Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration

• URL: http://arxiv.org/abs/2511.19538v1
• Date: Mon, 24 Nov 2025 10:35:37 GMT
• Title: Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration
• Abstract summary: This work leverages a diverse corpus of 771,561 map records and 99,715 digitized images aggregated from 38 digital catalogs.<n>The spatial focus of cartography is analyzed in relation to political dynamics, evidencing links between Atlantic maritime charting, the triangular trade, and colonial expansion.<n>The analysis of land classes shows that maps are designed images whose framing and composition emphasize features through centering and semantic symmetries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This thesis presents methods and datasets to investigate cartographic heritage on a large scale and from a cultural perspective. Heritage institutions worldwide have digitized more than one million maps, and automated techniques now enable large-scale recognition and extraction of map content. Yet these methods have engaged little with the history of cartography, or the view that maps are semantic-symbolic systems, and cultural objects reflecting political and epistemic expectations. This work leverages a diverse corpus of 771,561 map records and 99,715 digitized images aggregated from 38 digital catalogs. After normalization, the dataset includes 236,925 contributors and spans six centuries, from 1492 to 1948. These data make it possible to chart geographic structures and the global chronology of map publication. The spatial focus of cartography is analyzed in relation to political dynamics, evidencing links between Atlantic maritime charting, the triangular trade, and colonial expansion. Further results document the progression of national, domestic focus and the impact of military conflicts on publication volumes. The research introduces semantic segmentation techniques and object detection models for the generic recognition of land classes and cartographic signs, trained on annotated data and synthetic images. The analysis of land classes shows that maps are designed images whose framing and composition emphasize features through centering and semantic symmetries. The study of cartographic figuration encodes 63 M signs and 25 M fragments into a latent visual space, revealing figurative shifts such as the replacement of relief hachures by terrain contours and showing that signs tend to form locally consistent systems. Analyses of collaboration and diffusion highlight the role of legitimacy, larger actors, and major cities in the spread of figurative norms and semiotic cultures.

Related papers

• Evolving Thematic Map Design in Academic Cartography: A Thirty-Year Study Based on Multilingual Journals [5.279229963667604]
  Thematic maps play a central role in academic communication, yet their large-scale design evolution has rarely been examined empirically.<n>This study presents a longitudinal and multilingual analysis of thematic map design practices in academic cartography from 1990 to 2020.
  arXiv  Detail & Related papers  (2026-04-24T13:28:11Z)
• Deep learning enables urban change profiling through alignment of historical maps [22.61017848915787]
  We propose a fully automated, deep learning-based framework for fine-grained urban change analysis from large collections of historical maps.<n>This framework shifts the analysis of historical maps from ad hoc visual comparison toward systematic, quantitative characterization of urban change.
  arXiv  Detail & Related papers  (2026-02-02T14:31:33Z)
• FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models [38.67763789694245]
  We introduce FRIEDA, a benchmark for testing complex open-ended cartographic reasoning in LVLMs.<n> FRIEDA targets all three categories of spatial relations: topological (border, equal, intersect, within), metric (distance), and directional (orientation)<n>Even the strongest models, Gemini-2.5-Pro and GPT-5-Think, achieve only 38.20% and 37.20% accuracy, far below human performance of 84.87%.
  arXiv  Detail & Related papers  (2025-12-08T20:18:15Z)
• GeoSURGE: Geo-localization using Semantic Fusion with Hierarchy of Geographic Embeddings [3.43519422766841]
  We formulate geo-localization as aligning the visual representation of the query image with a learned geographic representation.<n>Our main experiments demonstrate improved all-time bests in 22 out of 25 metrics measured across five benchmark datasets.
  arXiv  Detail & Related papers  (2025-10-01T20:39:48Z)
• Segmenting France Across Four Centuries [31.251646879941486]
  We introduce a new dataset of historical maps tailored for analyzing large-scale, long-term land use and land cover evolution.<n>We provide both comprehensive modern labels and 22,878 km2 of manually annotated historical labels for the 18th and 19th century maps.
  arXiv  Detail & Related papers  (2025-05-30T17:26:52Z)
• MapExplorer: New Content Generation from Low-Dimensional Visualizations [60.02149343347818]
  Low-dimensional visualizations, or "projection maps," are widely used to interpret large-scale and complex datasets.<n>These visualizations not only aid in understanding existing knowledge spaces but also implicitly guide exploration into unknown areas.<n>We introduce MapExplorer, a novel knowledge discovery task that translates coordinates within any projection map into coherent, contextually aligned textual content.
  arXiv  Detail & Related papers  (2024-12-24T20:16:13Z)
• MAPWise: Evaluating Vision-Language Models for Advanced Map Queries [47.15503716894445]
  This study investigates the efficacy of vision-language models (VLMs) in answering questions based on maps. We introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China) Our benchmark incorporates 43 diverse question templates, requiring nuanced understanding of relative spatial relationships, intricate map features, and complex reasoning.
  arXiv  Detail & Related papers  (2024-08-30T20:57:34Z)
• Detecting Omissions in Geographic Maps through Computer Vision [18.36056648425432]
  We develop and evaluate a method for automatically identifying maps that depict specific regions and feature landmarks with designated names. We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names. Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks.
  arXiv  Detail & Related papers  (2024-07-15T13:26:58Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• AceMap: Knowledge Discovery through Academic Graph [90.12694363549483]
  AceMap is an academic system designed for knowledge discovery through academic graph. We present advanced database construction techniques to build the comprehensive AceMap database. AceMap provides advanced analysis capabilities, including tracing the evolution of academic ideas.
  arXiv  Detail & Related papers  (2024-03-05T01:17:56Z)
• Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis [12.025312586542318]
  We present a densely labeled ground truth map of Flanders paired with Sentinel-2 satellite imagery. Our methodology includes a formalized dataset division and sampling method, utilizing the topographic map layout 'Kaartbladversnijdingen,' and a detailed semantic segmentation model training pipeline.
  arXiv  Detail & Related papers  (2024-01-26T22:21:39Z)
• Narrative Cartography with Knowledge Graphs [10.715484138543069]
  We propose the idea of narrative cartography with knowledge graphs (KGs) To tackle the data acquisition & integration challenge, we develop a set of KG-based GeoEnrichment toolboxes. With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format.
  arXiv  Detail & Related papers  (2021-12-02T04:01:17Z)
• Placepedia: Comprehensive Place Understanding with Multi-Faceted Annotations [79.80036503792985]
  We contribute Placepedia, a large-scale place dataset with more than 35M photos from 240K unique places. Besides the photos, each place also comes with massive multi-faceted information, e.g. GDP, population, etc. This dataset, with its large amount of data and rich annotations, allows various studies to be conducted.
  arXiv  Detail & Related papers  (2020-07-07T20:17:01Z)
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
  We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
  arXiv  Detail & Related papers  (2020-04-17T11:55:03Z)

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.