Highly skilled Remote Sensing Engineer and Geospatial Data Scientist with expertise in satellite imagery processing for marine environments, specializing in water color product development. Proven experience in research and development, in-situ validation, and data analysis, with a strong foundation in Python, machine learning, and advanced geospatial tools. Driven to leverage interdisciplinary approaches in remote sensing, modeling, and automated learning to deepen understanding of marine phenomena and contribute to impactful scientific advancements.
Remote Sensing Engineer (Sargassum)
Toulon, Provence-Alpes-Côte d'Azur, France
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Summary
Led the SargAlert project, developing Python-based processing chains for atmospheric correction and sargassum detection, integrating satellite data and in-situ validation to model sargassum drift.
Highlights
Developed advanced Python processing chains for atmospheric correction of Sentinel-3 OLCI data, enhancing data accuracy for marine applications.
Implemented sargassum detection algorithms using TensorFlow and POLYMR, improving monitoring capabilities for critical marine ecosystems.
Modeled sargassum drift patterns utilizing GOES and MODIS satellite data, providing crucial insights for environmental management and forecasting.
Validated project results through rigorous in-situ measurements, ensuring high accuracy and reliability of satellite-derived data products.
Geospatial Cartographer
Le Havre, Normandy, France
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Summary
Designed and created comprehensive geographic databases for wet network systems, automating geospatial processes and ensuring data quality through statistical reporting.
Highlights
Conceived and established geographic databases for complex wet network systems, optimizing data accessibility and integrity for municipal planning.
Developed FME (Feature Manipulation Engine) scripts to automate repetitive geospatial processes, significantly increasing operational efficiency and reducing manual effort.
Generated detailed statistical reports on data quality, ensuring the reliability and accuracy of geospatial information for critical decision-making.
Remote Sensing and Data Processing Engineer
Villefranche-sur-Mer, Provence-Alpes-Côte d'Azur, France
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Summary
Contributed to the HYPERNETS project, validating satellite-derived water reflectance in French coastal waters and developing Python scripts for satellite data analysis and atmospheric correction algorithms.
Highlights
Validated satellite-derived water-leaving reflectance in contrasted French coastal waters using HYPERNETS field measurements, enhancing accuracy of marine remote sensing data.
Compared and validated atmospheric correction algorithms for SENTINEL2, SENTINEL3, and LANDSAT data, improving the precision of water quality parameter retrieval.
Developed robust Python scripts for analyzing and processing diverse satellite data, streamlining research workflows and enabling efficient data interpretation.
Contributed to scientific publications, effectively valorizing research findings and disseminating knowledge within the remote sensing community.
Adjunct Lecturer
Gabès, Gabès Governorate, Tunisia
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Summary
Delivered comprehensive courses and practical sessions on Geographic Information Systems (GIS) and remote sensing, educating university students on key geospatial technologies.
Highlights
Taught university-level courses and practical workshops on Geographic Information Systems (GIS) and remote sensing principles.
Developed engaging curriculum content and hands-on exercises to enhance student understanding and practical application of geospatial concepts.
Mentored and guided students in mastering complex geospatial software and data analysis techniques.
Master 2 Intern (Remote Sensing)
Villefranche-sur-Mer, Provence-Alpes-Côte d'Azur, France
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Summary
Conducted a Master 2 internship on the DCS4COP project, focusing on remote sensing and mapping spatio-temporal dynamics of suspended particles in Berre ponds by calibrating chlorophyll and turbidity estimation algorithms.
Highlights
Performed remote sensing and mapping of spatio-temporal dynamics of suspended particles in the Berre ponds, contributing to critical environmental monitoring.
Calibrated algorithms for estimating chlorophyll and turbidity using Sentinel 2 and Sentinel 3 OLCI data, improving the accuracy of water quality assessments.
Analyzed large datasets to identify trends and patterns in water quality, providing valuable insights for ecological studies.
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Master's Degree
Geomatics: Water and Environment
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Engineering Diploma
Geo-resources and Environment
Published by
Frontiers in Remote Sensing
Summary
Research validating satellite-derived water-leaving reflectance using HYPERNETS field measurements in French coastal waters.
Published by
Research Publication
Summary
Study evaluating drought effects on soils through innovative monitoring of soil salinity using SAR data, Sentinel-2 imagery, and machine learning in the Kerkennah archipelago.
Published by
Regional Studies in Marine Science
Summary
Research monitoring sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and Google Earth Engine.
Python, FME (Feature Manipulation Engine), Git, SLURM.
Satellite Data Processing, Atmospheric Correction, Change Detection, Classification, Time Series Analysis, Machine Learning, GIS (Geographic Information Systems), Cartography, Geodatabase Design, In-situ Validation, Spatial Analysis.
MODIS, GOES, Sentinel-2, Sentinel-3 OLCI, Landsat, SAR Data, HYPERNETS.
Reflectance, Chlorophyll, Turbidity, Sea Surface Temperature, Salinity.
ArcGIS, QGIS, ENVI, SNAP, Google Earth Engine, OpenLayers, POSTGIS, GeoServer, TensorFlow, POLYMR, Linux.
Scientific Publication, Data Validation, Algorithm Development, Statistical Analysis, Environmental Monitoring.