Disasters can have devastating impacts on communities and economies, underscoring the
urgent need for effective strategic disaster risk management (DRM). Although Artificial …

After a disaster, ascertaining the operational state of extensive infrastructures and building
clusters on a regional scale is critical for rapid decision-making and initial response. In this …

Accurate extraction of rooftop photovoltaic from high-resolution remote sensing imagery is
pivotal for propelling green energy planning and development. Conventional deep learning …

High spatial resolution remote sensing images (HRRSIs) contain intricate details and varied
spectral distributions, making their semantic segmentation a challenging task. To address …

Recent years have witnessed the superiority of deep learning-based algorithms in the field
of HSI classification. However, a prerequisite for the favorable performance of these …

Generalized few-shot semantic segmentation (GFSS) unifies semantic segmentation with
few-shot learning showing great potential for Earth observation tasks under data scarcity …

When natural disasters occur, timely and accurate building damage assessment maps are
vital for disaster management responders to organize their resources efficiently. Pairs of pre …

Few-shot aerial image semantic segmentation is a challenging task that requires precisely
parsing unseen-category objects in query aerial images with limited annotated support …

Image-level weakly supervised semantic segmentation (WSSS) methods have gained
prominence in remote sensing image building extraction tasks, primarily due to their cost …

Forests are the most widely distributed terrestrial vegetation type and play a significant role
in the global carbon cycle and ecological diversity. Accurate and timely forest detection …