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Deadline
29 Jun 2023
The registration is closed.

Disaster Risk Monitoring Using Satellite Imagery

Type
Course
Location
Web-based
Date
-
Duration
1 Days
Programme Area
Satellite Imagery and Analysis
Website
https://courses.nvidia.com/courses/course-v1:DLI+S-ES-01+V1/?ncid=so-link-59993…
Price
$0.00
Event Focal Point Email
unosat@unitar.org
Registration
Private – by invitation
Mode of Delivery
E-learning
Language(s)
English
Pillar
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