Weakly-semi-supervised object detection in remotely sensed imagery

TL;DR

This paper introduces weakly-semi-supervised object detection models for remote sensing imagery that leverage minimal bounding box labels and abundant point labels, significantly reducing annotation costs while maintaining high performance.

Contribution

It develops a novel WSSOD approach that combines point and bounding box labels, outperforming fully supervised models with fewer bounding box annotations.

Findings

WSSOD models outperform fully supervised models with the same bounding box data.

Models trained with 2-10x fewer bounding boxes match or exceed full data performance.

Approach reduces annotation effort for remote sensing object detection.

Abstract

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.