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Proceedings of the ICA
Journal topic
Articles | Volume 4
Proc. Int. Cartogr. Assoc., 4, 84, 2021
https://doi.org/10.5194/ica-proc-4-84-2021
Proc. Int. Cartogr. Assoc., 4, 84, 2021
https://doi.org/10.5194/ica-proc-4-84-2021

  03 Dec 2021

03 Dec 2021

Automatic vectorization of rectangular manmade objects: a case study applying OpenCV and GDAL on UAV imagery

Márton Pál1,2, Fanni Vörös1,2, and Béla Kovács2 Márton Pál et al.
  • 1ELTE Eötvös Loránd University, Doctoral School of Earth Sciences, Hungary
  • 2ELTE Eötvös Loránd University, Faculty of Informatics, Institute of Cartography and Geoinformatics, Hungary

Keywords: OpenCV, GDAL, Python, image processing, UAV

Abstract. UAV imagery has a big role in environmental mapping: various indices regarding plant health, soil condition or geological objects can be determined, or 3D models can be built for accurate measurements. Automatic vectorization of satellite images is widely applied nowadays for land coverage determination purposes. However, larger resolution UAV images are hard to process following this theory: too many details result in a long computing time. We propose a FOSS (free and open-source software) analytical solution for detecting and vectorizing quasi-rectangular shaped (mainly manmade) objects on relatively high-resolution images. Our sample area is the cemetery and its surroundings in Istenmezeje, Heves County, Hungary. The graves are good examples of regular, rectangular manmade objects. The traditional cadastral mapping of these sites means a large amount of digitizing work. We have used Python environment for conducting image analysis: delineating and vectorizing the grave outlines for the large-scale mapping of the cemetery. Open-source programming libraries were used during the process: OpenCV and GDAL/OGR. With these tools, we were able to digitize the graves automatically with systematic errors. Approximately 70–80 of 100 graves were correctly recognised (their number varies depending on the adjustable variables: the size and detailedness of the contours to be detected). Our approach is a relatively new methodology in large-scale cartography: computer vision tools have not been used widely for mapmaking purposes. The development of artificial intelligence and open-source tools connected to it may contribute to the broader dissemination of similar methodologies in cartography and GIS.

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