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Represent Project

This document is part of the RepreSent project proposal that was accepted in January 2022 and responded to ESA ITT for AI4EO Challenges – Non-Supervised Learning (AO/1-10552/21/I-DT). The project is performed under ESA Contract No. 4000137253/22/I-DT.

Introduction

The main scope of the RepreSent project is to design, implement and validate artificial intelligence (AI) non-supervised techniques that will allow the use of the Copernicus Sentinel data. These techniques are developed for:

  • Fusion of Sentinel sensors (e.g., Sentinel-1 and Sentinel-2)
  • Single sensor classification (multispectral or SAR Sentinel sensors)
  • Change detection (e.g., Sentinel-1 or Sentinel-2)
  • Image time series analysis (e.g., Sentinel-2)

The software is implemented as an open source library. The validation is done on five use cases (UC) related to:

  • UC1) Forest disturbance monitoring
  • UC2) Automated Land Cover mapping
  • UC3) Anomaly detection in long time series of PS-P InSAR
  • UC4) Cloud detection and removal
  • UC5) Forest biomass estimation

The resulting datasets of the project is to be distributed freely to the AI4EO community.

Dataset Folder Structure

The dataset provided as part of the RepreSent project follows a specific folder structure to organize the files and code. Here's an overview of the directory structure:

  • notebooks

  • represent

    • callbacks
    • config
    • data
    • datamodules
    • experiments
      • uc1_contrastive_learning
      • uc1_forest_change_map
      • uc2_settlement_evaluation.py
      • uc3_building_anomaly_detection
      • uc3_benchmark
        • ganf
        • maxdiv
        • dense_autoencoder.py
      • uc3_lstm_autoencoder.py
      • uc4_odc.py
      • uc4_resnet.py
    • losses
    • models
      • moco.py
      • simclr_resnet.py
      • uc1_byol.py
      • uc1_pixel_level_contrastive_learning.py
      • uc1_resnet_base.py
      • uc1_resnet_dcva.py
      • uc2_maml.py
      • uc2_segmentation_resnet.py
      • uc2_supervised_resnet.py
      • uc3_benchmark
        • ganf
        • maxdiv
      • uc3_lstm_autoencoder.py
      • uc4_odc.py
      • uc4_resnet.py
    • tools

    Model Weights

    The trained weights can be found obtained from HERE. Unzip the weights into the code represent directory for using them with the notebooks.