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Date limite
29 juin 2023
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Disaster Risk Monitoring Using Satellite Imagery

Type
Course
Emplacement
Web-based
Date
-
Durée
1 Days
Zone du programme
Satellite Imagery and Analysis
Site internet
https://courses.nvidia.com/courses/course-v1:DLI+S-ES-01+V1/?ncid=so-link-59993…
Prix
0.00 $US
Personne de référence de l'évenement
unosat@unitar.org
Inscription
Private – by invitation
Mode de livraison
E-learning
Langue (s)
English
Pilier
United Nations Satellite Centre UNOSAT
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The personal data of participants applying for, registering for or participating in UNITAR's training courses and other events is governed by the Data Protection and Privacy Policy. By applying for, registering for or participating in this event, the participant acknowledges that he or she is, (or they are) aware of the policy and agree to its terms.

Learn how to build and deploy a deep learning model to automate the detection of flood events using satellite imagery. This workflow can be applied to lower the cost, improve efficiency, and significantly enhance the effectiveness of various natural disaster management use cases.

 

THIS COURSE IS CO-DEVELOPED BY UNOSAT and NVIDIA. IN ORDER TO REGISTER FOR THIS COURSE, PLEASE KINDLY ENROLL ON THIS WEBSITE: https://courses.nvidia.com/courses/course-v1:DLI+S-ES-01+V1/?ncid=so-link-599935#cid=dli03_so-link_en-us

By participating in this is course, you will learn how to:

  • Implement a machine learning workflow for disaster management solutions
  • Use hardware accelerated tools to process large satellite imagery data
  • Apply transfer-learning to cost-efficiently build deep learning segmentation models
  • Deploy deep learning models for near real-time analysis
  • Utilize deep learning-based model inference to detect and respond to flood events

 

Prerequisites:

  • Competency in the Python 3 programming language
  • Basic understanding of Machine Learning and Deep Learning concepts (specifically variations of CNNs) and pipelines
  • Interest in understanding how to manipulate satellite imagery using modern methods

Tools, libraries, frameworks used: NVIDIA DALI, NVIDIA TAO Toolkit, NVIDIA TensorRT, and NVIDIA Triton Inference Server