Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

In recent years, the m ulti-task learning for k nowledge graph-based r ecommender system,
termed MKR, has shown its promising performance and has attracted increasing interest …

The extraction of building footprints, as a highly challenging task in remote sensing (RS)
image-based geospatial object detection and recognition, holds significant importance. Due …

Current state-of-the-art remote sensing salient object detectors always require high-
resolution spatial context to ensure excellent performance, which incurs enormous …

Land cover classification (LCC) is a process used to categorize the earth's surface into
distinct land types. This classification is vital for environmental conservation, urban planning …

Building 3-D reconstruction from remote sensing images has a wide range of applications in
smart cities, photogrammetry, and other fields. Methods for automatic 3-D urban building …

In the field of building extraction, many convolutional neural network (CNN)-based methods
have been developed to solve the problem of the irregular boundaries in their predictions …

The automatic extraction of lake water is one of the research hotspots in the field of remote
sensing image processing. Due to the small interclass variance between lakes and other …

Semantic segmentation (SS) and height estimation (HE) are two critical tasks in remote
sensing scene understanding that are highly correlated with each other. To address both the …

Boundary information is essential for the semantic segmentation of remote sensing images.
However, most existing methods were designed to establish strong contextual information …