# Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation

arXiv: https://arxiv.org/abs/2101.02144 · HTML: https://mlsanity.com/paper/2101.02144/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [Deep Watershed Transform for Instance Segmentation](https://mlsanity.com/paper/1611.08303/) (2016)
- [Explicit topological priors for deep-learning based image segmentation using persistent homology](https://mlsanity.com/paper/1901.10244/) (2019)
- [Recurrent Instance Segmentation](https://mlsanity.com/paper/1511.08250/) (2015)
- [Roads and cities of 18th century France](https://mlsanity.com/paper/1509.09055/) (2015)

## Built on by (most cited first, 4 of 4)

- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation](https://mlsanity.com/paper/2207.11446/) (2022)
- [Mapping Historic Urban Footprints in France: Balancing Quality, Scalability and AI Techniques](https://mlsanity.com/paper/2510.02097/) (2025)
- [Deep learning enables urban change profiling through alignment of historical maps](https://mlsanity.com/paper/2602.02154/) (2026)

## Abstract (arXiv)

The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps.