Fighting Hunger through Open Satellite Data: A New State of the Art for Land Use Classification

Nowadays, we have access to a tremendous amount of high-resolution satellite images of the earth. And with tools like Google Earth Engine, getting the data has never been easier and faster.

This huge amount of open data is a great opportunity to use AI for Good and create useful applications.

For the past 4 weeks, I have been working with a team in one of Omdena’s AI Challenges in collaboration with the United Nations World Food Program.

The goal is to fight hunger in Nepal by locating, tracking, and improving the growth of crops such as rice and wheat.

To locate the crops in Nepal, we need an open and good source of high-resolution satellite images.

One of the best open data sources, in 2019, is provided by the Sentinel satellites of the Copernicus program (European Space Agency).

The satellites cover the full earth on 13 bands with a revisiting every 5 days.

The Sentinel 2 spatial resolution is 10 meters per pixel (and only for some bands). It is a much lower resolution than what we would get using paid services (some services provide images with a resolution of less than 1m per pixel).

Using this 10 meters resolution to learn crops classification or segmentation is quite a challenge, especially in Nepal where crops fields are not that big.

Thankfully, Deep Learning is a powerful tool we can use for this job.

Furthermore, because we don’t have a lot of labeled data on Nepal crops, it would be helpful to benefit from a pre-trained Neural Network. Starting to train a model from scratch would require much more labeled data.

Usually, in Computer Vision, we can get great results using models pre-trained on the ImageNet dataset (14M images in 20.000 categories). But the ImageNet categories (animals, plants, food, …) are very different from satellite imagery.