#19.06 - Sensing earthquakes - Satellite images (architecture & deep learn-GAN)

• "Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables", Anthony Sladen (GeoAzur)

• "ColorMapGAN: Unsupervised domain adaptation for map segmentation using GANs", Onur Tasar (Inria, Titane team)

• "An architecture to massively download and deal with satellite images", Johnny Nguyen (M2-SSTIM, ACRI-ST)

A. Sladen:
Two thirds of the surface of our planet are covered by water and are still poorly instrumented, which has prevented numerous important questions to be adressed. The potential to leverage the existing fiber optic seafloor telecom cables that criss-cross the oceans, by turning them into dense arrays of seismo-acoustic sensors, remains to be evaluated. Here, we report Distributed Acoustic Sensing measurements on a 41.5 km-long telecom cable that is deployed offshore Toulon, France. Our observations demonstrate the capability to monitor with unprecedented details the ocean-solid earth interactions from the coast to the abyssal plain, in addition to regional seismicity (e.g., a magnitude 1.9 micro-earthquake located 100 km away) with signal characteristics comparable to those of a coastal seismic station.

Sladen, Rivet, Ampuero, De Barros, Hello, Calbris & Lamare (2019): "Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables "

O. Tasar:
Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations. The large shift between spectral distributions of training and test data causes the current state of the art supervised learning approaches to output poor maps. We present a novel end-to-end framework, called Color Mapping Generative Adversarial Networks (ColorMapGAN), that is robust to such shift. It can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to the distribution of the test images. We then use the fake images and the ground-truth for the training images to fine-tune the already trained classifier. Contrary to the existing GANs, the generator in ColorMapGAN does not have any convolutional or pooling layers. It learns to transform the colors of the training data to the colors of the test data by performing only one element-wise matrix multiplication and one matrix addition operations. ColorMapGAN outperforms the existing approaches by a large margin in terms of both accuracy and computational complexity.

Tasar, Happy, Tarabalka & Alliez (2019): "ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks"

J. Nguyen:
Since 2017, various types of images from the Sentinel satellites are available free of charge. These images are of a significant size (about 1 GB), and there are many of them (about 700 for a geographical area of 10,000 km² - knowing that the earth is 510 million km²).

Recovering this data by hand is a costly and time-consuming procedure that begs for automation and thus requires defining and implementing a data recovery architecture. The program that has been implemented makes it possible to manage the heavy data (1 GB) available on different complex interfaces (the different suppliers), over long time series (e. g. 3 years), and quickly. It also supports near-real-time downloading.

The resources needed include a large storage space, of the order of a hundred terabytes, and a remote server allowing the program to be deployed in production. The choosen language is python as it allows for prototyping and has many open-source libraries. The software used is docker. That software allows applications to be easily launched in software containers.

The service developed offers users different types of products containing images and metadata and corresponding to several satellite sources.