UNITAR Online Catalogue
Disaster Risk Monitoring Using Satellite Imagery
United Nations Satellite Centre UNOSAT
Type
Course
Location
Web-based
Date
to
Duration
1 Days
Programme Area
Satellite Imagery and Analysis
Event Focal Point Email
unosat@unitar.org
Background
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
Learning Objectives
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
Additional Information
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