Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Recently, agriculture has gained much attention regarding automation by artificial
intelligence techniques and robotic systems. Particularly, with the advancements in machine …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover map**, urban change detection …

Sudden-onset natural and man-made disasters represent a threat to the safety of human life
and property. Rapid and accurate building damage assessment using bitemporal high …

Semantic segmentation of remote sensing images plays an important role in a wide range of
applications, including land resource management, biosphere monitoring, and urban …

Map** the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …

Semantic segmentation of remotely sensed imagery plays a critical role in many real-world
applications, such as environmental change monitoring, precision agriculture …

Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

Semantic segmentation from very fine resolution (VFR) urban scene images plays a
significant role in several application scenarios including autonomous driving, land cover …

Accurate urban land-use maps, which reflect the complicated land-use pattern implied in the
function and distribution of land-cover types, play an important role in urban analysis. In …