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Communication Dans Un Congrès Année : 2021

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

Résumé

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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Dates et versions

hal-03066937 , version 1 (15-12-2020)

Identifiants

Citer

Robin Jarry, Marc Chaumont, Laure Berti-Équille, Gérard Subsol. Assessment of CNN-based Methods for Poverty Estimation from Satellite Images. PRRS 2021 - 11th IAPR International Workshop on Pattern Recognition in Remote Sensing, Jan 2021, Milan, Italy. pp.550-565, ⟨10.1007/978-3-030-68787-8_40⟩. ⟨hal-03066937⟩
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