Jerusalem 1840-1949 Road Extraction and Alignment
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, stretching, and rotation that may exist in historical data and the entire process is automated and efficient. The scale is restricted to Jerusalem in our project.
Methodology
Results
Limitation
Project Plan and Milestones
- Finished: Finshed as planed
- Delayed: Not finished as planed
- Changed: Replaced by other method or abandoned
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