https://github.com/muonray/drone_archaeology
Coding used to process drone-captured Near-Infrared Images into Normalised Differential Vegetation Index (NDVI) greyscale images which are then further processed using both a segmentation of ndvi around the tomb region followed by a contour overlay in the perimeter of the tombs. Version 2 uses a standard contour, Version 4 is an attempt, with limited success, to overlay feature boxes over the tomb images with the intent to extend the code into more automated feature detection. Updates to this last step will be ongoing.
https://github.com/muonray/drone_archaeology
aerial-imagery archaeology drone feature-detection image-analysis image-processing ndvi prehistoric-archaeology python remote-sensing
Last synced: 11 months ago
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Coding used to process drone-captured Near-Infrared Images into Normalised Differential Vegetation Index (NDVI) greyscale images which are then further processed using both a segmentation of ndvi around the tomb region followed by a contour overlay in the perimeter of the tombs. Version 2 uses a standard contour, Version 4 is an attempt, with limited success, to overlay feature boxes over the tomb images with the intent to extend the code into more automated feature detection. Updates to this last step will be ongoing.
- Host: GitHub
- URL: https://github.com/muonray/drone_archaeology
- Owner: MuonRay
- License: gpl-3.0
- Created: 2021-12-16T19:24:08.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-02-17T03:28:45.000Z (over 4 years ago)
- Last Synced: 2025-03-24T00:57:17.270Z (about 1 year ago)
- Topics: aerial-imagery, archaeology, drone, feature-detection, image-analysis, image-processing, ndvi, prehistoric-archaeology, python, remote-sensing
- Language: Python
- Homepage:
- Size: 18.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Demo Video: https://www.youtube.com/watch?v=iGgtG44AXw4
# Drone_Archaeology
Coding used to process drone-captured Near-Infrared Images into Normalised Differential Vegetation Index (NDVI) greyscale images which are then further processed using both a segmentation of ndvi around the tomb region followed by a contour overlay in the perimeter of the tombs. Version 2 uses a standard contour, Version 4 is an attempt, with limited success, to overlay feature boxes over the tomb images with the intent to extend the code into more automated feature detection. Updates to this last step will be ongoing.