Planetary Observation & Climate Resilience

Tracking environmental changes requires a mix of in-situ measurments, and remote sensing with drones, remotely operated vehicles, and satellite imagery. We use artificial intelligence on this data to automated it's interpretation, and improve the way we can monitor our planet and other planets in the solar system.

Computer vision has a major role to play in the study of the oceans. For instance, we worked on automating the classification of deep-sea biota using advanced deep learning models trained on remotely operated vehicle (ROV) image datasets. We developed a dataset with nearly 4,000 images across 33 organism classes, achieving classification accuracies up to 65% using Inception-ResNet architecture. This work aims to improve biodiversity assessments and conservation efforts in remote marine environments.

We also have spent considerable efforts developping tools to automate the recovery and correction of marine scenes in satellite images, notably to monitor coral reefs. The issue is that most operator's corrections (e.g. NASA, ESA) rely on dark vegetation or other continental features to auto-correct the images. When one is trying to build satellite time-series from Islands or coral reefs, it is difficult because the quality of the scene advertised by the operator cannot be always trusted, and the corrections can be inconsistent. Machine learning can overcome these issues.

One of the most exciting things about remote sensed data is that it is not limited to Earth. In fact, we have a growing collection of data from other planets in the solar system, notably Mars and the moon. Mars offers some exciting challenges, with multimodal data comprising satellite imagery, high-resolution imagery, hyperspectral data, and in-situ rover data.

We worked extensively on the Compact Reconnaisance Imaging Spectrometer for Mars (CRISM) instrument, a satellite capturing data across Mars in a number of different wavelength. This hyperspectral data is powerful, allowing us to make inference about the surface composition of the planet. However, the data quality of the instrument was declining over time, and became unsable. We demonstrated that AI could be used to denoise the signal, and to confidently identify minerals with a calibrated confidence. This approach can be extended in future to other bodies in the solar system.