Ship detection and classification pose significant challenges in remote sensing. The potent
feature extraction capabilities of deep learning algorithms render them pivotal for these …

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …

Abstract Large Vision-Language Models (VLMs) have demonstrated impressive
performance on complex tasks involving visual input with natural language instructions …

Typical representations for arbitrary-oriented object detection tasks include the oriented
bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet) …

Geospatial Copilots unlock unprecedented potential for performing Earth Observation (EO)
applications through natural language instructions. However existing agents rely on overly …

Abnormal behavior detection is currently receiving much attention because of the availability
of marine equipment and data allowing maritime agents to track vessels. One of the most …

With the development of deep learning (DL), research on ship classification in synthetic
aperture radar (SAR) images has made remarkable progress. However, such research has …

Speckle noise and the spatial resolution of the Sentinel− 1 Synthetic Aperture Radar (SAR)
image can cause significant difficulties in the detection of small objects, such as small ships …