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Computer Science

D-Index
39
Citations
9710
World Ranking
9562
National Ranking
172

Overview

Francesco Nex is affiliated with the University of Twente in the Netherlands. Their research primarily spans the fields of Computer Science and Engineering, with a focus on subfields such as Computer Vision and Pattern Recognition, Aerospace Engineering, Environmental Engineering, Geology, and Civil and Structural Engineering.

The scientist's work addresses several main topics, including:

  • Robotics and Sensor-Based Localization
  • Remote Sensing and LiDAR Applications
  • 3D Surveying and Cultural Heritage
  • Advanced Vision and Imaging
  • Advanced Neural Network Applications
  • Video Surveillance and Tracking Methods
  • Advanced Image and Video Retrieval Techniques

Frequent coauthors collaborating with Francesco Nex include:

  • Norman Kerle
  • George Vosselman
  • Fabio Remondino
  • Sofia Tilon
  • Michael Ying Yang

The scientist has published extensively in several venues, most notably:

  • ISPRS annals of the photogrammetry, remote sensing and spatial information sciences
  • The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
  • Remote Sensing
  • Drones
  • ISPRS Journal of Photogrammetry and Remote Sensing

Selected recent papers include:

  • "UAV in the advent of the twenties: Where we stand and what is next," 2022, ISPRS Journal of Photogrammetry and Remote Sensing
  • "Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing," 2020, Geo-spatial Information Science
  • "MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms," 2021, Remote Sensing
  • "Use of UAV-based photogrammetry products for semi-automatic detection and classification of asphalt road damage in landslide-affected areas," 2021, Engineering Geology
  • "CNN-Based Dense Monocular Visual SLAM for Real-Time UAV Exploration in Emergency Conditions," 2022, Drones

Best Publications

  • UAV for 3D mapping applications: a review

    Francesco Carlo Nex;Fabio Remondino

  • UAV PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING - CURRENT STATUS AND FUTURE PERSPECTIVES -

    F. Remondino;L. Barazzetti;F.C. Nex;M. Scaioni

  • State of the art in high density image matching

    Fabio Remondino;Maria Grazia Spera;Erica Nocerino;Fabio Menna

  • Review of the Current State of UAV Regulations

    Claudia Stöcker;Rohan Bennett;Francesco Nex;Markus Gerke

  • UAV photogrammetry for mapping and 3d modelling: current status and future perspectives

    F. Remondino;L. Barazzetti;F. Nex;M. Scaioni

  • Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning

    Anand Vetrivel;Markus Gerke;Norman Kerle;Francesco Nex

  • Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation

    Unknown

  • UAV and RPV systems for photogrammetric surveys in archaelogical areas : two tests in the Piedmont region (Italy)

    Filiberto Chiabrando;Francesco Carlo Nex;Dario Piatti;Fulvio Rinaudo

  • Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications

    Andrea Maria Lingua;Davide Marenchino;Francesco Carlo Nex

  • Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping

    Sophie Crommelinck;Rohan Bennett;Markus Gerke;Francesco Nex

  • UAV-Based Structural Damage Mapping: A Review

    Norman Kerle;Francesco Nex;Markus Gerke;Diogo Duarte

  • Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

    Daniele Giordan;Yuichi Hayakawa;Francesco Nex;Fabio Remondino

  • Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions

    Francesco Nex;Diogo Duarte;Fabio Giulio Tonolo;Norman Kerle

  • ISPRS benchmark for multi - platform photogrammetry

    F.C. Nex;M. Gerke;F. Remondino;H.J. Przybilla

  • Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing

    Desta Ekaso;Francesco Nex;Norman Kerle

  • SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH

    D. Duarte;F.C. Nex;N. Kerle;G. Vosselman

  • Use of unmanned aerial vehicles in monitoring application and management of natural hazards

    D. Giordan;A. Manconi;F. Remondino;F. Nex

  • Using UAVs for map creation and updating : a case study in Rwanda

    M. Koeva;M. Muneza;C. Gevaert;M. Gerke

  • Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

    Francesco Nex;Diogo Duarte;Anne Steenbeek;Norman Kerle

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

    Diogo Duarte;Francesco Nex;Norman Kerle;George Vosselman

  • Dense image matching: Comparisons and analyses

    Fabio Remondino;Maria Grazia Spera;Erica Nocerino;Fabio Menna

  • Aerial multi-camera systems: Accuracy and block triangulation issues

    Ewelina Rupnik;Francesco Nex;Isabella Toschi;Fabio Remondino

Frequent Co-Authors

Fabio Remondino
Fabio Remondino Fondazione Bruno Kessler
George Vosselman
George Vosselman University of Twente
Norman Kerle
Norman Kerle University of Twente
Michael Ying Yang
Michael Ying Yang University of Bath
Claudio Persello
Claudio Persello University of Twente
Paolo Tarolli
Paolo Tarolli University of Padua
Cesare Furlanello
Cesare Furlanello Fondazione Bruno Kessler
Andrea Manconi
Andrea Manconi ETH Zurich
C.J. van Westen
C.J. van Westen University of Twente
Alessandro Maria Michetti
Alessandro Maria Michetti University of Insubria

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