  Accurate mapping of oil palm is important for understanding its past and
future impact on the environment. We propose to map and count oil palms by
estimating tree densities per pixel for large-scale analysis. This allows for
fine-grained analysis, for example regarding different planting patterns. To
that end, we propose a new, active deep learning method to estimate oil palm
density at large scale from Sentinel-2 satellite images, and apply it to
generate complete maps for Malaysia and Indonesia. What makes the regression of
oil palm density challenging is the need for representative reference data that
covers all relevant geographical conditions across a large territory.
Specifically for density estimation, generating reference data involves
counting individual trees. To keep the associated labelling effort low we
propose an active learning (AL) approach that automatically chooses the most
relevant samples to be labelled. Our method relies on estimates of the
epistemic model uncertainty and of the diversity among samples, making it
possible to retrieve an entire batch of relevant samples in a single iteration.
Moreover, our algorithm has linear computational complexity and is easily
parallelisable to cover large areas. We use our method to compute the first oil
palm density map with $10\,$m Ground Sampling Distance (GSD) , for all of
Indonesia and Malaysia and for two different years, 2017 and 2019. The maps
have a mean absolute error of $\pm$7.3 trees/$ha$, estimated from an
independent validation set. We also analyse density variations between
different states within a country and compare them to official estimates.
According to our estimates there are, in total, $>1.2$ billion oil palms in
Indonesia covering $>$15 million $ha$, and $>0.5$ billion oil palms in Malaysia
covering $>6$ million $ha$.
