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DISASTER RISK MONITORING USING SATELLITE IMAGERY

28.08.2023 |

Embark on transformative learning as you gain experience constructing and implementing a deep learning model to automate flood identification through satellite imagery. This innovative workflow has the potential to revolutionize natural disaster management by reducing costs, improving operational efficiency, and significantly increasing overall effectiveness.

Course Objectives:

  • Construct a machine learning workflow tailored to address challenges in disaster management solutions.
  • Use hardware-accelerated tools to process extensive satellite imagery datasets.
  • Apply transfer learning strategies to economically develop advanced deep learning segmentation models.
  • Deploy deep learning models to enable near real-time analysis.
  • Leverage the skills of deep learning-based models to rapidly detect and respond to floods.

Prerequisites:

  • Proficiency in the Python programming language 3.
  • Basic understanding of machine learning and deep learning principles (with emphasis on different CNN variants) and their application in pipelines.
  • Curiosity to explore advanced methods for manipulating satellite imagery.

Tools used:

  • NVIDIA DALI
  • NVIDIA TAO Toolkit
  • NVIDIA TensorRT
  • NVIDIA Triton Inference Server.

Relevant training:

“Deploying an Inference Model at Production Scale”: a self-paced course that focuses on Triton for deploying deep learning models built in different frameworks.

For additional training through the NVIDIA Deep Learning Institute, visit www.nvidia.com/dli

The course was developed in collaboration with UNOSAT, the United Nations Satellite Centre.

Details

Target audience

Digital skills for all

Digital skills for the workforce

Digital skills for ICT professionals

Digital technology

Digital skills

Software development

Level

Middle

Format of the training

Online

Training fee

Free training

Duration of the training

Type of training

Language of the training

English

Country providing the training

Other

Classification

Database

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