01/09/2026 updated
100 % available
Remote Sensing Engineer & Geomatics Specialist
AIX-EN-PROVENCE, France Valedictorian in Remote Sensing Engineering, currently pursuing an M.Sc. in Geomatics TIGER Scholar
About me
Remote Sensing/GIS Engineer (Valedictorian). Expert Spatial AI, Deep Learning (LSTM) & Predictive Modeling. Proficient in ArcGIS, QGIS, Python, GEE. Ready for High-Level Geospatial Engineering, Network & Risk Missions. Status: Independent Consultant via ITG Portage Salarial.
C (Programming Language)HTMLJavaScript (Programming Language)Computer-Aided DesignAdobe PhotoshopAdobe Photoshop LightroomData AnalysisArcGIS (Software)Artificial Neural NetworksCascading Style Sheets (CSS)Computer ProgrammingGeodesyGeomaticsGeospatial AnalysisGoogle Earth
Remote Sensing Technologies
Expertise in geodesy, space techniques, remote sensing technologies, and GIS with practical field experience
Programming and Data Processing
Proficiency in C, Matlab, Python, Html, Css, JavaScript for technical applications and data analysis
Geospatial Analysis and Modeling
Advanced skills in image processing using Google Earth Engine, ENVI, TerrSet, ArcGIS, QGIS for spatial modeling
GNSS Equipment Operation
Handling of GNSS equipment including Trimble R10, net R9, and processing data for accurate positioning
Design Software Proficiency
Experience with Canva, Adobe Photoshop, Adobe Lightroom for visual design and image editing
Office Automation
Microsoft Office Pack proficiency for documentation and presentation tasks
Machine Learning Applications
Deep learning neural network implementation with LSTM and attention mechanisms for predictive modeling
Simulation and Modeling
Cellular automaton coupled with Monte Carlo simulations for spatio-temporal modeling
Expertise in geodesy, space techniques, remote sensing technologies, and GIS with practical field experience
Programming and Data Processing
Proficiency in C, Matlab, Python, Html, Css, JavaScript for technical applications and data analysis
Geospatial Analysis and Modeling
Advanced skills in image processing using Google Earth Engine, ENVI, TerrSet, ArcGIS, QGIS for spatial modeling
GNSS Equipment Operation
Handling of GNSS equipment including Trimble R10, net R9, and processing data for accurate positioning
Design Software Proficiency
Experience with Canva, Adobe Photoshop, Adobe Lightroom for visual design and image editing
Office Automation
Microsoft Office Pack proficiency for documentation and presentation tasks
Machine Learning Applications
Deep learning neural network implementation with LSTM and attention mechanisms for predictive modeling
Simulation and Modeling
Cellular automaton coupled with Monte Carlo simulations for spatio-temporal modeling
Languages
EnglishFluentFrenchNative speaker
Project history
Explored various real-world themes and challenges in space applications. Actively participated in training sessions and discussions with experts, enhancing understanding of technical and operational issues in remote sensing and space technologies.
During my remote sensing internship, I contributed to the analysis and processing of satellite imagery using various geomatics tools. I primarily used Google Earth Engine for the collection and processing of large spatial datasets, as well as for the computation of vegetation indices and land use/land cover change monitoring. I also used ArcGIS for mapping and result visualization, and ENVI for radiometric and spectral image processing. This internship enabled me to strengthen my skills in geoanalysis, geospatial programming, and satellite data interpretation.
Certificates
State Engineer Diploma
The Higher School of space Techniques2025
Portfolio
GPS – Practical Internship
Practical internship involving the setup, calibration, and operation of a high-precision Global Navigation Satellite System (GNSS) receiver (like a Trimble unit) mounted on a tripod for geodetic surveying and data collection.
Exposure Map
Creation of a map illustrating the solar radiation or sun exposure across Algeria using GIS tools. The color ramp (brown/yellow to light yellow) indicates the intensity of solar exposure, ranging from low (0) to high (359.276).
Drilling Trajectory Calculation with Python
Visualization and comparison of different drilling trajectory calculation methods (e.g., Tangential, Balanced Tangential, Average Angle, Radius of Curvature, Minimum Curvature) in 3D space using Python, typically for directional drilling in petroleum or geophysics applications.
NDVI Map
Generation of an NDVI map (Normalized Difference Vegetation Index) for Algeria and surrounding North African regions. The map utilizes a color ramp to indicate vegetation density, with darker green representing higher vegetation and lighter shades/tan representing bare soil or sparse vegetation.
GPS Processing – RTK
Visualization of survey points acquired using Real-Time Kinematic (RTK) GNSS surveying displayed over a Google Earth satellite image. The image shows measured boundary points (R#) and possibly the radiating lines indicating the measurement vectors or the central control point.
Burned Area Vectorization
Digitizing or vectorizing the perimeters of areas affected by a natural disaster, likely wildfires (burned areas), from satellite or aerial imagery. The final output is a set of distinct polygon features representing the extent of damage.
Drilling Trajectory Calculation with Python
Focused 3D visualization of a smooth drilling trajectory, likely emphasizing the Minimum Curvature method (the black line being the most continuous and accepted standard) in comparison to other methods. This shows the final, most accurate calculated path for a wellbore.
Forest Suitability Map of the Morvan Natural Park
This map illustrates forest suitability across the Morvan Regional Natural Park based on a weighted suitability index. Values range from 1 (very low suitability) to 5 (very high suitability), highlighting areas with more favorable topographic conditions for forest development.
Heat Map of Polygon Density
This heat map shows the spatial density of polygons representing buildings, land use, services, road networks, and railway infrastructure within the Grenoble-Alpes Métropole area. Higher density values highlight zones with greater concentrations of human activities and infrastructure.
Area of Natural Zones under Urban Pressure
This bar chart shows the surface area of natural zones affected by urban pressure for each municipality within Grenoble Alpes Métropole. It highlights significant differences between communes, identifying those where natural areas are most impacted by urban expansion.