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Description
1. Requester Information:
Dr. Marouane Temimi
Associate Professor
Department of Civil, Environmental, and Ocean Engineering (CEOE)
Tel:+12012165303
Mail to: mtemimi@stevens.edu
2. Project Information:
The project aims to introduce an innovative, automated deep learning-based approach for near real-time satellite monitoring of river ice conditions within the northern watersheds of the United States and Canada. This technique harnesses high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and utilizes the U-Net deep learning algorithm for the semantic segmentation of images, even under challenging conditions such as varying cloud cover and land surface variations.
3. Project Description:
The project involves the development of software and scripts in multiple programming languages, including Bash, MATLAB, Python, and the Google Earth Engine.
4. Resource Requirements:
CPUs: 30
Memory: 64 Gb
Network Bandwidth: at least 1Gbps
EBS bandwidth: at least 1Gbps
Disk size: at least 300Gb
OS: Linux (Ubuntu)
Root access required
Options:
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Cloud Provider: AWS
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Required Services in the Cloud:
List of AWS Services
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EC2
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Amazon CloudWatch or Amazon Managed Grafana
List of Azure Services
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Virtual Machines
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Azure File Storage
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Azure Machine Learning
5. Timeline:
The project requires resources as soon as possible because the system is operational for Alaska, and it is currently winter, which is a critical period for river ice monitoring. The project is expected to run for a minimum of one year.
6. Security and Compliance Requirements:
No security and compliance requirements
7. Estimation:
Monthly: 624.15/Month for 1 year or 475.96/Month for 3 years
8. Approval:
Contact Dr. Marouane Temimi (contact info above)