sparsity and Uncertainty

he 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
2025-05-31
DOI
https://doi.org/10.5281/zenodo.15558444
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

support information of Incorporating Image Inpainting with Machine Learning for Pollution Source Identification under Spatial Data Sparsity and Uncertainty

Zenodo (CERN European Organization for Nuclear Research)
Generative Adversarial Networks and Image Synthesis
article

support information of Incorporating Image Inpainting with Machine Learning for Pollution Source Identification under Spatial Data Sparsity and Uncertainty

article en

Abstract

he 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)
Openalex Percentile: Top 52%
Generative Adversarial Networks and Image Synthesis
DOIOpen Access PDFOpenAlex
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support information of 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