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    Delineating Agricultural Parcels Using Deep Learning | June 29th, 2022 at 10:00 AM

    Duration: 2 hours

    Organizers: VITO Remote Sensing  

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    Language: English

    Prerequisites:

    • Understanding of EO concepts
    • Basic understanding of ML concepts
    • Basic level python programming

    Summary: 

    In this training session, you will learn to delineate agricultural parcels with deep learning, using openEO. In the training, we will look both at the data science part of this use case (how edge detection in general works, and more specifically, how a U-Net works) and also at the computing side of it. For the computing side, we will be using the openEO platform and its Python client, which is an API that allows users to connect to various earth observation cloud backends using one client syntax. Finally, you will learn how you can make use of openEO yourself for EO data processing.

    Agricultural parcel delineation is important for a variety of reasons. It aids ministries and the private sector in decision-making and planning. It helps facilitate land registration. It is used in estimating subsidies, regulating water rights, and even for scientific purposes such as climate modeling (Garcia-Pedrero et al., 2017). Lastly, field delineation can improve classification results in other EO applications such as crop type mapping.


    Learning Outcomes:

    At the end of this training, you will:
    • Know how a U-Net works and why it was developed
    • Know about recent developments in distributed computing and why they are necessary
    • Be familiar with basic and more complex operations in openEO
    • Be able to do inference of pre-trained U-Net models yourself
    • Be able to do delineate agricultural parcels

  • Agenda

    • Introduction
    • Field delineation: choosing a model
      • History of edge detection
      • U-Net
      • Pre- and postprocessing
    • Field delineation: choosing a platform
      • Introduction to openEO
      • openEO workflows
      • openEO in practice
    • Hands-on demonstration using Jupyter Notebooks

  • Presentation

  • Record from webinar

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