The Need – Mapping Africa

DEMAND

By 2050 Africa will have:

2X
Population

4X
Economy

3X
Food Demand

Meeting the demand will require substantial development in the agricultural sector.

CHALLENGES

Agricultural development contributes to
and is impacted by

Increasing

Climate Change

Growing

Socio-economic disputes (e.g. land competition)

Loss of

Biodiversity

So agriculture needs to become more resilient and sustainable.

Key Questions

To meet the demand in the face of these challenges, we need to answer several key questions

Where do the farmers live?
How are farmers’ agricultural practices affecting productivity?
How are environmental conditions affecting productivity?
Are the farms small, medium or large scale?

Where and how much cropland is there?
Is it expanding or contracting?
What type of crops are grown on it?
How productive are these crops?

A major data challenge

Agricultural maps typically don’t
exist and are hard to develop

ON GROUND
FROM SPACE

Unlike US & Europe,

Ground-level data
are scarce in Africa

Few Agricultural Surveys

Limited Land
Ownership Data

Too slow and expensive

Collecting field data is time consuming and expensive

Satellite imagery often too coarse or infrequent

Small-scale fields confuse mapping algorithms

Training data is expensive to obtain & often error prone

Satellite Imagery +
Algorithms +
Training Data =

Inaccurate maps

Why is this important?

DEMAND

By 2050 Africa will have:

2X Population

4X Economy

3X Food Demand

Meeting the demand will require substantial development in the agricultural sector.

CHALLENGES

Agricultural development contributes to and is impacted by

Increasing

Climate Change

Growing

Socio-economic disputes (e.g. land competition)

Loss of

Biodiversity

So agriculture needs to become more resilient and sustainable.

Key Questions

To meet the demand in the face of these challenges, we need to answer several key questions

A major data challenge

Agricultural maps typically don’t exist and are hard to develop

Unlike US & Europe,

Ground-level data are scarce in Africa

Few Agricultural Surveys

Limited Land Ownership Data

Too slow and expensive

Collecting field data is time consuming and expensive

Satellite imagery often too coarse or infrequent

Small-scale fields confuse mapping algorithms

Training data is expensive to obtain & often error prone

Satellite Imagery + Algorithms + Training Data =

Inaccurate maps

Why is this
important?

2X
Population

4X
Economy

3X
Food Demand

Agricultural development contributes to
and is impacted by

Agricultural maps typically don’t
exist and are hard to develop

Ground-level data
are scarce in Africa

Limited Land
Ownership Data

Satellite Imagery +
Algorithms +
Training Data =