ICDAR 2021 Competition on Historical Map Segmentation
Not on the map. Why?
Abstract (arXiv)
This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task~1 consists in detecting building blocks and was won by the L3IRIS team using a DenseNet-121 network trained in a weakly supervised fashion. This task is evaluated on 3 large images containing hundreds of shapes to detect. Task~2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. Task~3 consists in locating intersection points of geo-referencing lines, and was also won by the UWB team who used a dedicated pipeline combining binarization, line detection with Hough transform, candidate filtering, and template matching for intersection refinement. Tasks~2 and~3 are evaluated on 95 map sheets with complex content. Dataset, evaluation tools and results are available under permissive licensing at urlhttps://icdar21-mapseg.github.io/.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 4 of its 15 references that are in the map. First posted on arXiv 27 May 2021.
Builds on
4 of 15 references.
References · 4
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2018Panoptic Segmentationcited by 802
- 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 2.
5 papers.
- 2024MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Mapscited by 7
- 2025Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Modelscited by 3
- 2025MapSAM2: Adapting SAM2 for Automatic Segmentation of Historical Map Images and Time Seriescited by 1
- 2022BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentationcited by 0
- 2023Linear Object Detection in Document Images using Multiple Object Trackingcited by 0
- 2025MapSAM2: Adapting SAM2 for Automatic Segmentation of Historical Map Images and Time Series31 Oct 2025
- 2025Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Models27 Jun 2025
- 2024MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps11 Nov 2024
- 2023Linear Object Detection in Document Images using Multiple Object Tracking26 May 2023
- 2022BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation23 Jul 2022