CV

Basics

Name Viet Nguyen
Label GIS Analyst
Email duc.nguyen@uni-greifswald.de
Phone +49 3834 420 4544
Url https://vietducng.github.io/
Summary A geospatial analyst with 3+ years of experience working with GIS, cartography, remote sensing, and WebGIS. Familiar with programming, photogrammetry, geo data, 3D point cloud, statistics, machine learning, and visualization

Work

  • 2023.11 - present

    Greifswald, Germany

    GIS analyst
    Institute of Geography and Geology, University of Greifswald
    • Analyzed spatial data with Python (GeoPandas, NumPy, GDAL, Rasterio, Multiprocessing, etc.), and R (sf, terra, lidR, rgdal, doParallel, etc.)
    • Performed drone-based data acquisition (spectral, RGB, and LiDAR data)
    • Processed drone-based data with Agisoft Metashape
    • Developed WebGIS using HTML, CSS, JavaScript (Leaflet)
  • 2022.03 - 2022.04

    Lübeck, Germany

    Student Assistant in Terrestrial laser scanning
    Naturwald Akademie
    https://www.pyrophob.de/
    • Planned and prepared for TLS campains
    • Performed TLS measurements in 45 forest plots in Brandenburg
  • 2021.10 - 2023.05

    Eberswalde, Germany

    Intern
    Thünen Institute of Forest Ecosystems
    Forest inventory based on ALS point cloud on large-scale levels (https://winmol.thuenen.de/)
    • Developed methods to derive individual tree attributes (coordinate, tree height, diameter at breast height (DBH), crown base height, crown area) from ALS point clouds using R (lidR, rLiDAR, TreeLS, etc.), achieved 78% tree detection rate in North Rhine-Westphalia using AMS3D algorithm
    • Evaluated statistical models for tree attributes using R, attained 0.86 R2 of tree height estimates, and 0.74 R2 of DBH estimates
  • 2021.01 - 2023.08

    Eberswalde, Germany

    GIS technician
    Centre for Econics and Ecosystem Management, Eberswalde University for Sustainable Development
    Land-use land-cover classification, crop type mapping, LiDAR analysis (https://www.transect.de/), (https://virtualforests.eu/)
    • Implemented Random Forest algorithm using Python with Landsat 8, Sentinel 1, and Sentinel 2 data in Google Earth Engine. Achieved 91% overall accuracy of crop type classification in central Asia
    • Collected and digitized on-screen training and validation data for landcover classification using QGIS
    • Interpreted multi-temporal remote sensing metrics (e.g., NDVI, NRPB, VV, VH)
    • Produced maps, figures, and tables using R and QGIS
    • Analyzed statistics with R (dplyr, ggplot2, etc.)

Education

  • 2020.10 - 2023.06

    Eberswalde, Germany

    M.Sc
    Eberswalde University for Sustainable Development
    Forest Information Technology
    • GPA: 1.0 (German grade)
  • 2015.10 - 2019.06

    Hanoi, Vietnam

    B.Sc
    Vietnam National University
    Soil Science

Certificates

Structure-from-Motion photogrammetry
The University Centre in Svalbard 2024
Elements of AI
University of Helsinki 2023
Geo-Python
University of Helsinki 2023
Automating GIS Processes
University of Helsinki 2023
Introduction to R
DataCamp 2021
Python Data Structures
University of Michigan 2021
Using Python to Access Web Data
University of Michigan 2021
Getting started with Python
University of Michigan 2021
Sustainable Forest Management and Bio-Economy
University of Valladolid 2020

Skills

GIS
QGIS
ArcGIS
Google Earth Engine
Programming
Python
R
WebGIS
HTML
CSS
JavaScript (Leaflet)
Database
SQL
PostgreSQL
PostGIS
Digital
Microsoft Office
Data Management
Git
GitHub
License
EU Drone License A1-A3

Languages

German
A2
English
C2
Vietnamese
Native