Small farms contribute to a large share of the productive land in developing
countries. In regions such as sub-Saharan Africa, where 80% of farms are small
(under 2 ha in size), the task of mapping smallholder cropland is an important
part of tracking sustainability measures such as crop productivity. However,
the visually diverse and nuanced appearance of small farms has limited the
effectiveness of traditional approaches to cropland mapping. Here we introduce
a new approach based on the detection of harvest piles characteristic of many
smallholder systems throughout the world. We present HarvestNet, a dataset for
mapping the presence of farms in the Ethiopian regions of Tigray and Amhara
during 2020-2023, collected using expert knowledge and satellite images,
totaling 7k hand-labeled images and 2k ground collected labels. We also
benchmark a set of baselines including SOTA models in remote sensing with our
best models having around 80% classification performance on hand labelled data
and 90%, 98% accuracy on ground truth data for Tigray, Amhara respectively. We
also perform a visual comparison with a widely used pre-existing coverage map
and show that our model detects an extra 56,621 hectares of cropland in Tigray.
We conclude that remote sensing of harvest piles can contribute to more timely
and accurate cropland assessments in food insecure region.

基于收割堆的遥感在农田评估中的应用