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Satellite imagery is a readily available data source for monitoring a broad range of urban geographical contexts related to environmental, socio-demographic, and health disparities. To analyze satellite images, deep learning (DL) tools efficiently extract latent multi-dimensional characteristics, beyond identifying specific urban elements like roads and houses. However, current DL approaches tend to largely rely on Convolutional Neural Networks applied to high-resolution imagery, and as such may be limited to capturing only local contextual information. To address this fundamental limitation, we propose to fuse the modalities of satellite imagery and a large language model (LLM). In particular, we develop a novel LLM-based Simplicial Contrastive Learning model (LLM-SCL) based on mutual information maximization between the latent simplicial complex-level representations of two kinds of augmented (superpixel) graphs, which allows for cohesive integration of LLM prompts and learning of both local and global higher-order properties of satellite imagery (from all pixels in an image). Extensive experiments on satellite imagery at several resolutions in Tijuana, Mexico, Los Angeles and San Diego, USA, suggest that LLM-SCL significantly outperforms state-of-the-art baselines on unsupervised image classification tasks. As such, the proposed LLM-SCL opens a new path for more accurate evaluations of latent urban forms and their associations with environmental and health outcome disparities.
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Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery
Satellite imagery is being leveraged for many societally critical tasks across climate, economics, and public health. Yet, because of heterogeneity in landscapes (e.g. how a road looks in different places), models can show disparate performance across geographic areas. Given the important potential of disparities in algorithmic systems used in societal contexts, here we consider the risk of urban-rural disparities in identification of land-cover features. This is via semantic segmentation (a common computer vision task in which image regions are labelled according to what is being shown) which uses pre-trained image representations generated via contrastive self-supervised learning. We propose fair dense representation with contrastive learning (FairDCL) as a method for de-biasing the multi-level latent space of a convolution neural network. The method improves feature identification by removing spurious latent representations which are disparately distributed across urban and rural areas, and is achieved in an unsupervised way by contrastive pre-training. The pre-trained image representation mitigates downstream urban-rural prediction disparities and outperforms state-of-the-art baselines on real-world satellite images. Embedding space evaluation and ablation studies further demonstrate FairDCL’s robustness. As generalizability and robustness in geographic imagery is a nascent topic, our work motivates researchers to consider metrics beyond average accuracy in such applications.
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- Award ID(s):
- 1845487
- PAR ID:
- 10592949
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
- Volume:
- 7
- ISSN:
- 3065-8365
- Page Range / eLocation ID:
- 1723 to 1734
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aies.v7i1.31760
