DOI: 10.5281/zenodo.16278380
title: Cartographic Sign Detection Dataset (CaSiDD)

description: The Cartographic Sign Detection Dataset (CaSiDD) comprises 796 manually annotated historical map samples, corresponding to 18,750 cartographic signs, such as icons and symbols. Moreover, the signs are categorized into 24 distinct classes, such as tree, mill, hill, religious edifice, or grave. The original images are part of the Semap dataset [1].

The dataset is published in the context of R. Petitpierre's PhD thesis: Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration [2]. Details on the annotation process and statistics on the annotated cartographic signs are provided in the manuscript.

Organization of the data

The data is organized following the COCO dataset format.

project_root/          ├── classes.txt        ├── images/            │   ├── train/         │   │   ├── image1.png │   │   └── image2.png │   └── val/           │       ├── image3.png │       └── image4.png └── labels/                ├── train/             │   ├── image1.txt     │   └── image2.txt     └── val/                   ├── image3.txt         └── image4.txt 

Label syntax

The labels are stored in separate text files, one for each image. In the text files, object classes and coordinates are stored line by line, using the following syntax:

class_id x_center y_center width height

Where x is the horizontal axis. The dimensions are expressed relative to the size of the labeled image. Example:

13 0.095339 0.271003 0.061719 0.0271611 0.154258 0.490052 0.017370 0.019010 8 0.317982 0.556484 0.017370 0.014063 

Classes

0  battlefield1  tree2  train (e.g. wagon)3  mill (watermill or windmill)4  bridge5  settlement or building6  army7  grave8  bush9  marsh10  grass11  vine12  religious monument13  hill/mountain14  cannon15  rock16  tower17  signal or survey point18  gate (e.g. city gate)19  ship/boat/shipwreck20  station (e.g. metro/tram/train station)21  dam/lock22  harbor23  well/basin/reservoir24  miscellaneous (e.g. post office, spring, hospital, school, etc.)

Model weights

A YOLOv10 model yolov10_single_class_model.pt, trained as described in [2], is provided for convenience and reproducibility. The model does not support multi-class object detection. The YOLOv10 implementation used is distributed by Ultralytics [3].

Descriptive statistics

Number of distinct classes: 24 + miscNumber of image samples: 796Number of annotations: 18,750Study period: 1492–1948.

Use and Citation

For any mention of this dataset, please cite :

@misc{casidd_petitpierre_2025,  author = {Petitpierre, R{\'{e}}mi and Jiang, Jiaming},  title = {{Cartographic Sign Detection Dataset (CaSiDD)}},  year = {2025},  publisher = {EPFL},  url = {https://doi.org/10.5281/zenodo.16278380}}@phdthesis{studying_maps_petitpierre_2025,  author = {Petitpierre, R{\'{e}}mi},  title = {{Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration}},  year = {2025},  school = {EPFL}}

Corresponding author

Rémi PETITPIERRE - remi.petitpierre@epfl.ch - ORCID - Github -  Scholar - ResearchGate 

Work ethics

85% of the data were annotated by RP. The remainder was annotated by JJ, a master's student from EPFL, Switzerland.

License

This project is licensed under the CC BY 4.0 License. See the license_images file for details about the respective reuse policy of digitized map images.

Liability

We do not assume any liability for the use of this dataset.

References



Petitpierre R., Gomez Donoso D., Kriesel B. (2025) Semantic Segmentation Map Dataset (Semap). EPFL. https://doi.org/10.5281/zenodo.16164781

Petitpierre R. (2025) Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration. PhD thesis. École Polytechnique Fédérale de Lausanne.

Jocher G. et al. (2024) Ultralytics YOLO. v8.3.39. https://github.com/ultralytics/ultralytics