  Deep learning for detecting objects in remotely sensed imagery can enable new
technologies for important applications including mitigating climate change.
However, these models often require large datasets labeled with bounding box
annotations which are expensive to curate, prohibiting the development of
models for new tasks and geographies. To address this challenge, we develop
weakly-semi-supervised object detection (WSSOD) models on remotely sensed
imagery which can leverage a small amount of bounding boxes together with a
large amount of point labels that are easy to acquire at scale in geospatial
data. We train WSSOD models which use large amounts of point-labeled images
with varying fractions of bounding box labeled images in FAIR1M and a wind
turbine detection dataset, and demonstrate that they substantially outperform
fully supervised models trained with the same amount of bounding box labeled
images on both datasets. Furthermore, we find that the WSSOD models trained
with 2-10x fewer bounding box labeled images can perform similarly to or
outperform fully supervised models trained on the full set of bounding-box
labeled images. We believe that the approach can be extended to other remote
sensing tasks to reduce reliance on bounding box labels and increase
development of models for impactful applications.
