Summer School: AI for Earth Observation and Scalable Data Management

By DaFab Project

Overview

This intensive summer school offers a unique opportunity to delve into the cutting-edge intersection of Artificial Intelligence (AI) and Earth Observation (EO), combined with the essential skills for managing large-scale data and workflows in modern computing environments. Participants will gain theoretical knowledge and practical experience in applying AI techniques to analyze EO data, optimizing AI performance, managing complex workflows with Kubernetes, and handling massive datasets. The program is designed for researchers, professionals, and students eager to explore the intersection of AI and environmental science.

DaFab is a European project sponsored by ESA with a focus on applying AI technique to Earth Observation data. As we observe the limited offer in terms of interdisciplinary event, we decide to set-up this summer school!

The goal is to kick off with a deep dive into AI applications in Earth Observation. Participants will learn how AI technologies are revolutionizing the way we monitor and understand our planet.

During 4 days, each day will be split in the morning dedicated to technical lesson and an afternoon for handson session.

Course Topics

  1. AI for Earth Observation: Explore the application of various AI techniques, including machine learning and deep learning, to process and analyze EO data from Copernicus Sentinel-2 sources.
    Earth
    • Data format and STAC metadata format
    • Image processing and analysis with AI
    • Object detection and classification in EO imagery
    • Change detection and time-series analysis
    • Applications in smart agriculture, disaster management and urban planning.
  2. AI Performance Tuning: Learn techniques for optimizing the performance of AI models, crucial for handling the large volumes of data in EO applications.
    • Model selection and architecture optimization
    • Distributed training and inference
    • Hardware acceleration (GPUs, CPUs)
    • Performance monitoring and profiling
  3. Workflow Management with Kubernetes Containers: Master the use of Kubernetes for managing and scaling complex data processing and AI workflows.
    Connected cubes
    Topics include:
    • Containerization with Docker
    • Kubernetes architecture and components
    • Deploying and managing applications on Kubernetes
    • Scaling and resource management
    • Orchestrating data pipelines with Kubernetes
  4. Data Management at Scale: Acquire the skills to manage and process massive datasets, a critical aspect of both AI and EO applications.
    • Distributed file systems and object storage
    • Data warehousing and data lakes
    • Data pipelines and ETL processes
    • Data governance and security

Target Audience

This summer school is designed for graduate students, researchers, and professionals in fields such as:

  • Remote sensing
  • Geospatial science
  • Computer science
  • Environmental science
  • Data science

Prerequisites

A basic understanding of programming (Python preferred), machine learning concepts, and Linux command-line is recommended.

Learning Outcomes

Upon completion of this summer school, participants will be able to:

  • Apply AI techniques to analyze EO data for various applications.
  • Optimize the performance of AI models for large-scale datasets.
  • Design and deploy scalable data processing workflows using Kubernetes.
  • Manage and process massive datasets efficiently.
  • Collaborate effectively in interdisciplinary teams.

Conclusion

DaFab Summer School offers a unique opportunity to gain comprehensive knowledge and practical skills in applying AI to Earth Observation, optimizing AI performance, managing workflows with Kubernetes, and handling data at scale. Join us for an enriching experience that will empower you to leverage AI technologies for environmental and data-driven challenges.

We look forward to welcoming you to this exciting and informative program!