A more exhaustive description of the job proposal is available at :
ANR HIATUS project aims at automatically processing these archival images to retrieve the evolution of land cover. More specifically, this project aims both at (i) improving the automatic georeferencing / pose estimation of archival surveys in order to retrieve their geometry and derive ortho-images and digital surface models, and (ii) developing method to analyze such data taking into account their associated specificities and uncertainties (e.g. varying qualities and resolutions depending on acquisitions, training ground truth data available only at most recent dates).
Over the recent years, a great deal of work has been devoted to digitizing archival aerial surveys. The aim is now to retrieve camera calibrations, positions and orientations to be able to generate surface models and orthophotos, both necessary for automated land cover classification.
The work of this post-doctoral position is thus defined in the context of georeferencing these historical images. A first operational workflow based on MicMac [Rupnik et al., 2018] has already been developed [Giordano et al., 2018]. More precisely, to meet the specificity of the data, a prototype of automated ground control points extraction using recent geo-referenced images has been put in place. However, this existing solution needs further development concerning several aspects:
I. Cléry, M. Pierrot-Deseilligny, and B. Vallet. (2014). Automatic georeferencing of a heritage of old analog aerial photographs. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3, pp. 33–40.
D. Feurer, F. Vinatier.. (2018). Joining multi-epoch archival aerial images in a single SfM block allows 3-D change detection with almost exclusively image information. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2018, 146, pp. 495 – 506.
S. Giordano, A. Le Bris, and C. Mallet. (2018) .Toward automatic georeferencing of archival aerial images. ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences, 4(2), pp. 105-112.
E. Rupnik, M. Daakir, and M. P. Deseilligny. (2017). Micmac–a free, open-source solution for photogrammetry. Open Geospatial Data, Software and Standards, 2(1):14.
The LaSTIG lab. of IGN, the French national mapping agency is one of the leading laboratories in photogrammetric computer vision, image analysis and remote sensing applied to geospatial imagery and ground based imagery. In particular, it has developed MicMac, an open source photogrammetric software.
- The candidate must have a PhD degree in photogrammetry, image processing or computer vision.
- Good spoken and written English. Knowledge of French would be very useful.
- Good knowledge of programming languages C++/Python.
Duration : 18 months.
Start : first months of 2020, as soon as possible.
Location : Laboratoire en Sciences et Technologies de l'Information Géographique (LaSTIG) at IGN (Saint-Mandé, France).
Application procedure :
- a detailed resume including a list of publications and a description of the projects in which you were involved ;
- a cover letter describing how your research experience is relevant to the position;
- a summary of the Phd thesis;
- recommendation letters.
→ Send all required documents by email in a single pdf file.
- Sébastien GIORDANO (email@example.com)
- Arnaud LE BRIS (firstname.lastname@example.org)
- Ewelina RUPNIK (email@example.com)
(c) GdR 720 ISIS - CNRS - 2011-2020.