WORKSHOPS

The educational workshops provide a short training on the basic stages of using GIS and modern digital technologies.

The workshops will take place in the facilities of the Unit.

Participants will be awarded certificates of attendance.

The educational workshops will be conducted in the Greek language.

Geospatial Artificial Intelligence (GeoAI) in GIS Software Applications in Agriculture & the Environment

What it is: The combination of GIS + Machine Learning / Deep Learning for the analysis of large-scale spatial data.

What it includes:

  • Introduction to GeoAI and core ML concepts

  • Spatial classification & regression

  • Change detection (land use / land cover)

  • Crop yield prediction

  • Environmental risks (drought, floods, erosion)

Applications: Crop yield forecasting, plant disease detection from imagery, and wildfire risk prediction models.

Objective: To automate information extraction from Big GeoData.

What it is: The R programming language is exceptionally powerful for statistical analysis and spatial data management without the use of a Graphical User Interface (GUI).

What it includes:

  • Packages: Learning the sf package (for vector data), terra or raster (for rasters), and ggplot2 (for mapping).

  • Data Wrangling: Cleaning, joining, and transforming spatial data.

  • Automation: Creating scripts for bulk file processing (e.g., processing 1,000 climate files simultaneously).

Applications: Environmental analysis, agricultural experiments, spatial productivity models.

Objective: The ability to perform reproducible research and manage complex datasets.

What it includes:

  • Spatial Modeling: Overlay, buffers, suitability analysis.

  • Exploratory Spatial Data Analysis (ESDA): Testing for spatial autocorrelation (Moran’s I indices), identifying spatial outliers, and analyzing dispersion patterns.

  • Management Zones: Clustering areas of a field with similar characteristics for Variable Rate Application (VRA) of inputs.

Applications: Variable rate inputs (fertilization, irrigation), agro-environmental planning, spatial data understanding prior to modeling.

Objective: Converting data into actionable instructions for the grower, resulting in the optimization of inputs (fertilizers, water) based on field variability.

What it includes:

  • Fundamentals of WebGIS

  • Dashboards: Creating control panels for real-time monitoring of environmental indicators.

  • StoryMaps: Narrative maps combining text, multimedia, and interactive maps.

Applications: Decision support, agricultural platforms, public environmental information.

Objective: Creating tools that make data available even on mobile devices, ensuring continuous monitoring and increasing information accessibility for the target audience.

What it includes:

  • Multi-Criteria Decision Analysis (MCDA): Using methods such as AHP (Analytic Hierarchy Process) for criteria prioritization.

  • Criteria: Combining soil maps, climate data, topography (slope, aspect), and water availability.

  • Suitability Models: Producing maps showing the degree of suitability (S1, S2, N, etc.) for specific crops.

Applications: Suitability maps, crop scenarios, investment decision support.

Objective: Strategic crop planning to maximize yield and ensure soil protection.

What it includes:

  • Flight Planning: Image overlap, flight altitude, and GSD (Ground Sampling Distance).

  • Structure from Motion (SfM): The process of generating 3D point clouds from 2D images.

  • Outputs: Orthomosaics, Digital Surface Models (DSM/DTM), and vegetation index maps (e.g., NDVI) using multispectral cameras.

Applications: Crop monitoring, damage mapping, precision farming.

Objective: Producing ultra-high resolution data (<5cm) for immediate monitoring of crops and environmental conditions.

What it includes:

  • Cloud Computing: Processing petabytes of satellite data (Sentinel, Landsat, MODIS) without requiring a powerful local computer.

  • Time-series Analysis: Analyzing crop phenology and environmental conditions over time.

  • Coding: Using JavaScript or Python API to create algorithms for monitoring drought or soil moisture.

Applications: Time-series crop analysis, NDVI / EVI trends, drought detection, yield monitoring.

Objective: Macroscopic and long-term monitoring of agricultural plots and environmental changes.

Conference registration is a mandatory requirement for participation in any workshop.

Conference registration includes free access to one (1) workshop of your choice.

The cost for every extra workshop is 20€.

Workshop Form

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