ICDAR 2021

- **Revision:** v1.0.0-full-20210527a
- **DOI:** 10.5281/zenodo.4817662
- **Authors:** J. Chazalon, E. Carlinet, Y. Chen, J. Perret, C. Mallet, B. Duménieu and T. Géraud
- **Official competition website:** https://icdar21-mapseg.github.io/

This is the dataset of the ICDAR 2021 Competition on Historical Map Segmentation (“MapSeg”).  
This competition ran from November 2020 to April 2021.

## Motivation
This competition aims as encouraging research in the digitization of historical maps. In order to be usable in historical studies, information contained in such images need to be extracted. The general pipeline involves multiples stages; we list some essential ones here:

- segment map content: locate the area of the image which contains map content;
- extract map object from different layers: detect objects like roads, buildings, building blocks, rivers, etc. to create geometric data;
- georeference the map: by detecting objects at known geographic coordinate, compute the transformation to turn geometric objects into geographic ones (which can be overlaid on current maps).


## Tasks
The tasks we propose simulate the three essential digitization steps we just mentioned.

### Task 1: “Detect Building Blocks”
This task is the flagship of this competition.
Given a fragment of map sheet image focused on map content, you need to detect the building blocks.

Building blocks are symbolized by a thick line.
They do not overlap between themselves, but many other elements can perturb their detection:

- special buildings (hatched areas) can be included in building blocks (sometimes they cover the building block completely);
- text can be overlaid on lines;
- those maps contain many lines which need to be filtered out (internal building structures, railways, rivers, gardens…).

Expected output for this task is a binary mask indicating for each pixel whether it belongs to a building block or not.
Evaluation tools also tolerate a label map in TIFF format, where each pixel is labelled with the identifier of the shape it belongs to, using an INT16.
To extract shapes from the binary mask, 4-connectivity is used (hence the background has 8-connectivity).

### Task 2: “Segment Map Area”
This tasks is the equivalent of text area detection for OCR: given the image of a complete map sheet, you need to segment the area which contains map content.

This area is usually well separated from the other elements (title, legend, scale…) by several frames but sometimes map contents exceeds the frame for some large objects.
While most of the area is delineated by straight lines, some objects were drawn outside the frame on several sheets.
We decided to segment each of those regions as closely as possible.

Expected output for this task is a binary mask indicating for each pixel whether it belongs to the map area or not.

### Task 3: “Locate Graticule Lines Intersections”
This task is essential to the georeferencing of the map: graticule lines are lines which indicate the North/South/East/West coordinates relative to the reference point. Their intersection points are very useful to provide key points for the registration of the map image.

Given the image of a complete map sheet, you need to locate the intersection points of such lines.

These lines usually cover the map content from left to right or from top to bottom but beware:

- due to document aging paper sheets are not flat anymore and lines are not straight;
- lines may be in diagonal for some areas;
- lines can be overlaid with many other objects.

Expected output for this task is a list of coordinates (in image referential, i.e. `0,0` at top left, x-axis pointing to the right and y-axis pointing downward).

Introducing paper: ICDAR 2021 Competition on Historical Map Segmentation