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Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7209-9623
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-1145-4297
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2023 (English)In: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Deep convolutional neural networks (CNNs) have been shown to predict poverty and development indicators from satellite images with surprising accuracy. This paper presents a first attempt at analyzing the CNNs responses in detail and explaining the basis for the predictions. The CNN model, while trained on relatively low resolution day- and night-time satellite images, is able to outperform human subjects who look at high-resolution images in ranking the Wealth Index categories. Multiple explainability experiments performed on the model indicate the importance of the sizes of the objects, pixel colors in the image, and provide a visualization of the importance of different structures in input images. A visualization is also provided of type images that maximize the network prediction of Wealth Index, which provides clues on what the CNN prediction is based on.

Place, publisher, year, edition, pages
IEEE, 2023.
Keywords [en]
Poverty prediction, Deep Convolutional Neural Networks, Satellite Images, Explainable AI
National Category
Signal Processing
Research subject
Smart Cities and Communities
Identifiers
URN: urn:nbn:se:hh:diva-51353DOI: 10.1109/DSAA60987.2023.10302541ISBN: 979-8-3503-4503-2 (electronic)ISBN: 979-8-3503-4504-9 (print)OAI: oai:DiVA.org:hh-51353DiVA, id: diva2:1787038
Conference
2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), Thessaloniki, Greece, 9-13 October, 2023
Funder
Riksbankens JubileumsfondAvailable from: 2023-08-10 Created: 2023-08-10 Last updated: 2023-12-07Bibliographically approved

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fulltext(1730 kB)40 downloads
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Sarmadi, HamidRögnvaldsson, ThorsteinnCarlsson, Nils RogerOhlsson, Mattias

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Citation style
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  • ieee
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