Observing Health Outcomes Using Remote Sensing Imagery and Geo-Context Guided Visual Transformer

• URL: http://arxiv.org/abs/2602.00110v1
• Date: Mon, 26 Jan 2026 22:45:28 GMT
• Title: Observing Health Outcomes Using Remote Sensing Imagery and Geo-Context Guided Visual Transformer
• Authors: Yu Li, Guilherme N. DeSouza, Praveen Rao, Chi-Ren Shyu,
• Abstract summary: We propose a novel model that enhances remote sensing imagery processing with guidance from auxiliary geospatial information.<n>Our approach introduces a geospatial embedding mechanism that transforms diverse geospatial data into embedding patches that are spatially aligned with image patches.<n>We show that the proposed framework outperforms existing pretrained geospatial foundation models in predicting disease prevalence.
• Score: 8.825339734603862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual transformers have driven major progress in remote sensing image analysis, particularly in object detection and segmentation. Recent vision-language and multimodal models further extend these capabilities by incorporating auxiliary information, including captions, question and answer pairs, and metadata, which broadens applications beyond conventional computer vision tasks. However, these models are typically optimized for semantic alignment between visual and textual content rather than geospatial understanding, and therefore are not suited for representing or reasoning with structured geospatial layers. In this study, we propose a novel model that enhances remote sensing imagery processing with guidance from auxiliary geospatial information. Our approach introduces a geospatial embedding mechanism that transforms diverse geospatial data into embedding patches that are spatially aligned with image patches. To facilitate cross-modal interaction, we design a guided attention module that dynamically integrates multimodal information by computing attention weights based on correlations with auxiliary data, thereby directing the model toward the most relevant regions. In addition, the module assigns distinct roles to individual attention heads, allowing the model to capture complementary aspects of the guidance information and improving the interpretability of its predictions. Experimental results demonstrate that the proposed framework outperforms existing pretrained geospatial foundation models in predicting disease prevalence, highlighting its effectiveness in multimodal geospatial understanding.

Related papers

• DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories [52.57197752244638]
  We introduce DeepImageSearch, a novel agentic paradigm that reformulates image retrieval as an autonomous exploration task.<n>Models must plan and perform multi-step reasoning over raw visual histories to locate targets based on implicit contextual cues.<n>We construct DISBench, a challenging benchmark built on interconnected visual data.
  arXiv  Detail & Related papers  (2026-02-11T12:51:10Z)
• GCRPNet: Graph-Enhanced Contextual and Regional Perception Network for Salient Object Detection in Optical Remote Sensing Images [68.33481681452675]
  We propose a graph-enhanced contextual and regional perception network (GCRPNet)<n>It builds upon the Mamba architecture to simultaneously capture long-range dependencies and enhance regional feature representation.<n>It performs adaptive patch scanning on feature maps processed via multi-scale convolutions, thereby capturing rich local region information.
  arXiv  Detail & Related papers  (2025-08-14T11:31:43Z)
• A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning [5.299218284699214]
  High-performance segmentation models are challenged by annotation scarcity and variability across sensors, illumination, and geography.<n>This paper introduces a domain generalization approach to leveraging emerging geospatial foundation models by combining soft-alignment pseudo-labeling with source-to-target generative pre-training.<n> Experiments with hyperspectral and multispectral remote sensing datasets confirm our method's effectiveness in enhancing adaptability and segmentation.
  arXiv  Detail & Related papers  (2025-05-02T19:52:02Z)
• Interactive dense pixel visualizations for time series and model attribution explanations [8.24039921933289]
  DAVOTS is an interactive visual analytics approach to explore raw time series data, activations of neural networks, and attributions in a dense-pixel visualization. We apply clustering approaches to the visualized data domains to highlight groups and present ordering strategies for individual and combined data exploration.
  arXiv  Detail & Related papers  (2024-08-27T14:02:21Z)
• Language Guided Domain Generalized Medical Image Segmentation [68.93124785575739]
  Single source domain generalization holds promise for more reliable and consistent image segmentation across real-world clinical settings. We propose an approach that explicitly leverages textual information by incorporating a contrastive learning mechanism guided by the text encoder features. Our approach achieves favorable performance against existing methods in literature.
  arXiv  Detail & Related papers  (2024-04-01T17:48:15Z)
• Multi-Spectral Image Stitching via Spatial Graph Reasoning [52.27796682972484]
  We propose a spatial graph reasoning based multi-spectral image stitching method. We embed multi-scale complementary features from the same view position into a set of nodes. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features.
  arXiv  Detail & Related papers  (2023-07-31T15:04:52Z)
• Cross-view Geo-localization with Evolving Transformer [7.5800316275498645]
  Cross-view geo-localization is challenging due to drastic appearance and geometry differences across views. We devise a novel geo-localization Transformer (EgoTR) that utilizes the properties of self-attention in Transformer to model global dependencies. Our EgoTR performs favorably against state-of-the-art methods on standard, fine-grained and cross-dataset cross-view geo-localization tasks.
  arXiv  Detail & Related papers  (2021-07-02T05:33:14Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Damage detection using in-domain and cross-domain transfer learning [4.111375269316102]
  We propose a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges. We show that the combination of cross-domain and in-domain transfer persistently shows superior performance even with tiny datasets.
  arXiv  Detail & Related papers  (2021-02-07T17:36:27Z)
• Unsupervised Discovery of Disentangled Manifolds in GANs [74.24771216154105]
  Interpretable generation process is beneficial to various image editing applications. We propose a framework to discover interpretable directions in the latent space given arbitrary pre-trained generative adversarial networks.
  arXiv  Detail & Related papers  (2020-11-24T02:18:08Z)
• Contextual Encoder-Decoder Network for Visual Saliency Prediction [42.047816176307066]
  We propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. We combine the resulting representations with global scene information for accurately predicting visual saliency. Compared to state of the art approaches, the network is based on a lightweight image classification backbone.
  arXiv  Detail & Related papers  (2019-02-18T16:15:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.