Soft-labeling Strategies for Rapid Sub-Typing
This work addresses the challenge of limited labeled data in computer vision for satellite imagery, offering a domain-specific solution to reduce human labor in data curation.
The researchers tackled the problem of labeling large satellite image datasets for object detection by developing an automated pipeline that uses iterative soft-labeling with a partially trained YOLOv5 model, achieving the ability to predict car color from space observations across an entire 68-square-mile city.
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.