Geo-Referencing and Analysis of Entities Extracted from Old Drawings and Photos Using Computer Vision and Deep Learning Algorithms

Abstract

This study offers a quantitative solution that automates the creation of a historical timeline starting with old drawings from the beginning of the 18th century and ending with present-day photographs of the Old City of Jerusalem. This is performed using GIScience approaches, computer vision, and deep learning. The motivation to select the Old City of Jerusalem is the substantial availability of old archival drawings and photographs, owing to the area’s significance throughout the years. This task is challenging, as drawings, old photographs, and new photographs exhibit distinct characteristics. Our method encompasses several key components for the analyses: a 2D location recommendation engine, which detects an approximate location in the image of 3D landmarks; 2D landmarks to 3D conversion; and 2D polygonal areas to 3D GIS polylines conversion. This is applied to the segmentation of built areas. To achieve more accurate results, Meta’s Segment Anything model was utilized, which eliminates the need for extensive data preparation, training, and validation, thus optimizing the process. Using such techniques enabled us to examine the landscape development throughout the last three centuries and gain deeper insights concerning the evolution of prominent landmarks and features such as built area over time.

Keywords

• GIScience
• deep learning
• computer vision
• geo-referencing
• Jerusalem
• Segment Anything