Change detection (CD) is essential in remote sensing (RS) for natural resource monitoring,
territorial planning, and disaster assessment. With the abundance of data collected by …

Leveraging the extensive training data from SA-1B, the segment anything model (SAM)
demonstrates remarkable generalization and zero-shot capabilities. However, as a category …

Monitoring changes in the Earth's surface is crucial for understanding natural processes and
human impacts, necessitating precise and comprehensive interpretation methodologies …

Remote sensing image change captioning (RSICC) aims to articulate the changes in objects
of interest within bitemporal remote sensing images using natural language. Given the …

Temporal image analysis in remote sensing has traditionally centered on change detection,
which identifies regions of change between images captured at different times. However …

Segment Anything Model (SAM) is an advanced foundational model for image
segmentation, which is gradually being applied to remote sensing images (RSIs). Due to the …

The segmentation and interpretation of the Martian surface play a pivotal role in Mars
exploration, providing essential data for the trajectory planning and obstacle avoidance of …

This study addresses the biofouling challenges in Tidal Stream Turbines (TSTs) to ensure
their reliable and optimal operation. In this context, it is proposed an effective methodology …

Vision foundation models (VFMs) have rapidly gained application across various visual
scenarios due to their robust universality and generalization capabilities. However, when …

Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing
various image interpretation tasks. Pretraining is an active research topic, encompassing …