A semi-automatic tool to georeference historical landscape images
- Published
- Accepted
- Subject Areas
- Human-Computer Interaction, Computer Education, Computer Vision, Graphics, Spatial and Geographic Information Systems
- Keywords
- pairwise matching, crowdsourcing, georeferencing, historical landscape images, automatic geolocalization
- Copyright
- © 2018 Blanc et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. A semi-automatic tool to georeference historical landscape images. PeerJ Preprints 6:e27204v1 https://doi.org/10.7287/peerj.preprints.27204v1
Abstract
Smapshot is a web-based participatory virtual globe where users can georeference historical images of the landscape by clicking a minimum of six well identifiable correspondence points between the image and a 3D virtual globe. The images database is expected to grow exponentially. In a near future, the work of the web users will no longer be enough.
To tackle this issue, we developed a semi-automatic process to georeference images. The volunteers will be shown only images having a maximum number of neighbour images in the matching graph. These neighbour images are the ones with which they share some overlay. This overlap is detected using the SIFT algorithm in a pairewise matching process.
For an image pair made of a reference image with a known pose and a query image we want to georeference, we extracted the 3D world coordinates of the tie points from a digital elevation model.
Then, by running a perspective-n-point algorithm after having geometrically tested the resulting homography between the two images, we compute the 6 degree of freedom pose, i.e. the position (X,Y,Z) and orientation (azimuth, tilt and roll angles) of the query image. The query image then becomes a reference and the georeference computation can be propagated more deeply in the graph structure.
Author Comment
This submission is intended for the OGRS'2018 Collection