developing a complex computer vision system, a case study : solar panels equipped roofs

At nam.R we are working hard to build the Digital Twin of France. To this end, we use a lot of sources of information such as aerial images.Extracting useful information from the unstructured data that are images? Sounds like a job for the computer vision team !

To detect all solar panels on the roofs of french buildings, we used aerial imagery and the known outlines of the buildings, through a pipeline consisting of a solar panel outline detector and a filtering algorithm.

We took inspiration from the projects revolving around the state-of-the-art object segmentation deep learning algorithm known as Mask-RCNN. This algorithm is the newborn of a family of algorithms developed by Ross Girshick & al., in direct continuation of RCNN, Fast-RCNN and Faster-RCNN.

The chosen pipeline consists in two complementary parts :
– an object detector, more specifically an instance segmentation algorithm, meant to detect solar panels and extract their contours ;
– a filtering algorithm that takes all detections and filters out those that don’t match our business rules.

While the filtering algorithm can easily be developed using the expert rules we chose to consider (size of the detected solar panels, their position regarding to the considered roof), the deep learning model depends directly of the data we will feed it with.

The first part of the project was, accordingly, to generate a dataset of roofs equipped with solar panels, and the matching labels. There are multiple existing tools for image annotations (VGG VIA, MIT LabelMe,…) that can be used as is. We chose the VGG Image Annotator. After a few (hundreds of) clicks, we ended up with a dataset we’re quite proud of.

Only then, we were able to train the Mask-RCNN model to detect solar panels.
The first version of our model wasn’t performing all that well and if was necessary to add more data to the training stage. We realized semi-supervised learning with automatic labelling.

This technique consists in using the model as a way to compute more labels that are then controlled by human operators and used as training data for a new, more robust version of the model. Controlling whether the proposed labels were right and correcting the wrong ones was way simpler than labelling by hand hundreds of images. Basically, we used our first model as a replacement for crowdsourcing !
After a few loops we fetched more data and matching labels and were able to train a model that had acceptable performances.

We transformed the raw output of the model into polygons in the same format and projection as our building polygons using geometric algorithms (Marching Squares, Douglas-Peucker) and geographic transformations. This enables us to directly filter out the potential false positives. We found out that roof windows, glass roofs and blue awning fabric were likely to be mistaken for solar panels due to their similar visual textures.

The first conclusion we draw is that the combination of machine learning and expert rules can become a reliable framework, harnessing the power of machine learning algorithms and the robustness of business rules.

The second one was the use of our first imperfect model to help us label more data. Real data but synthetic labels, a great example of human-machine cooperation, isn’t it?

Finally, there are many different ways for computing the performances of this kind of pipelines. The deep learning model itself can be evaluated using metrics such as its mean average precision but we were mainly interested in the performances of the whole flow. Thus, we chose metrics that are less image-centric and oriented more towards information retrieval : precision, recall and overall accuracy. We added a geometric metric that indicates how well our predicted panels matched with the actual ones, the Intersection over Union (IoU).

We achieved the performances of 96% overall algorithm accuracy and 84% IoU on our test set, values we’re quite proud of.

The predictions of solar panels were integrated in nam.R’s Digital Twin and the information is already put to good use !

Plus d'articles

  • European AI for Finance

    Le mardi 3 septembre 2019, Startup Inside, l’agence référence de la transformation digitale, rassemblait les experts de l’intelligence artificielle dans l’industrie de la finance européenne, startups, grands groupes, laboratoires de recherche, universitaires et amateurs d’IA lors de son évènement European AI for […]


    LIRE LA SUITE
  • nam.R à Impact AI : rendez-vous fort du développement d’une IA responsable et éthique.

    Laurence Lafont, COO de Microsoft France et Présidente d’Impact AI, a annoncé la sortie du Manifeste « Un engagement collectif pour un usage responsable de l’Intelligence Artificielle » . Le 8 Juillet dernier, nam.R était à Impact AI lors d’une conférence […]


    LIRE LA SUITE
  • Une semaine à la Data Science Summer School 

    Une semaine à la Data Science Summer School  Après le succès des deux premières éditions de la Data Science Summer School (DS3), l’École Polytechnique a accueilli, du 24 au 28 juin, sa 3ème édition sur son campus à Palaiseau.  Cet […]


    LIRE LA SUITE
  • IA et art : comment l’art et les musées se réinventent avec la data

    Depuis quelques années les IA artistiques se multiplient. Certaines sont capables de recréer une image selon le style d’un peintre, d’improviser avec un musicien en direct ou encore de composer un récit dont le lecteur est le héros. Sont-elles pour […]


    LIRE LA SUITE
  • L’intelligence artificielle transforme les voyages

    En matière de données, le secteur du tourisme est complexe à analyser. Pour 25% de données structurées produites par une multiplicité de professionnels (sites internet corporate et e-shop des voyagistes, hébergeurs, restaurateurs, transporteurs et logiciels CRM et de gestion), on […]


    LIRE LA SUITE
  • Emmanuel Bacry, speaker à l’European AI Night 2019

    European AI Night Emmanuel Bacry représentait nam.R lors de la seconde édition de l’European AI Night au Palais de Tokyo, le rendez-vous annuel européen pour les acteurs de l’intelligence artificielle : laboratoires, startups, géant de la tech ou amateurs d’IA. Encourager […]


    LIRE LA SUITE
  • nam.R à la conférence EGG Dataiku 2018

    À propos de la conférence EGG Dataiku Dataiku est un éditeur de logiciel français avec qui nous travaillons depuis le début de notre aventure. Ils se placent comme animateur de la communauté data à l’échelle nationale et internationale : à Paris, […]


    LIRE LA SUITE
  • Séminaire franco-norvégien “Observations, IA et développement durable”

    Les 24 et 25 septembre 2018, Charles Hutin-Persillon, Data Strategist et Nicolas Berthelot Lead Data Strategist de nam.R participaient au séminaire franco-norvégien sur le thème “Observations, IA et développement durable”. Observations, IA et développement durable Organisé par le Ministère de […]


    LIRE LA SUITE