332_150x150

## Overview

332_150x150 is a dataset for object detection tasks - it contains 332_150x150 annotations for 316 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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 0.027161
1 0.154258 0.490052 0.017370 0.019010
8 0.317982 0.556484 0.017370 0.014063

Classes

0 battlefield
1 tree
2 train (e.g. wagon)
3 mill (watermill or windmill)
4 bridge
5 settlement or building
6 army
7 grave
8 bush
9 marsh
10 grass
11 vine
12 religious monument
13 hill/mountain
14 cannon
15 rock
16 tower
17 signal or survey point
18 gate (e.g. city gate)
19 ship/boat/shipwreck
20 station (e.g. metro/tram/train station)
21 dam/lock
22 harbor
23 well/basin/reservoir
24 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 + misc
Number of image samples: 796
Number of annotations: 18,750
Study 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

1. Petitpierre R., Gomez Donoso D., Kriesel B. (2025) Semantic Segmentation Map Dataset (Semap). EPFL. https://doi.org/10.5281/zenodo.16164781
2. Petitpierre R. (2025) Studying Maps at Scale: A Digital Investigation of Cartography and the Evolution of Figuration. PhD thesis. École Polytechnique Fédérale de Lausanne.
3. Jocher G. et al. (2024) Ultralytics YOLO. v8.3.39. https://github.com/ultralytics/ultralytics

This data set provides a sample of 6480 labeled images of still hand gestures depicting 14 distinct signs of the Bangla Sign Language (BdSL). It is specifically geared towards helping to advance the real-time communication tools to the deaf and the hard-of-hearing people and specifically in the business world like shopkeeper-customer relationship. The data was formed under the free will of people of different ages and types. The images were shot through the use of different smartphone-specific camera (13 MP and above), in varied real-life conditions. The light conditions include changes in light environment including natural light and dark light, the position and the backgrounds. All the cameras operated the auto exposure mode and had standard settings without manual settings and filters.

Each image in the dataset corresponds to one of 14 commonly used Bangla words or phrases: "আমি" (I), "আপনি" (You), "স্যার" (Sir), "প্যাকেট" (Packet), "বিস্কুট" (Biscuit), "খাওয়া" (Eat), "এক" (One), "দুই" (Two), "তিন" (Three), "চার" (Four), "পাঁচ" (Five), "ওজন" (Weight), "টাকা" (Money), and "আমি তোমাকে ভালোবাসি" (I love you). The data is split into two groups in order to fulfil the requirements of different machine learning reasons: • Training (Detection): This folder consists of annotated images (bounding box) that is used to train object detection models such as YOLOv10. • Testing (Recognition): It has images that are not labeled and images that are labeled with the classes and may be used to test and train a gesture classification model. They are all in JPG format and have been filtered and compressed to make the file size smaller and still maintain quality of the image. This brings the dataset to be more available to the researchers with limited resources of computation. The current data is especially useful to those of us in the sphere of: • recognition of sign language • Human to computer interaction • Supporting technologies of the deaf and hard-of-hearing population The fact that the dataset allows to accurately identify and detect gestures of the Bangla Sign Language enables inclusiveness to its members and closes the communication gap between the hearing impaired and the rest of the society. It can also be used as a benchmark corpus on future study on regional sign language systems, which are usually underexamined in the global datasets.