Jerusalem 1840-1949 Road Extraction and Alignment: Difference between revisions
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dhSegment is a generic approach for Historical Document Processing. It relies on a Convolutional Neural Network to predict pixelwise characteristics. | dhSegment is a generic approach for Historical Document Processing. It relies on a Convolutional Neural Network to predict pixelwise characteristics. | ||
[[File:The network architecture is adapted from ResNet-50.jpg|550px|thumb | [[File:The network architecture is adapted from ResNet-50.jpg|550px|thumb|Figure 1: network architecture]] | ||
==Results== | ==Results== | ||
Revision as of 07:56, 24 November 2021
Introduction
In this work, we present a semantic segmentation model based on neural networks for historical city maps. Based on the Jerusalem Old City corpora, we propose a new automatic map alignment method that surpasses the state of the art in terms of flexibility and performance.
Motivation
The creation of large digital databases on urban development is a strategic challenge, which could lead to new discoveries in urban planning, environmental sciences, sociology, economics, and in a considerable number of scientific and social fields. Digital geohistorical data can also be used and valued by cultural institutions. These historical data could also be studied to better understand and optimize the construction of new infrastructures in cities nowadays, and provide humanities scientists with accurate variables that are essential to simulate and analyze urban ecosystems. Now there are many geographic information system platforms that can be directly applied, such as QGIS, ARCGIS,etc. how to digitize and standardize geo-historical data has become the focus of research. We hope to propose a model that can associate geographic historical data with today's digital maps, analyze and study them under the same geographic information platform, same coordinate projection, and the same scale. Eliminate errors caused by scaling, rotation, and the deformation of the map carrier that may exist in historical data and the entire process is automated and efficient.
The scale is restricted to Jerusalem in our project. We are going to do georeferencing among Jerusalem’s historical maps from 1840 to 1949 and the modern map from OpenStreetMap.
Why georeferencing? The overlaid maps reveal changes over time and enable map analysis and discovery.
Why Jerusalem? It is one of the oldest cities in the world, and is considered holy to the three major Abrahamic religions—Judaism, Christianity, and Islam.
We are going to use the wall of the Old City as the feature to georeferenced.
Why the Old City? The region outside the Old City has seen many new constructions while the Old City has not great changes.
Why the wall? Among the maps, the shape of the wall is relatively more consistent than other features like road networks.
Methodology
Dataset:
126 historical maps of Jerusalem from 1837 to 1938.
Modern geographical data of Jerusalem from OpenStreetMap.
- Wall Extraction:
dhSegment is a generic approach for Historical Document Processing. It relies on a Convolutional Neural Network to predict pixelwise characteristics.
Results
Project Plan and Milestones
| Date | Task | Completion |
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| By Week 12 |
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