Switzerland Road extraction from historical maps

Introduction

Historical maps provide valuable information about spatial transformation of the landscape over time spans. This project, based on historical maps of Switzerland, is to vectorize road network and landcover and to visualize the transformation using a machine vision library developed at the DHLAB.

The main data source of this project is GeoVITe (Geodata Versatile Information Transfer environment),a browser-based access to geodata for research and teaching, operated by the Institute of Cartography and Geoinformation of ETH Zurich (IKG) since 2008.

Figure1:Dufour map of Switzerland divided by sequencial grids(1:25000)

Motivation

Historical maps contain rich information, which are helpful in urban planning, historical study, and various humanities research. Digitization of massive printed documents is a significant step before further research. However, most historical maps are scanned in rasterized graphical images. To conveniently use geographic data extracted from these maps in GIS software, vectorization is needed.

However, vectorization process has always been a challenge due to manual painting. In this project, we try to use dh-segmentation tool for automatic vectorization. With 60 high-resolution patches(1km*1km) for the training dataset, the model is tested on randomly selected patches and proposed to approximate idealized main roads of Dufour map of Switzerland.

Plan and Milestones

Milestones:

' Milestone 1: '

• Choose the topic for the project, present the first ideas. Get familiar with the data provided.
• Define the final subject of the project. Find reliable sources of information. Get familiar with the dhSegment tool.

' Milestone 2: '

• Prepare a small dataset (60 samples) for training and testing with Convolutional Neural Network, main part of the dhSegment tool. This dataset should consist of small patches extracted from the Dufour Map of Switzerland, their versions in jpeg format and binary labels created in GIMP.
• Try the dhSegment tool with the created dataset. Evaluate the results, modify the algorithm. Make conclusions about using this tool for road extraction, advantages and disadvantages of this approach.

' Milestone 3: '

• Download the final dataset automatically using the python script: patches with corresponding coordinates completely covering the whole map of Switzerland.
• Test the dhSegment tool on the final dataset.

' Milestone 4: '

• Get the vectorised map of roads with skeletonization in Python using OpenCV.
• Visualise the results and prepare a final presentation.

Date	Task	Completion
By Week 4	Brainstorm, select project, and raise bold and feasible ideas Present preliminary proposal and modify it according to feedbacks	✓
By Week 6	Literature review on relevant road extraction research Experiment on possible solutions Determine overall methodology	✓
By Week 8	Prepare the training dataset of square patches of the Dufour Map downloaded from GeoVITe Create labels, binary masks with black background and white roads, using GIMP Prepare the mid-term presentation	✓
By Week 10	Test on the pre-trained model Improve and modify the algorithm of the segmentation tool Work on road extraction of the whole map	...
By Week 12	Finish Georeferencing and finalize the vectorized map Determine implement of visualization	...
By Week 14	Sort out all the data in the Github repository Prepare final report and presentation	...

Methodology

Dataset:

• Dufour Map from GeoVITe
• The 1:100 000 Topographic Map of Switzerland was the first official series of maps that encompassed the whole of Switzerland. It was published in the period from 1845 to 1865 and thus coincides with the creation of the modern Swiss Confederation.

• Classification : Main roads

• Layer: Topographic Raster Maps - Historical Maps - Dufour Maps

• Coordinate system: CH1903/LV03

• Predefined Grids: 1:25000

• Patch Size: 1000 by 1000 pixels

DhSegment:

A generic framework for historical document processing using Deep learning approach, created by Benoit Seguin and Sofia Ares Oliveira at DHLAB, EPFL.

Data preparation:

• GeoVITe: Automatic data crawling / Manually data accessing

• Swisstopo: black-and-white images -> difficult to annotate with low resolution

Labeling:

60 patches (1000x1000 pixels) using GIMP for model testing:

• Original patches with spatial information: tiff
• Patches for training: jpeg
• labels in black and white: png

Figure2:a region of Bischofszell

Figure3:label of the sample patch

Testing:

Limitation

The main limitation of our project is due to the data source platform. GeoVITE only allows small patches downloading, while automatic downloading from other sources leads to unsatisfying low-quality images.

Links

• Github repository: [1]
• GeoVITE :[2]
• dhSegment:[3]