Learning to Extract Data from the Geospatial Heatmap Visualizations

Geospatial heatmaps have become a predominant visualization modality for urban data analysis, particularly for representing population distributions and mobility patterns. While offering rich spatial insights, these visualizations exhibit greater structural complexity compared to conventional charts, and are frequently compromised by non-expert designers contravening perceptual design principles. We propose a novel four-stage pipeline for data recovery: (1) Layer decoupling through generative adversarial networks (GANs) that disentangle geospatial and density map components; (2) Dual extraction process combining SLIC (Simple Linear Iterative Clustering) superpixel analysis for color decoding with hybrid OCR-image matching for coordinate alignment; (3) Iterative generative estimation for density map deconstruction; (4) Multimodal fusion integrating color, spatial, and density features for source data reconstruction. Our experimental evaluation leverages a multimodal corpus comprising 6670 synthetic samples and 67 real-world heatmaps curated from scientific literature and news media. The proposed method demonstrates three transformative applications: (1) Vectorization of raster heatmaps enabling multiscale interaction; (2) Dynamic recoloring for perceptual optimization; (3) Explanatory overlay generation through automated statistical annotation. Quantitative evaluations show 0.94 correlation ( R^2 ) in data recovery accuracy, with 61