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Related Experiment Video

Updated: Jan 13, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

From Film to Data: Automating Meta-Feature Extraction in Historical Aerial Imagery.

Felix Dahle¹, Yushan Liu², Roderik Lindenbergh¹

¹Department of Geoscience and Remote Sensing, TU Delft, Delft, The Netherlands.

Journal of Photogrammetry, Remote Sensing and Geoinformation Science

|January 12, 2026

Summary

This study introduces an automated workflow using computer vision and machine learning to extract crucial camera parameters from historical aerial images, improving 3D reconstruction accuracy for valuable geospatial data.

Keywords:

Altimeter Computer vision Fiducial marks Historical imagery Meta-data

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Last Updated: Jan 13, 2026

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Area of Science:

Geospatial Science
Computer Vision
Photogrammetry

Background:

Historical aerial imagery is vital for regions with limited geospatial data.
Structure-from-Motion (SfM) requires accurate camera parameters for 3D reconstruction.
Manual feature extraction from historical images is time-consuming and automated methods struggle with poor quality.

Purpose of the Study:

To develop an automated workflow for detecting and extracting key features from historical aerial images.
To address challenges with image quality and missing data through estimation protocols.
To improve the accuracy and efficiency of 3D model generation from historical aerial archives.

Main Methods:

Combined computer vision and machine learning for automated feature detection (fiducial marks, text annotations).
Developed estimation protocols to compensate for missing or unreliable detections using multi-path redundancy.
Evaluated on the Trimetrogon Aerial (TMA) archive of Antarctic Peninsula images.

Main Results:

High success rates in detecting fiducial marks, image subsets, and textual annotations.
Usable focal length data extracted from ~70% of images; high accuracy in fiducial mark detection.
Altitude data extraction was challenging (15% success) due to degraded altimeter readings.

Conclusions:

The automated workflow effectively estimates missing parameters for robust image reconstruction.
The method significantly reduces manual effort and improves data utility from historical aerial imagery.
Open-source code is available, promoting further research and application in historical geospatial data analysis.