Personal profile

Personal profile

Francesco Nex received his master degree in Environmental Engineering at the Politecnico di Torino in 2006 and a PhD in Geomatics in 2010 in the same University. From 2011 to 2015 he was with the 3DOM (3D Optical Metrology) unit of FBK (Bruno Kessler Foundation, Trento, Italy) working as Post-Doc in the CIEM Project (co-founded Marie-Curie Actions 7thF.P.) from 2011 to 2014 and then as Researcher in the 3DSolarWeb project and the RAPIDMAP project (Concert-Japan Project funded by EU 7th F.P.).

In 2015, Francesco moved to the University of Twente, where he is currently Assistant Professor at the faculty of Geo-Information Science and Earth Observation (ITC), department of Earth Observation Science (EOS). His main research interests concern the use of UAV platforms and the automation in feature extraction from 3D geo-referenced data as well as the integration of LiDAR and photogrammetric data. He is author of over 70 publications in journals and international conferences. He is the secretary of the ICWG I/Vb (Unmanned Aerial Vehicles: sensors and applications) of the International Society of Photogrammetry and Remote Sensing (ISPRS) and he is Principal Investigator of the scientific initiative A new benchmark dataset for multi-platform very high resolution photogrammetry.

Expertise

UAV, feature extraction, 3D modelling, 3D mapping, LiDAR, photogrammetry

Prizes and awards

  • Finalist at the Licinio Ferretti Awards 2010 (supported by BLOM-CGR S.p.A.)
  • Winner at the Licinio Ferretti Awards 2011 (supported by BLOM-CGR S.p.A.)
  • Sensors (MDPI) Best Paper Awards 2013

See also: Francesco's publications on ResearchGate

Teaching

Activities in education

The duties in education cover core modules of Photogrammetry, UAV and 3D modeling.

  • Teaching basics in photogrammetry and 3D modelling in GFM courses.
  • Supervised/co-supervised MSc theses.

Research interests

Activities in research

Within the topic "UAV for real-time 3D mapping" the current focus is on geometric and semantic information extraction from UAV images.

Ongoing projects and activities:

A leaflet describing our activities in the area of mapping from UAV at ITC can be downloaded here.

Projects

Relevant past projects:

  • RapidMap: CONCERT- Japan project (EU funded project in the International Cooperation Activities under the Capacities Programme of the F.P.7th for Research and Technology Development - http://rapidmap.fbk.eu/)
  • Galileo Project 2013-2014: Automated scene information extraction from a joint analysis of aerial remote sensing images and their photogrammetric DSM - Partner: Grenoble Institute of Technology (Grenoble INP) -  GIPSA Lab, France
  • STEM Project: the project is commissioned by the Autonomous Province of Trento.
  • COSCH Project: Cost Action TD 1201 (Short Term Scientific Mission)
  • CIEM Project (Cartographic Information Extraction and Management): co-founded Marie-Curie Actions 7th F.P. - PCOFOUND- GA-2008-226070, acronym “Trentino Project”

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

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Research Output 2007 2018

Antennas
Data acquisition
Cameras
Trajectories
Availability

Exploring UAV in Indonesian cadastral boundary data acquisition

Ramadhani, S. A., Bennett, R. M. & Nex, F. C. 2018 In : Earth Science Informatics. 11, 1, p. 129-146

Research output: Scientific - peer-reviewArticle

data acquisition
2 Citations

Accurate Estimation Of Orientation Parameters Of Uav Images Through Image Registration With Aerial Oblique Imagery

Onyango, F. A., Nex, F. C., Peter, M. S. & Jende, P. L. H. 2017 Proceedings of ISPRS Hannover Workshop : HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany. Heipke, C. & [et al], . (eds.). International Society for Photogrammetry and Remote Sensing (ISPRS), p. 599-605 7 p. (ISPRS Archives Volume XLII-1/W1)

Research output: Scientific - peer-reviewConference contribution

Image registration
Antennas
6 Citations
Neural networks
Damage detection
Disasters
Antennas
Supervised learning