  Spatial variability has been observed in many geo-phenomena including
climatic zones, USDA plant hardiness zones, and terrestrial habitat types
(e.g., forest, grasslands, wetlands, and deserts). However, current deep
learning methods follow a spatial-one-size-fits-all(OSFA) approach to train
single deep neural network models that do not account for spatial variability.
In this work, we propose and investigate a spatial-variability aware deep
neural network(SVANN) approach, where distinct deep neural network models are
built for each geographic area. We evaluate this approach using aerial imagery
from two geographic areas for the task of mapping urban gardens. The
experimental results show that SVANN provides better performance than OSFA in
terms of precision, recall,and F1-score to identify urban gardens.

Computer Vision and Pattern Recognition;Machine Learning