Austrian cadastral map

Abstract

Many Venetian maps and cadasters were digitized in the Venice Time Machine project. Cadastral maps offer a detailed representation of properties in a specific area. As a part of the Time Machine, a pipeline was created to extract geometries from the 1808 Napoleonian cadastral map of Venice. The goal of this project is to use this pipeline to extract geometries from the 1848 Austrian cadaster and adapt it as necessary. The extracted geometries could then be used to compare the different shapes of the city of Venice in 1808, 1848 and eventually today.

Planning

Project steps

1. First overview

• Install the pipeline used on the 1808 cadaster and train a few models on it (done)
• Use the models trained to predict geometries on the Austrian cadaster (done)

2. Training on the Austrian cadaster

• Georeference the Austrian cadaster
• Prepare training data from the Austrian cadaster (make masks)
• Adapt model to the Austrian cadaster
• Train models on the prepared data
• Evaluate models

3. Optimizing of post-processing

• Understand the post-processing done on the probability maps
• Research similar solutions
• Try to optimize post-processing

4. Extensions (if time)

• Compare new 1848 geometries to 1808 ones
• Make some statistics on the similarities/differences

Timetable

Timeframe	Model	Post-processing
Weeks 8-10	Finish georeferencing	Examine post-processing
Week 10-11	Prepare training data from Austrian cadaster, adapt model
Weeks 11-13	Train and test models on Austrian cadaster	Research and optimize post-processing
Week 13	Combine and evaluate results	If time, compare with 1808 geometries
Week 14	Final Project presentation

Methods

• DHSegment-torch :

Historical document segmentation has been an issue in Digital Humanities for a number of years, due to the diversity of these documents. DHSegment is a method that uses a generic CNN-architecture that can be used for multiple different processing tasks.

This method consists of two steps. The first step takes the images the type of documents to be processed and the masks associated as input to train a Fully Convolutional Neural Network. When given a new image corresponding to the same type of document, the network will output a map of label probabilities associated with each pixel. The second step is post-processing. It takes the probabilities map and using standard image processing techniques, transforms it to an output depending on the task.