Estimating Socioeconomic Proxy Variables Using Multimodal Deep Learning Models
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5618-6_35
Reference19 articles.
1. Abitbol, J.L., Karsai, M.: Interpretable socioeconomic status inference from aerial imagery through urban patterns. Nat. Mach. Intell. 2(11), 684–692 (2020)
2. Aiken, E., Rolf, E., Blumenstock, J.: Fairness and representation in satellite-based poverty maps: evidence of urban-rural disparities and their impacts on downstream policy. arXiv preprint arXiv:2305.01783 (2023)
3. Chefer, H., Gur, S., Wolf, L.: Transformer interpretability beyond attention visualization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 782–791 (2021)
4. Chen, B., et al.: Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network. Int. J. Appl. Earth Obs. Geoinf. 109, 102794 (2022)
5. Chen, C.F.R., Fan, Q., Panda, R.: CrossViT: cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 357–366 (2021)
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