Strategies for Semi-Automated Registration of Historic Aerial Photographs Utilizing Street and Roof Segmentations as Durable Landmarks

Gerald Adam Zwettler, Yuta Ono, Michael Franz Stradner, Christoph Praschl

Research output: Contribution to conferencePaperpeer-review

Abstract

Historical and current aerial photographs are only of great value if the geolocation or address of the photographed areas is also available. In Western Europe, especially Austria, Germany and Czech Republic, there is a market for the sale of aerial photographs of one’s own private residential building. Automated geolocation is a feasible way to enable the sales agents to assign the addresses for the sale more quickly. In the course of this research work, a process chain is modeled that allows the assignment of aerial photographs to residential addresses using machine vision. After model-based rectifying the aerial images to compensate for perspective distortions, larger image blocks get assembled using image stitching. The assignment to a 2D reference map, such as satellite imagery via Google Maps, is done by applying a U-Net CNN after extracting durable image features such as roads or buildings. The mapping of aerial imagery to two-dimensional cartography is either automated via registration approaches or based on manually placed corresponding landmarks and homography. Test runs on imagery between the years 1969 and 2020 show that the labor-intensive process of geolocation of aerial imagery can be solved by the proposed process model in a hybrid way.

Original languageEnglish
DOIs
Publication statusPublished - Sep 2022
EventEuropean Modeling & Simulation Symposium 2022 - Rom, Italy
Duration: 19 Sep 202221 Sep 2022
Conference number: 34
https://www.msc-les.org/emss2022/

Conference

ConferenceEuropean Modeling & Simulation Symposium 2022
Abbreviated titleEMSS
Country/TerritoryItaly
CityRom
Period19.09.202221.09.2022
Internet address

Keywords

  • Aerial images
  • CNN Street Segmentation
  • Feature-Based Image Registration
  • Georeferencing
  • SIFT

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