Jan Skaloud

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Senior Scientist

jan.skaloud@epfl.ch +41 21 693 27 53

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Google Scholar ID

+41 21 693 27 53
Office: GC C2 397
EPFL > ENAC > ENAC-SSIE > SSIE-ENS

+41 21 693 27 53
Office: GC C2 397
EPFL > VPA-AVP-DLE > AVP-DLE-EDOC > EDCE-ENS

+41 21 693 27 53
Office: GC C2 397
EPFL > ENAC > IIE > CRYOS

Web site: Web site: https://cryos.epfl.ch

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Administrative data

Fields of expertise

Satellite positioning, inertial and integrated navigation Sensor orientation and calibration, attitude determination Mobile mapping, airborne laser scanning Adjustment methods, Kalman Filtering

Mission

Teaching in 3 EPFL sections and 2 faculties, director of ESO lab.

Editorial

ISPRS Journal of Photogrammetry and Remote Sensing, 2012-current, editorial board IEEE Transaction on Vehicular Technology IEEE Transaction on Aerospace and Electronic Systems IEEE Transaction on Radar, Sonar & Navigation IEEE Transaction on Robotics and Autonomous Systems IEEE Transaction on Instrumentation and Measurement IEEE Transaction on Pattern Recognition IEEE Transaction on Signal Processing IEEE Geoscience and Remote Sensing Letters ISPRS Journal of Photogrammetry and Remote Sensing Photogrammetry Engineering and Remote Sensing Canadian Aeronautics and Space Journal Canadian Journal on Remote Sensing Geomatica Photogrammetry Records Advances in Space Research Measurement Science and Technology Remote Sensing Journal of photogrammetry, remote sensing and geo-information processing (PFG) Survey Review

Books / Monographs

J. Skaloud and M. Cramer, Data Capture, in GIS Handbook, W. Kresse and D. Danko, Eds. Springer, 2011. D. Lichti and J. Skaloud, Registration and Calibration, in Airborne and terrestrial laser scanning, H.-G. Maas and G. Vosselman, Eds. Whittles Publishing, 2010. J. Skaloud, Reliability of direct georeferencing phase 1: An overview of the current approaches and possibilities, in Checking and Improving of Digital Terrain Models / Reliability of Direct Georeferencing, EuroSDR Official Publication 51, 2006. K. Legat, J. Skaloud, and R. Schmidt, Reliability of direct georeferencing phase 2: A case study on practical problems and solutions., in Checking and Improving of Digital Terrain Models / Reliability of Direct Georeferencing., EuroSDR Official Publication 51, 2006.

Awards

2021 : Samuel Gamble Award, carrier contribution in photogrammetry & sensing : International Society for Photogrammetry and Remote Sensing

2020 : big U.V. Helava Award - best paper in J ISPRS over 2016-2019 period : International Society for Photogrammetry and Remote Sensing

2017 : Best Demo Award - 4th IEEE International Workshop on Metrology & Aerospace : IEEE

2014 : Hansa Luftbild Award - best-paper of the year in PFG journal : PFG Journal

2012 : Karl Kraus Medal - best textbook in Photogrammetry, Remote Sensing and GIS : International Society for Photogrammetry and Remote Sensing

2009 : GNSS Leader to Watch - Innovation Award : GPS World journal

Publications

Infoscience publications

Teaching & PhD

Teaching

Environmental Sciences and Engineering

PhD Students

Brun Aurélien Arnaud, Carreaud Antoine Paul, Joseph Paul Kenneth, Lahaye Jesse Ray Murray, Mouzakidou Kyriaki,

Past EPFL PhD Students

Barbieux Kevin Sylvain , Clausen Philipp , Clédat Emmanuel Pierre Quentin , Garcia Crespillo Omar , Khaghani Mehran , Laupré Gabriel François , Longobardi Pasquale , Rehak Martin , Schär Philipp , Sharma Aman , Stebler Yannick , Wägli Adrian ,

Courses

Inference for large-scale time series with application to sensor fusion

Large-scale time series analysis is performed by a new statistical tool that is superior to other estimators of complex state-space models. The identified stochastic dependences can be used for sensor fusion by Bayesian (e.g. Kalman) filtering or for studying changes in natural/biological phenomena.

Estimation methods

The students treat observations affected by uncertainty in a rigorous manner. They master the main methods to adjust measurements and to estimate parameters. They apply specific models to real-world problems encountered in various experimental sciences.

Sensing and spatial modeling for earth observation

Students get acquainted with the process of mapping from images (orthophoto and DEM), as well as with methods for monitoring the Earth surface using remotely sensed data. Methods will span from machine learning to geostatistics and model the spatiotemporal variability of processes.

Sensor orientation

Robotics practicals

The goal of this lab series is to practice the various theoretical frameworks acquired in the courses on a variety of robots, ranging from industrial robots to autonomous mobile robots, to robotic devices, all the way to interactive robots.