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Personal profile

Personal profile

Dr. Francesco Nex is currently Assistant Professor in the Earth Observation Science (EOS) Department at the University of Twente.

Francesco Nex received his master degree in Environmental Engineering (2006) and his PhD degree (2010) from the TU Turin (Italy). From 2011 to 2015 he was with the 3DOM unit of FBK (Italy) working as Marie-Curie Post-Doc in the CIEM Project from 2011 to 2014 and then as Researcher. 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 are in the use of UAV platforms and oblique imagery as well as the automation in the feature extraction and classification of this data. Francesco is author of over 90 publications in international journals and conferences. He is currently involved in three European research projects dealing with UAV imagery: Its4Land, Panoptis and Ingenious.

He is the Chairman of the ICWG I/II (Unmanned Aerial Vehicles: sensors and applications) of the ISPRS (International Society of Photogrammetry and Remote Sensing) and he was the PI of the ISRPS scientific initiativeon the integration of UAVs and oblique imagery.He chairs the UAV-g 2019 conference. He has been serving as guest Editor in several indexed journals and he is involved in the Editorial board of Drones (MDPI).

 

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 GeoInformatics Master courses.
  • Teaching photogrammetry and computer vision in S&C Master course.
  • Supervised/co-supervised MSc theses.

He is currently the coordinator of two courses:

  • Unmanned Aerial Vehicles for Earth Observation (GeoInformatics)
  • 2D and 3D Scene Analysis (System & Control)

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:

Completed Projects at the University of Twente:

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 Dive into the research topics where Francesco Nex is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Unmanned aerial vehicles (UAV) Engineering & Materials Science
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nadir Earth & Environmental Sciences

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2007 2019

10 Citations (Scopus)
9 Downloads (Pure)

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Duarte, D., Nex, F., Kerle, N. & Vosselman, G., 14 Oct 2018, In : Remote sensing. 10, 10, p. 1-26 26 p., 1636.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
image classification
damage
imagery
remote sensing
computer vision
9 Citations (Scopus)
1 Downloads (Pure)

A deep learning approach to DTM extraction from imagery using rule-based training labels

Gevaert, C. M., Persello, C., Nex, F. & Vosselman, G., 1 Aug 2018, In : ISPRS journal of photogrammetry and remote sensing. 142, p. 106-123 18 p.

Research output: Contribution to journalArticleAcademicpeer-review

digital terrain model
imagery
learning
Labels
education
6 Citations (Scopus)

A fully automatic approach to register mobile mapping and airborne imagery to support the correction of platform trajectories in GNSS-denied urban areas

Jende, P. L. H., Nex, F. C., Gerke, M. & Vosselman, G., 1 Jul 2018, In : ISPRS journal of photogrammetry and remote sensing. 141, p. 86-99 14 p.

Research output: Contribution to journalArticleAcademicpeer-review

airborne sensing
GNSS
registers
imagery
platforms
9 Citations (Scopus)
68 Downloads (Pure)

Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment

Cusicanqui, J., Kerle, N. & Nex, F. C., 8 Jun 2018, In : Natural hazards and earth system sciences. 18, p. 1583-1598 16 p.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
disaster
imagery
damage
video
damage assessment
3 Citations (Scopus)
205 Downloads (Pure)

Multi-Temporal Classification And Change Detection Using UAV Images

Makuti, S., Nex, F. C. & Yang, M. Y., 6 Jun 2018, 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 4–7 June 2018, Riva del Garda, Italy. Riva Del Garda: International Society for Photogrammetry and Remote Sensing (ISPRS), p. 651-658 8 p. (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; vol. XLII-2).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Unmanned aerial vehicles (UAV)
detection
building construction
Image acquisition
detection method