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! 