The annotations used during the training process are crucial for the inference results of
remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be …

Remote sensing (RS) images enable high-resolution information collection from complex
ground objects and are increasingly utilized in the earth observation research. Recently, RS …

This research paper proposes a real-time obstacle avoidance strategy for mobile robots with
a monocular camera. The approach uses a binary semantic segmentation FCN-VGG-16 to …

Automatic recognition and size measurement of nanoparticles in SEM images have been an
evolving field of study within the material informatics domain. These methods have been …

As a method that can incorporate unlabeled data into model training, semi-supervised
semantic segmentation (SSS) can mitigate the burden of manual annotation in geographic …

Semantic segmentation is a fundamental task in computer vision and finds extensive
applications in scene understanding, medical image analysis, and remote sensing. With the …

Mangroves grow in intertidal zones in tropical and subtropical regions, offering numerous
advantages to humans and ecosystems. Mangrove monitoring is one of the important tasks …

Introduction Accurately predicting suitable areas for double-cropped soybeans under
changing climatic conditions is critical for ensuring food security anc optimizing land use …

The accuracy of mapping agricultural fields across large areas is steadily improving with
high-resolution satellite imagery and deep learning (DL) models, even in regions where …

Accurate urban land cover information is crucial for effective urban planning and
management. While convolutional neural networks (CNNs) demonstrate superior feature …