Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks
(2023) 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 In 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings- Abstract
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... (More)
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.
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- author
- Sarmadi, Hamid ; Rognvaldsson, Thorsteinn ; Carlsson, Nils Roger LU ; Ohlsson, Mattias LU ; Wahab, Ibrahim LU and Hall, Ola LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Deep Convolutional Neural Networks, Explainable AI, Poverty prediction, Satellite Images
- host publication
- 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
- series title
- 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
- editor
- Manolopoulos, Yannis and Zhou, Zhi-Hua
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023
- conference location
- Thessaloniki, Greece
- conference dates
- 2023-10-09 - 2023-10-12
- external identifiers
-
- scopus:85179010090
- ISBN
- 9798350345032
- DOI
- 10.1109/DSAA60987.2023.10302541
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 IEEE.
- id
- a2e6c515-5454-4f25-ae9f-fdc94fe009b7
- date added to LUP
- 2023-12-18 08:07:37
- date last changed
- 2023-12-18 11:34:51
@inproceedings{a2e6c515-5454-4f25-ae9f-fdc94fe009b7, abstract = {{<p>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.</p>}}, author = {{Sarmadi, Hamid and Rognvaldsson, Thorsteinn and Carlsson, Nils Roger and Ohlsson, Mattias and Wahab, Ibrahim and Hall, Ola}}, booktitle = {{2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings}}, editor = {{Manolopoulos, Yannis and Zhou, Zhi-Hua}}, isbn = {{9798350345032}}, keywords = {{Deep Convolutional Neural Networks; Explainable AI; Poverty prediction; Satellite Images}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings}}, title = {{Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks}}, url = {{http://dx.doi.org/10.1109/DSAA60987.2023.10302541}}, doi = {{10.1109/DSAA60987.2023.10302541}}, year = {{2023}}, }