GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-
wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists …

Deep learning (DL) is a powerful state-of-the-art technique for image processing including
remote sensing (RS) images. This letter describes a multilevel DL architecture that targets …

Many applied problems arising in agricultural monitoring and food security require reliable
crop maps at national or global scale. Large scale crop map** requires processing and …

Abstract Google Earth Engine (GEE) serves as a versatile platform for processing and
visualising geospatial datasets, with its primary aim being to provide an open platform for …

For many applied problems in agricultural monitoring and food security it is important to
provide reliable crop classification maps in national or global scale. Large amount of …

In the paper we propose the methodology for solving the large scale classification and area
estimation problems in the remote sensing domain on the basis of deep learning paradigm …

The methodology for solving the problem of processing of large amount of remote sensing
data is proposed. The hierarchical structure of the model of deep learning method is based …

Land cover information is vital for research and applications concerning natural resources
and environmental modeling. Accuracy assessment is an important dimension in use and …

Our aim is to propose a new deep learning framework approach which uses an ensemble of
convolutional neural network (CNN) for land use-land cover map**. Every CNN layer was …

In this paper, we investigate global and national level datasets, used to estimate trends in
land cover and in land productivity in Ukraine within Land Degradation Neutrality (LDN) …