Pixel-wise classification of remote sensing imagery is highly interesting for tasks like land cover classification or change detection. The acquisition of large training data sets for these tasks is challenging, but necessary ...

Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object ...

The automation of inspections in aircraft engines is an ever-increasing growing field of research. In particular, the inspection and quantification of coating damages in confined spaces, usually performed manually with ...

This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes ...

Land use (LU) is an important information source commonly stored in geospatial databases. Most current work on automatic LU classification for updating topographic databases considers only one category level (e.g. ...

Pixel-based land cover classification of aerial images is a standard task in remote sensing, whose goal is to identify the physical material of the earth's surface. Recently, most of the well-performing methods rely on ...

Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the ...