Workshop On: Geoinformatics for Dust Risk Management (Online-Live with Supervised Hands-On Practice)
Workshop on: Leveraging Geoinformatics for Strategic Dust Disaster Risk Reduction Planning
Online Live Event, with open discussions and hands-on practice on case studies, May 27-28, 2025, Organized by World-Academies Startup of the Technical University of Dresden, Germany
Overview on Content: Within this workshop you have opportunity to learn about techniques, models, data analysis, and advanced AI-GIS integrated tools, with hands-on practice on practical case studies for Strategic Dust Disaster Risk Reduction Planning and Management, covering:
- Introduction to Geoinformatics, Fundamentals of Remote Sensing Physics, Theoretical Background on Land Degradation & Dust Formation
- Case Studies: Examples linking sensor physics to dust source susceptibility.
- Explore remote sensing datasets to identify spectral bands and sensor characteristics.
- Activity: Use GIS software to visualize how sensor physics translates to environmental data
- Remote Sensing for Hazard Profiling & Dust Analysis
- Vulnerability, Risk Mapping & Advanced Data Analysis
- Strategic & Contingency Planning Integrating AI and Geoinformatics
- Apply disaster risk reduction (DRR) models using integrated Geoinformatics and AI-driven insights
Workshop Agenda
Day 1
Morning Session (3 hours): Session 1 – Principles of Geoinformatics & Remote Sensing Physics
Objectives:
- Introduce key geoinformatics concepts.
- Review the physics behind remote sensing including electromagnetic spectrum fundamentals, sensor properties, and data calibration.
Agenda:
- Lecture (1.5 – 2 hours):
- Introduction to Geoinformatics: Overview of geospatial data, tools, and their role in environmental studies.
- Fundamentals of Remote Sensing Physics:
- Electromagnetic spectrum and sensor physics.
- Radiometric principles, resolution, calibration, and atmospheric corrections.
- Theoretical Background on Land Degradation & Dust Formation: Understanding the processes driving dust hazards.
- Case Studies: Examples linking sensor physics to dust source susceptibility.
- Hands-on Session (30 minutes – 1 hour):
- Exercise: Explore remote sensing datasets to identify spectral bands and sensor characteristics.
- Activity: Use GIS software to visualize how sensor physics translates to environmental data interpretation.
Afternoon Session (3 hours): Session 2 – Remote Sensing for Hazard Profiling & Dust Analysis
Objectives:
- Apply remote sensing techniques for environmental hazard profiling.
- Delve deeper into how physics informs the detection and analysis of dust storms.
Agenda:
- Lecture (1.5 – 2 hours):
- Real-world Applications: Analysis of dust storm events with a focus on the Middle East.
- Physics in Data Acquisition:
- Understanding spectral signatures of dust and radiometric correction techniques.
- How sensor characteristics impact data quality.
- Hazard Impact Analysis: Methods for profiling dust hazards using remote sensing data.
- Hands-on Session (30 minutes – 1 hour):
- Exercise: Analyze remote sensing imagery to detect dust storm events.
- Activity: Practice radiometric corrections and interpret spectral data for dust hazard profiling.
Day 2
Morning Session (3 hours): Session 3 – Vulnerability, Risk Mapping & Advanced Data Analysis
Objectives:
- Understand methods to assess vulnerability and map risks associated with dust hazards.
- Introduce advanced data analysis techniques and AI applications in geospatial analysis.
Agenda:
- Lecture (2 hours):
- Vulnerability & Risk Mapping Techniques: Methods for environmental and socio-economic risk assessment.
- Data Analysis Tools:
- Statistical methods and geospatial data analysis.
- Introduction to AI and machine learning applications in remote sensing.
- AI in Risk Prediction: How AI models can enhance dust hazard risk mapping.
- Hands-on Session (30 minutes – 1 hour):
- Exercise: Develop a risk map using provided datasets, applying statistical analysis.
- Activity: Experiment with a basic AI model for spatial risk prediction in a GIS environment.
Afternoon Session (3 hours): Session 4 – Strategic & Contingency Planning Integrating AI and Geoinformatics
Objectives:
- Apply disaster risk reduction (DRR) models using integrated geoinformatics and AI-driven insights.
- Develop strategic contingency plans informed by comprehensive data analysis.
Agenda:
- Lecture ( 1:30 hours):
- DRR Models for Dust Hazards: Overview of strategic planning frameworks.
- Integrating Data & AI: Best practices for merging geospatial datasets with AI insights.
- Contingency Planning: Scenario modeling and prioritization of high-risk areas using AI.
- Hands-on Session (45 min):
- Exercise: Work in groups to design a contingency plan using simulation tools and integrated data analysis.
- Activity: Draft intervention strategies and discuss the role of AI in real-time disaster response.
Wrap-Up and Feedback (45 min)
- Closing Discussion: Recap key insights from both days.
- Q&A Session: Open forum for clarifications and discussion of potential applications in participants’ work.
Registration Form
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