Data analysis – Geographic Information System GIS (Advanced)
Aix-Marseille University
Reference Teacher: Jafar Anbar (AMU)
Teacher(s): Jafar Anbar & Prof. Kalliopi Baika
This course offers a comprehensive skills-set of underwater documentation technologies, focusing on the integration of cutting-edge tools and techniques for geoarchaeological and archaeological studies. This advanced QGIS course equips participants with practical skills for high-level spatial analysis and geoprocessing. It focuses on advanced raster analysis using the Raster Calculator and SAGA tools for terrain modeling and spatial statistics. Participants will learn contour generation techniques, terrain profiling with the Profile Tool, and image classification using the Semi-Automatic Classification Plugin (SCP). The course also emphasizes integration of interdisciplinary datasets (remote sensing, environmental, and field data) to support complex geospatial workflows. Ideal for professionals and researchers seeking to enhance analytical accuracy and decision-making using QGIS.
PhD training in Aix-Marseille University (The PhD training platform ADUM (Accès Doctorat Unique et Mutualisé). Master, PhD students and professionals for up-skilling, re-skilling, and capacity building.
Participants will develop advanced theoretical and practical understanding of raster-based geospatial analysis in QGIS. This includes in-depth knowledge of Raster Calculator operations, SAGA geoprocessing algorithms, contour generation, terrain profiling, and the principles of semi-automatic image classification. Participants will also understand methods for integrating interdisciplinary spatial datasets to support complex geoscientific and environmental analyses.
Participants will apply advanced QGIS tools to solve real-world spatial problems by performing raster calculations, generating contours, executing SAGA-based analyses, and creating terrain profiles. They will use the Semi-Automatic Classification Plugin to classify satellite imagery and integrate diverse datasets, translating theoretical concepts into practical workflows for geoarchaeological applications.
Participants will develop the ability to critically evaluate spatial data quality, analytical methods, and model outputs. They will select appropriate raster operations, classification techniques, and SAGA tools based on data characteristics and project objectives. They will interpret results, assess uncertainties, and make informed, evidence-based judgments when integrating interdisciplinary datasets for decision-making and spatial analysis.
Participants will enhance their ability to communicate geospatial analyses effectively through maps, profiles, classified images, and analytical outputs generated in QGIS. They will present results clearly to both technical and non-technical audiences, explaining methodologies, assumptions, and findings using appropriate visualizations and spatial narratives to support informed discussion and decision-making.
Participants will strengthen independent and lifelong learning skills by exploring advanced QGIS functionalities, plugins, and analytical workflows. They will learn to troubleshoot geoprocessing tasks, adapt to new datasets, and integrate emerging tools and methods. This fosters the capacity to continuously update geospatial skills and apply advanced GIS techniques in professional and research contexts.
Basic knowledge in QGIS software
Personal laptop for the treatment of data:
– at least Intel i5 processor or equivalent, but Intel i7 or equivalent would be much better
– a minimum of 12 GB of RAM, but as much as possible is preferred
– a good graphic adapter, preferably by Nvidia or AMD
– at least 40 GB of disk space
In-person lectures or online.
Evaluation by the completion of the course and the final visualization and sharing of the data. Evaluation sheet from AMU (in English, French, – adapted to other languages as well) sent online.
This course emphasizes hands-on, project-based learning using real-world spatial datasets to deepen analytical proficiency in QGIS. Participants will work through structured exercises and case studies that reflect professional and research-oriented geospatial challenges. Attention is given to data preparation, workflow optimization, accuracy assessment, and interpretation of outputs rather than only tool execution. By the end of the course, learners will be capable of designing complete raster analysis, integrating multiple data sources, and producing reliable spatial results that support environmental assessment and geoscientific decision-making.
The expected learning outcomes can contribute to the achievements of the following goals: Goal 14: Life below water, Goal 13: Climate Action, and Goal 4: Quality Education
For PhD students of Aix-Marseille University, they can simply apply through their personal account on ADUM (https://adum.fr/index.pl). For other PhD, master students and professionals by simply showing their interest to follow this course through contacting the following email address: info@unescochair-mca.org
This module introduces advanced raster data processing techniques in QGIS, focusing on the Raster Calculator for complex spatial modelling. Participants will apply mathematical and logical operations to real-world datasets, enhancing their ability to design accurate and efficient raster-based analytical workflows.
This module focuses on the integration of SAGA GIS algorithms within QGIS for advanced terrain analysis. Participants will apply digital elevation model (DEM) processing techniques, including slope and terrain classification. Hands-on sessions will explore geomorphological modelling, landform and extraction. They will be able to generate reliable topographic maps for archaeological and geoarchaeological applications.
This module covers contour generation techniques and the use of the Profile Tool for detailed terrain and landscape analysis. Participants will learn methods for producing accurate contour lines, elevation profiles, and cross-sectional analyses from raster surfaces. Practical exercises include creating longitudinal and transversal profiles, analyzing terrain gradients, and interpreting morphological patterns. Emphasis is placed on cartographic quality, visualization standards, and spatial interpretation, enabling participants to translate complex elevation data into meaningful analytical outputs.
This module introduces advanced image processing workflows using the Semi-Automatic Classification Plugin for satellite image classification. Participants will perform supervised and unsupervised classification, accuracy assessment, and post-classification refinement. The module also focuses on integrating interdisciplinary datasets, including remote sensing, environmental, and field data.
Several sections from the user manuals of different softwares can be useful during and after the completion of the introductory digital courses.
– QGIS Desktop 3.22 user guide: https://docs.qgis.org/3.22/pdf/en/QGIS-3.22-DesktopUserGuide-en.pdf
Moreover, step by step instructions and practical recommendations will be provided in PDF format to guide the participants.