CMA: An End-to-End System for Reverse Engineering Choropleth Map Images

Choropleth maps are widely used geovisualizations due to their simplicity, especially for applications involving political, climate, and other geospatial data for contiguous regions. There is a need for automated data extraction from such maps to aid the human-in-the-loop in handling cognitive overload from large-scale visualization generation and visual impairments. There are gaps in generalizing such a system for choropleth maps with different types of color legends. We propose the choropleth map analytics (CMA) system to address these gaps using a six-step workflow involving deep learning (DL) architectures and tools. We propose a novel method for color-to-data mapping for different color legend types. We finally demonstrate the usability of CMA for a set of choropleth images in climate research for a text summarization application. Our work is a step toward reverse engineering choropleth visualizations. Our code and curated datasets are at:
https://github.com/GVCL/Choropleth-CMA