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Sentinel-2 Band Pan-Sharpening

Deploy the Sentinel-2 Band Enhancement model using Flask and Leaflet

Introduction

This is a state of the art Convolutional Neural Network algorithm to derive higher resolution images from existing lower resolution images using Sentinel-2 datasets as input. The code is adapted from https://github.com/lanha/DSen2 and is an extension for GUI interface and model deployment using Flask

Input: AOI

Output: Sentinel-2 Bands at 10m

Final Step Output

Requirements

This example requires the Ubuntu 16.

  • Tensorflow 2 GPU
  • Python 3.7

Ideal EC2 Instances

  • g4dn.4xlarge
  • p2.xlarge

Ideal System Config

  • 64 GB Mem
  • 12 GB GPU

Instructions

The applications will run through Flask and the processing time depends on Network Speed and GPU/RAM

  • Clone the repo
  • Move Sentinel_2A_PS to home directory (/home/ubuntu)
  • mkdir -p /home/ubuntu/digisat
  • Move the other 3 files from repo to digisat Moveinit.shto/home/ubuntu/`
  • Give permissions to init.sh using sudo chmod 755 init.sh
  • Run ./init.sh

The applications should be hosted at http://<your_ip>:5000