Color Palette Identification and Intelligent Knowledge Extraction for Natural Disaster Mapping

Natural disaster emergency cartography requires high semantic accuracy in color design and efficient visual communication. However, existing studies still lack systematic palette analysis and knowledge organization methods based on real-world emergency maps. To address this gap, this study proposes a framework for palette analysis and knowledge organization that uses publicly available Emergency Response Coordination Centre (ERCC)’s emergency maps as the primary data source. The framework extracts disaster types and thematic mapping indicators. It performs palette identification, matching, and statistical analysis using color information from legend regions in the RGB, HSV, and CIELab color spaces, together with the ColorBrewer palette system. Based on the statistical matching results, we constructed a structured knowledge graph that links disaster types, thematic mapping indicators, and palettes, enabling organized retrieval of palette knowledge. Results show that color extraction from legend regions effectively reduces interference from non-thematic elements and improves the accuracy of palette identification. In addition, palette usage in ERCC emergency maps exhibits clear statistical associations and shared and differentiated patterns, indicating stable yet non-unique associations among disaster themes, thematic mapping indicators, and color palettes. The proposed knowledge graph provides a structured framework for organizing palette knowledge and analyzing semantic relationships in ERCC emergency cartography.