Remote Sensing Object Detection

Remote sensing object detection uses computer vision techniques to identify and locate objects within images captured from satellites or aerial platforms. Current research emphasizes improving model accuracy and efficiency through advancements in feature extraction (e.g., employing feature pyramid networks and novel convolution methods), data augmentation strategies (like manipulating bounding boxes), and pre-training techniques tailored to the unique characteristics of remote sensing imagery. These improvements are crucial for applications ranging from urban planning and environmental monitoring to precision agriculture and disaster response, enabling more accurate and timely analysis of large-scale geospatial data.