Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data

How to estimate the potential electricity generation of solar panels any building of a country? In this blogpost, we present nam.R’s methodology to solve this problem.

Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data

How could we automatically estimate the amount of electricity that solar panels could generate on any building of a country, instantly? It would require to be able to massively compute the roofs topology and their weather condition as well as other key components such as:

Although this information may be available at the scale of a city, it rarely exists at the scale of a country.

At nam.R, we used machine learning models trained on various data to predict the missing information and make this large scale estimation possible. We process open data (1) to train machine learning models (2) to recognize rooftops on aerial images (3) and predict their inclination (4), which enable us to estimate the maximum number of installable modules and their power generation (5). 

Illustration of the five steps constituting our methodology

Illustration of the five steps constituting our methodology.


This article presents our methodology applied on the French territory. We also submitted these results to the Applied Machine Learning Days conference #AMLDays2020 at EPFL (Lausanne, Switzerland).


Why is Predicting the Solar Potential of Rooftops Important?

Before we go straight into the solution, we need to step back and look at the context. In our future world of 10 billion people, meeting the energy demand while limiting global warming is a major challenge. Yet, fossil fuels are still our main source of electricity and heat generation, accounting for 42% of the greenhouse gas emissions worldwide in 2016. To lower these numbers, energy efficiency and development of renewable energy are presented as the two main approaches.

Solar panels are one of the fastest-growing renewable energies, despite the presence of non-recyclable materials and a low average efficiency of 15% compared to 50% to 90% for hydropower or wind power. But their operation and maintenance costs are low, and modules can be installed as well on the ground as on top of a roof: useful for cities, where space is limited resource. Estimating the production of solar panels on a large scale is therefore a key challenge to accelerate this transition.


Combining open data and AI

At nam.R, we are building a digital representation of the world to perform ecological, economical and digital transformations. As a data producer, we aggregate, clean and re-organize data from various sources that can help to solve real world problems at a large scale. To solve this problem, we decided to use computer vision to identify the roofs and structured data to predict or compute their characteristics.


Discover the full article here.

More articles

  • Estimation of vegetated surfaces with Computer Vision: how we improved and scaled up our model

    How we improved our estimation of vegetated surfaces with Computer Vision Here at nam.R, we’re putting our hard work into building a Digital Twin of the French territory. We aggregate, clean and re-organize a large quantity of data of different […]

  • Partnering for Progress: How Real-time Social Data Advances the SDGs

    SDGs: Sustainable Development Goals On September 23rd, during the the International UN Summit on Climate taking place in New York, UN Global Pulse, Dataminr and Twitter planed an evening of discussions and presentations of concretes examples of technologies and innovations […]

  • nam.R at European AI for Finance

    “Time to be concrete”  nam.R was at Ai for finance, a unique opportunity of networking at the european scale for all the actors of the AI in finance, the fintech meeting of the reopening season had as topic: “Time to […]

  • nam.R was at Impact AI

    nam.R at Impact AI On July 8th, nam.R was at Impact AI for their first annual conference. Laurence Lafont – as President of Impact AI, announced that the first white paper of Impact AI : “A collective commitment for a […]

  • A week at Data Science Summer School 

    A week at DS3 After the success of the 2 firsts editions of  Data Science Summer School (DS3), the Ecole polytechnique welcomed from June 24th to June 28th, the third event DS3 on its campus in Palaiseau. This event, made […]

  • Ecological transition : the central theme of the 225th anniversary of the Ecole polytechnique

    Ecole polytechnique decided to celebrate this 225th anniversary by committing to a strategy of sustainable development For its 225th anniversary, Ecole polytechnique organised on June 7, 2019, the international scientific symposium : «RefleXions: researching, educating and acting for sustainable development» […]

  • nam.R was at GeoData Days 2019

    Our Data Strategist team was at GeoData Days 2019 on the behalf of nam.R nam.R was present at the GeoData Days 2019 from july 2nd to july 3rd! Our Data Strategist, Nicolas Berthelot, Alexis Camberlyn and Charles Hutin-Persillon were able […]

  • What you should know about GeodataDays 2018

    The first professional meeting of the Geodata field took place at le Havre on July 3 and 4, 2018: Geodatadays 2018. After they launched each on their side « Rencontres Dynamiques Régionales en information géographique » and « DécryptaGéo» , Afigéo and DécryptaGé […]

  • Know your territory well to win elections

    The techniques might change but the objective of election campaigns is always the same: to understand each block in the constituency so that they can be effectively targeted with customized messages and campaign actions. Those days are gone when voters’ […]

  • AI and art: How art and museums are reinventing themselves with data

    In the last few years, AI art has proliferated. Some AI algorithms can recreate an image in a painter’s style, some can improvise a track alongside a human musician on stage and some can even write a story presenting the […]

  • Big data and insurance industry: towards a tailor-made service?

    Insurance sector suffers from lack of image. This market has a captive clientele (since they are legally obliged to continue paying after having purchased an insurance) that in return feels that they have to pay too much and that too […]

  • Artificial intelligence transforms travel experience

    In terms of data, tourism industry is complex to analyze. It has just 25% of structured data that comes from varied sources like business websites of tour operators, e-commerce by travel agencies, hosting companies, restaurants, transporters and CRM & other […]