Extraction of cultivated land information from high spatial resolution remote sensing images
is increasingly becoming an important approach to digitization and informatization in …

Extracting farmland from high spatial resolution remote sensing images is a basic task for
agricultural information management. According to Tobler's first law of geography, closer …

Traditional and convolutional neural network (CNN)-based geographic object-based image
analysis (GeOBIA) land-cover classification methods prosper in remote sensing and …

Image segmentation is a key technique involved in information extraction from high spatial
resolution remote sensing images. Studying the impact of the evaluation method on the …

National mapping programs (eg, INFOMAR and MAREANO) and global efforts (Seabed
2030) acquire large volumes of multibeam echosounder data to map large areas of the …

Quickly obtaining fine-scale mining area types information in large-scale scenes is
significant for dynamically detecting mineral resources. Currently, mining area types …

As valued members of forest ecosystems, fast-growing tree species are of great importance
for protecting natural forests, fulfilling the demand for raw wood material, and preserving …

Accurate information on agricultural field boundaries is important for precision agriculture.
Contour detection combining local cues presents a high performance on nature images …

Change detection (CD) remains an important issue in remote sensing applications,
especially for high spatial resolution (HSR) images, but it has yet to be fully resolved. This …

Spatially explicit information about forest cover is fundamental for operational forest
management and forest monitoring. Although open-satellite-based earth observation data in …