Auto-Calibration of Geolocation & Heading Direction: Segmentation based Matching between Network Camera & Satellite Images
(2025) In Thesis in geographical information technics EXTM05 20251Dept of Physical Geography and Ecosystem Science
- Abstract
- Geospatial data is becoming increasingly important as artificial intelligence develops rapidly, creating new opportunities in the field of Geographic Information Systems. The thesis investigates the possibility of establishing a method to automatically calibrate a network camera’s geolocation and heading direction by using a camera view image and its corresponding satellite image. The aim is to locate the camera’s geolocation with one to two meters accuracy and its general heading direction.
For the method, it is centered around four steps: Object-detection and segmentation, perspective transformation, cross-view image matching and the final calculations of the geolocation and heading direction. Three network cameras were used for the... (More) - Geospatial data is becoming increasingly important as artificial intelligence develops rapidly, creating new opportunities in the field of Geographic Information Systems. The thesis investigates the possibility of establishing a method to automatically calibrate a network camera’s geolocation and heading direction by using a camera view image and its corresponding satellite image. The aim is to locate the camera’s geolocation with one to two meters accuracy and its general heading direction.
For the method, it is centered around four steps: Object-detection and segmentation, perspective transformation, cross-view image matching and the final calculations of the geolocation and heading direction. Three network cameras were used for the testing, all positioned in different locations and heading directions around an office building in Lund, Sweden.
For the results, the method showed potential where the closest calculated geolocation was approximately 11 meters away from the actual position and around 30 degrees off for the heading direction. What was discovered during the thesis was that e.g. none of the cross-view matching models used were ideal for the thesis purposes.
In conclusion, the method has four distinct steps which makes it hard to identify potential local error sources, however, when one is located the following steps improve accordingly. Cross-view matching was deemed the step which had the most influence over the final results and the one needed to improve the most to gain a more accurate geolocation and heading direction of a network camera. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9205194
- author
- Bengtsson, Matilda LU and Björkman, Alba LU
- supervisor
-
- Lars Harrie LU
- organization
- course
- EXTM05 20251
- year
- 2025
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Geo-localization, Cross-view matching, Image segmentation, Object-detection, Bird's Eye View, Perspective Transformation
- publication/series
- Thesis in geographical information technics
- report number
- 41
- language
- English
- id
- 9205194
- date added to LUP
- 2025-06-27 14:02:42
- date last changed
- 2025-06-27 14:02:42
@misc{9205194,
abstract = {{Geospatial data is becoming increasingly important as artificial intelligence develops rapidly, creating new opportunities in the field of Geographic Information Systems. The thesis investigates the possibility of establishing a method to automatically calibrate a network camera’s geolocation and heading direction by using a camera view image and its corresponding satellite image. The aim is to locate the camera’s geolocation with one to two meters accuracy and its general heading direction.
For the method, it is centered around four steps: Object-detection and segmentation, perspective transformation, cross-view image matching and the final calculations of the geolocation and heading direction. Three network cameras were used for the testing, all positioned in different locations and heading directions around an office building in Lund, Sweden.
For the results, the method showed potential where the closest calculated geolocation was approximately 11 meters away from the actual position and around 30 degrees off for the heading direction. What was discovered during the thesis was that e.g. none of the cross-view matching models used were ideal for the thesis purposes.
In conclusion, the method has four distinct steps which makes it hard to identify potential local error sources, however, when one is located the following steps improve accordingly. Cross-view matching was deemed the step which had the most influence over the final results and the one needed to improve the most to gain a more accurate geolocation and heading direction of a network camera.}},
author = {{Bengtsson, Matilda and Björkman, Alba}},
language = {{eng}},
note = {{Student Paper}},
series = {{Thesis in geographical information technics}},
title = {{Auto-Calibration of Geolocation & Heading Direction: Segmentation based Matching between Network Camera & Satellite Images}},
year = {{2025}},
}