Disaitek's purpose is to fight against environmental and public health damages. Our goal is to get rid of pollution, make companies comply with environmental regulation and shed light on unpunished polluting activities. 

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Founded in 2017, Disaitek is a French company based in Paris which has invested early in R&D to develop strong skills and a best of its kind knowledge in the integration of optical, thermal and SAR satellite data into proprietary AI models to raise in semi real time alarms about pollution events at risk (illegal landfills, GHG leaks, illegal lodging, pollutants, etc.)

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Our cutting-edge algorithms automate the detection of pollution sources at scale, allowing us to notify end users about risks like fire outbreaks, air, water and soil pollution and public health risks.

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The new space sector with artificial intelligence will disrupt the environment risk market and environmental compliance market. With its abilities to deliver at scale, targeted markets for Disaitek are worldwide and benefit large corporation with interests all over the world.

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Disaitek specializes in the detection, quantification and monitoring of sources of pollution by leveraging earth observation technologies, advanced artificial intelligence and open source intelligence.

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Disaitek is labelled as French Young Innovative Enterprise. 

Disaitek is part of the incubation program delivered by ESA and Incuballiance (Paris Saclay).

Disaitek has also been accepted as part of the BLAST acceleration program (consortium composed of Polytechnique, SATT Paris Saclay, ONERA and Starburst, with CNES as partner).  

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Skill set

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Computer Vision

In Image Recognition, our core skills have been strengthened with one outstanding experience we have conducted in 2020 with the University of Geneva in order to develop new methodologies to detect exoplanets. Deep learning applied to exoplanets has been here for a few years but only for uniform orbital transit. Using data from the Kepler & TESS space telescope, our challenge was to detect exoplanets with non periodic orbits. We have implemented state of the art image analysis technology in a context of low signal to noise data to be able to achieve this task.

Since then we have developed new methodologies using self and semi supervised training in order to reduce the amount of labels required for semantic segmentation tasks. ​

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Earth Observation

Disaitek uses state of the art processing methodologies to combine data coming from different sensors and satellites. We leverage optical, thermal and SAR data to train AI models in various methodologies (SSL, semi supervised and supervised) to capture insights on what we observe in the air and on the ground.  ​

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Natural Language Processing

Disaitek area of expertise is the use of deep learning methods for NLP tasks. More specifically, we use neural networks that have been trained in an unsupervised way on a huge corpus of documents and transfer their knowledge to other problems. In order to do this, we use state of the art semi-supervised learning algorithms and create proxy tasks to get the most value possible out of our data.

Disaitek has been contributing to Pytorch implementations of GPT and BERT, large pre-trained models that capture deep information about syntax and grammar of languages.

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