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Streamline the Identification of Suitable Sites for Solar Panel Installations in UK

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This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at

The problem

Globally, the transition to renewable energy sources, particularly solar power, is a critical step towards sustainable development and environmental conservation. However, a significant challenge in this transition is the identification of suitable sites for solar panel installations. Traditional methods, involving manual assessments and physical site visits, are not only time-consuming and labor-intensive but also prone to inaccuracies and inconsistencies. This inefficiency in site selection hampers the scalability and speed of solar energy deployment, creating a bottleneck in the widespread adoption of this clean energy source.

The current approach to identifying potential rooftops for solar installations is fraught with challenges. It often overlooks recent structural changes, environmental factors, and new constructions, leading to suboptimal site selection. This manual and subjective process results in delays in project implementation, increased costs, and limited reach of solar solutions. Consequently, the slow pace of identifying and approving sites for solar installations is a significant barrier in the global effort to reduce carbon emissions and combat climate change.

Recognizing these challenges, this project aims to leverage the capabilities of Artificial Intelligence (AI) and advanced image recognition algorithms to revolutionize the process of solar site identification. By analyzing recent satellite imagery, the AI solution will pinpoint the most suitable rooftops for solar panel installations with greater accuracy and efficiency. This innovative approach will automate and expedite the site selection process, reducing reliance on manual assessments and subjective judgments.

The successful implementation of this AI-driven solution will have far-reaching impacts. It will enable faster and more efficient deployment of solar installations, significantly reducing the time and resources required for site assessments. By providing more accurate and objective evaluations, the solution will enhance the overall efficiency of solar energy systems. Furthermore, this project will facilitate the scaling up of solar energy adoption, contributing to global environmental goals and the transition to sustainable energy sources.

In summary, this initiative addresses the critical need for a more streamlined, accurate, and scalable method of identifying suitable sites for solar installations. It stands to accelerate the adoption of solar energy, support global sustainability efforts, and play a pivotal role in the fight against climate change.

The project goals

The main goal of this project is to develop a comprehensive database and analytical tool that identifies and assesses suitable rooftops for solar panel installations in the UK. This involves gathering detailed site information, utilizing advanced analytical tools like Google Solar API, and creating a user-friendly dashboard to showcase the data for a specific Area of Interest (AoI). The project aims to facilitate the expansion of solar energy capacity by efficiently pinpointing optimal locations for solar panel deployment.

Project Scope:

  • Compile a Comprehensive Database: Gather detailed information on UK sites, including current solar installations, rooftop dimensions, building types, roof materials, height, and estimated solar energy output.
  • Identify Suitable Rooftops for Solar Panels: Determine rooftops’ suitability for solar panel installation, focusing on size, structure, and potential capacity.
  • Implement Advanced Analytical Tools: Utilize tools like Google Solar API for efficient rooftop analysis and data processing.
  • Develop a Scalable Information Database: Create a well-structured database for storing and managing the collected site data.
  • Define and Focus on a Specific Area of Interest (AoI): Collaborate with partners to select a targeted AoI for the project’s initial phase.
  • Create an MVP Dashboard: Develop a user-friendly dashboard to showcase the project’s capabilities and facilitate data pipeline testing for the AoI.


**More details will be shared with the designated team.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

Find more information on how an Omdena Top Talent Program works

First Omdena Project?

Join the Omdena community to make a real-world impact and develop your career

Build a global network and get mentoring support

Earn money through paid gigs and access many more opportunities

Eligibility to join an Omdena Top Talent project

Finished at least one AI Innovation Challenge

Received a recommendation from the Omdena Core Team Member/ Project Owner (PO) is a plus

Skill requirements

Good English

Machine Learning Engineer

Experience working with Machine Learning, Satellite Imagery and/or Remote Sensing is a plus.

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