Print Email Facebook Twitter Automated Map Generalization Title Automated Map Generalization: Emerging Techniques and New Trends (Editorial) Author Zhang, Xiang (Sun Yat-sen University) Touya, Guillaume (Université Gustave Eiffel; IGN; École nationale des sciences géographiques (ENSG)) Meijers, B.M. (TU Delft Digital Technologies) Date 2024 Abstract Automated map generalization has been a major area of research for decades but has still not reached maturity. Besides the needs for more adaptive algorithms, a fundamental question remains: How can we transfer human generalization knowledge into a computational system more effectively? Previous efforts do not seem capable to fully overcome the “knowledge acquisition bottleneck.” As new theories and technologies emerged in artificial intelligence (particularly deep learning), computers are now able to tackle human-level tasks with superior performance, showing great potential in automated generalization. Meanwhile, crowdsourced geographic information and social sensing is growing at an increasing speed, and the needs for visualizing and analyzing massive geo-referenced data at various scales are numerous. It is therefore necessary to adapt map generalization to these fields. This highlights the potential of applying map generalization in the visual, interactive, and exploratory analysis of abstract (e.g., hierarchical relations) and physical (e.g., movement trajectories) data. This topical collection brings six contributions reporting recent progress and trends in automated generalization in various aspects mentioned above, with which we hope to trigger further discussion and research in our field with new ideas and methodologies. Subject Deep learningMap generalizationVisualization To reference this document use: http://resolver.tudelft.nl/uuid:e4f173b9-f95e-4458-8c96-10f18c202ed9 DOI https://doi.org/10.1007/s41651-024-00174-4 Embargo date 2024-09-27 ISSN 2509-8810 Source Journal of Geovisualization and Spatial Analysis, 8 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type contribution to periodical Rights © 2024 Xiang Zhang, Guillaume Touya, B.M. Meijers Files file embargo until 2024-09-27