Catherine Nakalembe

They're homogeneous, so there's like one crop over a really large area.

There's been a lot of investment in collecting data to have those examples.

So when I build a model and train to predict what is growing in Kansas, it's easy.

But for me to produce a map of where maize is in Kenya, a whole different story, because I don't have those examples yet.

It's much harder to collect the data because there's no default data collection for this purpose.

The other side of it is that the satellites that we have access to, where we have open data that allows you to scale for all of Kenya, the resolution doesn't fit the small Kenyan fields.

And so if I use the same amount of labels but trained on the same data set for Kenya and the U.S.,

It wouldn't work because these fields are so much smaller and much more complex.

So the products we have are usually not relevant at the farmer's scale.

They're relevant at a larger scale.

Like I can tell, yes, there is a drought in Western Kenya.

It has affected approximately 20,000 plus or minus 5,000 acres.

A week ago, there was a huge hailstorm in Kenya and there was hail everywhere.

literally.

So we can actually see, we can show the damage, we can give an approximation.

However, it is not the same as me, a farmer, Jane, in my field in Hoima, for example, this is in Uganda.

What exactly happened to me?

Was my field completely destroyed?

Maybe.

Was it not?