In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

Wetlands are critical to the ecology because they maintain biodiversity and provide home for
a variety of species. Researching, mapping, and conservation of wetlands is a challenging …

Using deep learning semantic segmentation for land use extraction is the most challenging
problem in medium spatial resolution imagery. This is because of the deep convolution layer …

Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as
global warming, population growth, and environmental pollution. Sustainable monitoring of …

Sun glint contamination is a significant problem for high-resolution remote sensing over
aquatic environments. Sun glint is a particular issue for researchers using aerial imagery to …

In remote sensing along with image interpretation, water body segmentation (WBS) is a
significant problem. Over the course of a long period of time, the researchers have been …

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to
draw sea-ice maps that reflect the geographical distribution of different types of sea ice …

As the lakes located in the Qinghai-Tibet Plateau are important carriers of water resources in
Asia, dynamic changes to these lakes intuitively reflect the climate and water resource …

The classification of objects that are present in the images or in the videos is being
developed progressively to obtain good results because of the use of convolutional …

The primary focus of the method, developed here is on classifying soil types from satellite
images with deep convolutional neural network. As we know there exist different types of soil …