HSAT has built the world’s largest database of crops. This data helps understand food security and the impact of climate change.
This data is used by agriculture firms, insurance companies and traders.
But why?
Satellite data has the ability to “see” crops around the world, however the “view” is not very good. This is because the satellite data used for crop predictions has 10 meter resolution, this means that its not possible to identify crops simply by looking at the image. This means that the smallest area seen is 10mx10m – or several times bigger than a car. This is too low resolution to identify a crop from a picture.
As a comparison the satellite data seen in Google Maps is 0.2 meter resolution
The Challenge - Low Resolution
The Solution – Machine Learning
To resolve this issue machine learning is used to build models and identify the signal from different crops
Crop Signals
These signals are created using different spectral bands (e.g infra-red) examined across time
Spectrum and Time
This data then results in probability maps of every field across an entire country. These maps are then used to predict the area
Probability of Crops
Machine Learning – Needs Ground Truth
These signals are all built with machine learning and machine learning requires “ground truth”
The ground truth is used to teach of “train” the system. The satellite cannot recognise what sugar or wheat is, so we build a model using thousands of examples of sugar, wheat, rice, etc. The machine then learns from this data and then generates the “machine learning” model that predicts the crops