Centerpoints Are All You Need in Overhead Imagery

Every day, observation satellites capture terabytes of imagery of the Earth's surface that feed into a wide variety of civil and military applications. This stream of data has grown so large that only automated methods can feasibly analyze it. One critical component of remote sensing analysis is object detection: locating objects of interest on the Earth's surface in overhead imagery. Automated object detection algorithms have advanced by leaps and bounds over the last decade, but they still require vast amounts of labeled data for training, which is expensive and tedious to produce. Any technique that can reduce the resources needed to label objects in overhead imagery is therefore desirable. Most existing datasets for training overhead object detectors are labeled with horizontal bounding boxes [1][2][3][4][5], object-aligned bounding boxes [6][7][8][9][10], or segmentation masks [11][12].