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Communication dans un congrès

Assessment of CNN-based Methods for Poverty Estimation from Satellite Images

Abstract : One of the major issues in predicting poverty with satellite images is the lack of fine-grained and reliable poverty indicators. To address this problem, various methodologies were proposed recently. Most recent approaches use a proxy (e.g., nighttime light), as an additional information, to mitigate the problem of sparse data. They consist in building and training a CNN with a large set of images, which is then used as a feature extractor. Ultimately, pairs of extracted feature vectors and poverty labels are used to learn a regression model to predict the poverty indicators. First, we propose a rigorous comparative study of such approaches based on a unified framework and a common set of images. We observed that the geographic displacement on the spatial coordinates of poverty observations degrades the prediction performances of all the methods. Therefore, we present a new methodology combining grid-cell selection and ensembling that improves the poverty prediction to handle coordinate displacement.
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Contributeur : Laure Berti-Equille <>
Soumis le : mardi 15 décembre 2020 - 14:54:59
Dernière modification le : mercredi 30 juin 2021 - 21:40:11
Archivage à long terme le : : mardi 16 mars 2021 - 19:47:46


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  • HAL Id : hal-03066937, version 1


Robin Jarry, Marc Chaumont, Laure Berti-Équille, Gérard Subsol. Assessment of CNN-based Methods for Poverty Estimation from Satellite Images. 11th IAPR International Workshop on Pattern Recognition in Remote Sensing (PRRS) in conjunction with the International Conference on Pattern Recognition (ICPR 2020),, Jan 2021, Milan, Italy. ⟨hal-03066937⟩



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