DeployAI Earth Observation Services: Enabling Environmental Insights on the European AI-on-Demand Platform

The DeployAI project [1] designs and delivers a fully operational European AI-on-Demand Platform (AIoDP) to empower the European industry with access to cutting-edge AI technology, and to promote trustworthy, ethical, and transparent European AI solutions, with a focus on SMEs and the public sector. To achieve this, the platform enables the development and deployment of AI solutions through the following core solutions: (i) AI Builder [2], which allows the assembling of reusable AI modules into AI pipelines; (ii) seamless access to Cloud and HPC infrastructures (e.g., MeluXina and LUMI); (iii) a marketplace for the listing and distribution of ready-to-use AI products; (iv) an expansive and growing library of diverse AI-driven use cases.

As part of its domain-driven solutions, AIoDP seeks to empower Environmental Scientists, AI Engineers, Developers, Researchers, and SMEs via the DeployAI Earth Observation Services. These services will accelerate the development of AI-driven environmental applications, by providing pre-trained models that simplify satellite imagery processing, land usage classification, and image segmentation. Key models available as modules within the DeployAI’s AI Builder include:

• Leaf Area Index (LAI) Model: Enables precise monitoring of vegetation health and ecological dynamics by calculating leaf area per unit ground [3]. 
• Object Detection Model: Identifies specific objects in high-resolution satellite images, supporting applications such as  infrastructure monitoring, pollution tracking, and deforestation assessment [4].
• Segment Anything Model (SAM): Simplifies analysis across diverse environmental applications through the capabilities of SAM that allows flexible, prompt-based image segmentation for new datasets, with zero-shot and few-shot learning [5].

These models, along with the broader functionalities of AI Builder, enable users to create custom AI pipelines that address their specific environmental challenges in several environmental areas, including vegetation health monitoring, water balance analysis, climate modeling, urban planning, traffic management, pollution monitoring, and infrastructure maintenance. Users can leverage the visual pipeline editor to easily assemble pipelines from reusable AI modules without needing to write code. Once created, these pipelines can be deployed as AI applications on various execution environments. DeployAI facilitates seamless transitions between these environments by providing connectors to a host of target infrastructures, including Cloud platforms and HPC systems. This empowers users to leverage the most suitable computational resources for their specific needs.

By providing a user-friendly platform with access to cutting-edge AI technology and Cloud/HPC resources, DeployAI empowers users to address critical environmental challenges and unlock new possibilities for sustainable development.

[1] https://deployaiproject.eu
[2] https://gitlab.eclipse.org/eclipse/graphene
[3] https://github.com/DeployAI-Environmental-Services/depai-lai
[4] https://github.com/DeployAI-Environmental-Services/depai-yolov8-obb
[5] https://github.com/DeployAI-Environmental-Services/depai-sam-interactive

This work has received funding from the European Union’s Digital Europe Programme (DIGITAL) under grant agreement No 101146490.