Announcing the Launch of the AI/ML Enhancement Project for GEP and Urban TEP Exploitation Platforms

Terradue AI Extensions
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We are excited to announce the launch of a new project aimed at augmenting the capabilities of two Ellip-powered Exploitation platforms, the Geohazards Exploitation Platform (GEP) and the Urban Thematic Exploitation Platform (U-TEP). The project’s primary objective is to seamlessly integrate an AI/ML processing framework into both platforms to enhance their services and empower service providers to develop and deploy AI/ML models for improved geohazards and urban management applications.

Project Overview
The project will focus on integrating a comprehensive AI/ML processing framework that covers the entire machine learning pipeline, including data discovery, training data, model development, deployment, hosting, monitoring, and visualization. A critical aspect of this project will be the integration of MLOps processes into both GEP and Urban TEP platforms’ service offerings, ensuring the smooth operation of AI-driven applications on the platforms.

GEP and Urban TEP Platforms
GEP is designed to support the exploitation of satellite Earth Observations for geohazards, focusing on mapping hazard-prone land surfaces and monitoring terrain deformation. It offers over 25 services for monitoring terrain motion and critical infrastructures, with more than 2500 registered users actively participating in content creation.

Urban TEP aims to provide end-to-end and ready-to-use solutions for a broad spectrum of users to extract unique information and indicators required for urban management and sustainability. It focuses on bridging the gap between the mass data streams and archives of various satellite missions and the information needs of users involved in urban and environmental science, planning, and policy.

Project Partners
The project brings together a strong partnership of experienced organizations, including Terradue, CRIM, Solenix, and Gisat. These partners have a proven track record in various aspects of Thematic Exploitation Platforms, cloud research platforms, AI/ML applications, and EO data analytics.

Expected Outcomes
Upon successful completion, the project will result in the enhancement of both GEP and Urban TEP platforms and their service offerings. The addition of AI/ML capabilities will empower service providers to develop and deploy AI/ML models, ultimately improving their services and delivering added value to their customers. This enhancement will greatly benefit the GEP and Urban TEP platforms by expanding their capabilities and enabling new AI-driven applications for geohazards and urban management.

Discussion Points:

  1. How do you foresee AI/ML capabilities enhancing the services provided by GEP and Urban TEP?
  2. What challenges do you anticipate in integrating AI/ML processing frameworks into existing platforms?
  3. Which use cases do you believe would benefit the most from the addition of AI/ML capabilities in GEP and Urban TEP?

We encourage you to share your thoughts, ideas, and experiences related to the project. Let’s discuss the potential impact and improvements this project can bring to the GEP and Urban TEP platforms and their user communities.

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AI/ML Enhancement Project - Progress Update

Background

One year has passed since the announcement of the AI/ML Enhancement Project launch (see post). This project innovatively integrates cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) technologies into Earth Observation (EO) platforms like Geohazards Exploitation Platform (GEP) and Urban Thematic Exploitation Platform (U-TEP) through MLOps - the fusion of ML with DevOps principles.

Leveraging these platforms’ extensive EO data usage, the new AI extensions promise enhanced efficiency, accuracy, and functionalities. The integration of these new capabilities unlocks advanced data processing, predictive modelling, and automation, strengthening capabilities in urban management and geohazard assessment.

User Personas, User Scenarios and Showcases

For the project implementation we have identified two types of users:

  • A ML Practitioner that we will call “Alice”: expert in building and training ML models, selecting appropriate algorithms, analysing data, and using ML techniques to solve real-world problems.
  • And a Consumer that we will call “Eric”: stakeholder or user (e.g. business owner, a customer, a researcher, etc) who benefits from, or relies upon, the insights or predictions generated by the ML models to inform his decision-making process.

From these users we have derived ten User Scenarios that capture the key activities and goals of these types of users in utilising the service. The user scenarios are:

  • User Scenario 1 - Alice does Exploratory Data Analysis (EDA)
  • User Scenario 2 - Alice labels Earth Observation data
  • User Scenario 3 - Alice describes the labelled Earth Observation data
  • User Scenario 4 - Alice discovers labelled Earth Observation data
  • User Scenario 5 - Alice develops a new Machine Learning model
  • User Scenario 6 - Alice starts a training job on a remote machine
  • User Scenario 7 ​- Alice describes her trained machine learning model
  • User Scenario 8 -​ Alice reuses an existing pre-trained model
  • User Scenario 9 - Alice creates a training dataset
  • User Scenario 10 - Eric discovers a model and consumes it

From these user scenarios, three Showcases were selected to develop and apply AI approaches in different context in order to validate and verify the activities of the AI Extensions service:

  • “Urban greenery” showcase: urban greenery using EO data, specifically focusing on monitoring urban heat patterns and preventing flooding in urban areas.
  • “Informal settlement” showcase: AI approaches in the context of urban management, specifically targeting the challenges posed by informal settlements.
  • “Geohazards - volcanoes” showcase: AI approaches for EO data for monitoring and assessing volcanic hazards.

Project Status

The first release of this project was critical in setting the foundation as it focused on developing a cloud-based environment and related tools that enabled users to work with EO data and data labels. With the successful completion of the second release, the user is now able to build and train ML models with EO data labels effectively.

The project implementation with the User Scenarios focused on developing interactive Jupyter Notebooks that aim at validating and verifying all the key requirements of the activities performed in each Scenario.

To date, Jupyter Notebooks for User Scenarios 1 - 5 have been developed and validated.

Upcoming Work

The project’s future phases are eagerly anticipated. Release 3 will focus on enabling users to train their ML models on remote machines, while Release 4 will empower them to execute these models from the stakeholder/end-user Eric’s perspective. This progression underscores a strategic roadmap towards making GEP and U-TEP powerful platforms for data analysis and interpretation using advanced AI techniques.

Dedicated articles will be published in the coming weeks, describing the activities and main outcomes of each Scenario / Notebook, so stay tuned! :wink:

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