Taking it further: leveraging pseudo labels for field delineation across
  label-scarce smallholder regions
Philippe Rufin Sherrie Wang Sá Nogueira Lisboa Jan Hemmerling Mirela G. Tulbure Patrick Meyfroidt
Transfer learning allows for resource-efficient geographic transfer of
pre-trained field delineation models. However, the scarcity of labeled data for
complex and dynamic smallholder landscapes, particularly in Sub-Saharan Africa,
remains a major bottleneck for large-area field delineation. This study
explores opportunities of using sparse field delineation pseudo labels for
fine-tuning models across geographies and sensor characteristics. We build on a
FracTAL ResUNet trained for crop field delineation in India (median field size
of 0.24 ha) and use this pre-trained model to generate pseudo labels in
Mozambique (median field size of 0.06 ha). We designed multiple pseudo label
selection strategies and compared the quantities, area properties, seasonal
distribution, and spatial agreement of the pseudo labels against
human-annotated training labels (n = 1,512). We then used the human-annotated
labels and the pseudo labels for model fine-tuning and compared predictions
against human field annotations (n = 2,199). Our results indicate i) a good
baseline performance of the pre-trained model in both field delineation and
field size estimation, and ii) the added value of regional fine-tuning with
performance improvements in nearly all experiments. Moreover, we found iii)
substantial performance increases when using only pseudo labels (up to 77% of
the IoU increases and 68% of the RMSE decreases obtained by human labels), and
iv) additional performance increases when complementing human annotations with
pseudo labels. Pseudo labels can be efficiently generated at scale and thus
facilitate domain adaptation in label-scarce settings. The workflow presented
here is a stepping stone for overcoming the persisting data gaps in
heterogeneous smallholder agriculture of Sub-Saharan Africa, where labels are
commonly scarce.