  The representations of the Earth's surface vary from one geographic region to
another. For instance, the appearance of urban areas differs between
continents, and seasonality influences the appearance of vegetation. To capture
the diversity within a single category, like as urban or vegetation, requires a
large model capacity and, consequently, large datasets. In this work, we
propose a different perspective and view this diversity as an inductive
transfer learning problem where few data samples from one region allow a model
to adapt to an unseen region. We evaluate the model-agnostic meta-learning
(MAML) algorithm on classification and segmentation tasks using globally and
regionally distributed datasets. We find that few-shot model adaptation
outperforms pre-training with regular gradient descent and fine-tuning on (1)
the Sen12MS dataset and (2) DeepGlobe data when the source domain and target
domain differ. This indicates that model optimization with meta-learning may
benefit tasks in the Earth sciences whose data show a high degree of diversity
from region to region, while traditional gradient-based supervised learning
remains suitable in the absence of a feature or label shift.
