Working on problematics of change detection in SAR Time Series of forests with the help of Deep Learning methods.

SONDRA Laboratory is a laboratory mixing 4 entities: French ONERA, Supélec (known today as CentraleSupélec), the National University of Singapore and the DSO of Singapore.

• Use of multimodal deep learning networks for duplicate product identification in a multi-retailer database.
• Development of multimodal siamese networks with different losses evaluated and benchmarked (contrastive loss, triplet loss).
• Analysis of sampling strategy: mini-batch hard sampling, semi-hard sampling, uniform-random sampling, mini-batch distance weighted negative sampling
• Comparison with more traditional ML approach (PCA + Decision Tree + Handcoded textual features)

As a deep learning Intern, I have trained, tuned and tested a model capable of doing building segmentation using satellite imagery. In this internship, I have tried multiple models (Mask RCNN, UNet, Deep UNet) and tried to take the best out of them all. The code was written using Keras with Tensorflow Back-End and was manipulated using a web-based RESTFul GUI with Flask and HTML5 technologies.

Also, multiple postprocessing technologies were considered and tried such as Logistic Regression (using scikit-learn) or Conditional Random Field (using pycrf).