Increasing the Spatial Resolution of Satellite Images With Deep Learning

This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at https://omdena.com/projects.

The problem

The problem that this project is trying to address is the limitation of the spatial resolution of Sentinel-2 satellite images. Sentinel-2 is a satellite mission from the European Space Agency (ESA) that provides multi-spectral imagery for various applications such as land monitoring, agriculture, and forestry. However, the spatial resolution of Sentinel-2 images is limited to 10 meters for some bands and 20 meters for others. This means that some details in the images may not be visible or may be too small to be useful.

To address this problem, this project proposes the use of deep learning models, specifically generative networks like GANs (Generative Adversarial Networks), to increase the spatial resolution of Sentinel-2 images by a factor of 10x. GANs are a type of neural network that can generate new data samples that are similar to a given set of training data. By training a GAN on high-resolution images and low-resolution Sentinel-2 images, the model can learn to generate high-resolution images that contain the same level of detail as the training data.

The project goals

The main project goal is to build a deep learning model, in particular generative networks like GAN, capable of increasing the spatial resolution of sentinel 2 satellite images by a factor of 10x.

Scope of the project:

• The model must work on the 4 RGBN bands (Red, Green, Blue, Nir) at 16bit to exploit all the information contained in the starting image. 
• The expected output is 16-bit.

**More details will be shared with the selected team.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

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