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 Knowledge on geographical location and distribution of crops at global, national
and regional scales is an extremely valuable source of information for many applications …

Accurate and timely crop yield forecasts are critical for making informed agricultural policies
and investments, as well as increasing market efficiency and stability. Earth observation data …

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 …

Automatic semantic segmentation has expected increasing interest for researchers in recent
years on multispectral remote sensing (RS) system. The agriculture supports 58% of the …

Crop classification maps based on high resolution remote sensing data are essential for
supporting sustainable land management. The most challenging problems for their …

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) …

Remote sensing is based on the extraction of data, acquired by satellites or aircrafts, through
multispectral images, that allow their remote analysis and classification. Analysing those …