Deep learning (DL) has great influence on large parts of science and increasingly
established itself as an adaptive method for new challenges in the field of Earth observation …

Road extraction is to automatically label the pixels of roads in satellite imagery with specific
semantic categories based on the extraction of the topographical meaningful features. For …

Data augmentation is a commonly used technique for increasing both the size and the
diversity of labeled training sets by leveraging input transformations that preserve …

" In this chapter, we introduce some of the very basics that are used throughout the book.
First, we give the definition of a topological space and related notions of open and closed …

We present a preliminary report for xBD, a new large-scale dataset for the advancement of
change detection and building damage assessment for humanitarian assistance and …

One of the most challenging research subjects in remote sensing is feature extraction, such
as road features, from remote sensing images. Such an extraction influences multiple …

One of the most important tasks in the advanced transportation systems is road extraction.
Extracting road region from high-resolution remote sensing imagery is challenging due to …

Recently, the first foundation model developed specifically for image segmentation tasks
was developed, termed the" Segment Anything Model"(SAM). SAM can segment objects in …

Online lane graph construction is a promising but challenging task in autonomous driving.
Previous methods usually model the lane graph at the pixel or piece level, and recover the …

Road extraction from high-resolution remote sensing images is a challenging but hot
research topic in the past decades. A large number of methods are invented to deal with this …