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Traditional cancer rate estimations are often limited in spatial resolutions and lack considerations of environmental factors. Satellite imagery has become a vital data source for monitoring diverse urban environments, supporting applications across environmental, socio-demographic, and public health domains. However, while deep learning (DL) tools, particularly convolutional neural networks, have demonstrated strong performance in extracting features from high-resolution imagery, their reliance on local spatial cues often limits their ability to capture complex, non-local, and higher-order structural information. To overcome this limitation, we propose a novel LLM-based multi-agent coordination system for satellite image analysis, which integrates visual and contextual reasoning through a simplicial contrastive learning framework (Agent- SNN). Our Agent-SNN contains two augmented superpixel-based graphs and maximizes mutual information between their latent simplicial complex representations, thereby enabling the system to learn both local and global topological features. The LLM-based agents generate structured prompts that guide the alignment of these representations across modalities. Experiments with satellite imagery of Los Angeles and San Diego demonstrate that Agent-SNN achieves signi cant improvements over state-of-the-art baselines in regional cancer prevalence estimation tasks. 

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.