  The challenge of labeling large example datasets for computer vision
continues to limit the availability and scope of image repositories. This
research provides a new method for automated data collection, curation,
labeling, and iterative training with minimal human intervention for the case
of overhead satellite imagery and object detection. The new operational scale
effectively scanned an entire city (68 square miles) in grid search and yielded
a prediction of car color from space observations. A partially trained yolov5
model served as an initial inference seed to output further, more refined model
predictions in iterative cycles. Soft labeling here refers to accepting label
noise as a potentially valuable augmentation to reduce overfitting and enhance
generalized predictions to previously unseen test data. The approach takes
advantage of a real-world instance where a cropped image of a car can
automatically receive sub-type information as white or colorful from pixel
values alone, thus completing an end-to-end pipeline without overdependence on
human labor.
