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Deep-learning-based object detection has recently played a vital role in both computer vision and Earth observation communities. However, the performance of modern object detectors is highly limited by the quantity and quality of manually labeled training samples. Furthermore, compared to object detection in natural scenes, remote-sensing object detection (RSOD) faces two specific critical challenges: 1) densely arranged instances: geospatial objects tend to be densely packed in remote-sensing scenarios and 2) large variations in object scale: the wide field of the bird’s eye view leads to dramatic variations in object scale across various categories. The above issues bring significant difficulties to attaining manual annotations for deep-learning-based RSOD. To this end, in this article, we turn our attention from fully supervised RSOD to semisupervised RSOD and propose a novel framework based on the teacher …