CV
Basics
| Name | Viet Nguyen |
| Label | GIS and Remote Sensing Analyst |
| duc.nguyen@uni-greifswald.de | |
| Phone | +49 3834 420 4544 |
| Url | https://vietducng.github.io/ |
| Summary | A GIS and Remote Sensing analyst with 4+ years of experience working with geospatial analysis, remote sensing, and WebGIS. Skills on 🤖 Python, R, QGIS, Metashape, PostgreSQL, HTML, CSS, JavaScript, Git, Machine learning. |
Work
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2023.11 - present Greifswald, Germany
GIS and Remote Sensing analyst
Institute of Geography and Geology, University of Greifswald
- Analyzed spatial data with Python (GeoPandas, Xarray,NumPy, GDAL, etc.), and R (sf, terra, lidR, etc.)
- Performed drone-based data acquisition (spectral, RGB, and LiDAR data)
- Processed drone-based data with Agisoft Metashape, DJI Terra
- Developed WebGIS using HTML, CSS, JavaScript (Leaflet)
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2022.03 - 2022.04 Lübeck, Germany
Student Assistant in Terrestrial laser scanning
Naturwald Akademie
https://www.pyrophob.de/
- Planned and performed TLS campaigns in 45 forest plots in Brandenburg
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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 4584 km2 of heterogeneous forest in North Rhine-Westphalia using AMS3D algorithm
- Evaluated statistically models for tree attributes using R, attained 0.86 R2 of tree height estimates, and 0.74 R2 of DBH estimates
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2021.01 - 2023.08 Eberswalde, Germany
GIS and Remote Sensing technician
Centre for Econics and Ecosystem Management, Eberswalde University for Sustainable Development
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. Achieved 91% overall accuracy of crop type classification in central Asia
- Collected and digitized on-screen ground truth data for landcover classification using QGIS, Google Earth
- 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
Certificates
| Introduction to Hyperspectral Remote Sensing | ||
| EO College | 2024 |
| EnMAP data access and image preprocessing techniques | ||
| EO College | 2024 |
| Structure-from-Motion photogrammetry | ||
| The University Centre in Svalbard | 2024 |
| An Introduction to Web GIS | ||
| Louisiana State University | 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 |
| Programming | |
| Python | |
| R |
| WebGIS | |
| HTML | |
| CSS | |
| JavaScript (Leaflet) |
| Photogrammetry | |
| Agisoft Metashape |
| Remote sensing | |
| FORCE |
| Digital | |
| Microsoft Office | |
| Git | |
| GitHub | |
| Docker | |
| Linux |
Languages
| German | |
| B1 |
| English | |
| C2 |
| Vietnamese | |
| Native |