Data about the land cover have already been for decades one of the most important sources
for determining human impact on nature and the environment and possible backward effects …

Currently, we can solve a wide range of tasks using computer vision algorithms, which
reduce manual labor and enable rapid analysis of the environment. The remote sensing …

The rapid increase in population accelerates the rate of change of Land use/Land cover
(LULC) in various parts of the world. This phenomenon caused a huge strain for natural …

Urban pluvial flooding (UPF) has emerged as a serious natural hazard, especially in recent
years. Previous research on UPF prediction has mainly focused on hydrological models …

To the best of our knowledge, the impacts of crop residue cover (CRC) on agricultural
productivity and soil fertility have not been studied by previous researchers. In this regard …

Land Use and Land Cover (LULC) classification using remote sensing data is a challenging
problem that has evolved with the update and launch of new satellites in orbit. As new …

Spatiotemporally consistent information on global cropland extent is essential for resource
management and scientific research. Multiple cropland datasets derived from remotely …

Tropical dry forests are highly threatened at a global scale. Long-term monitoring of
remaining stands is needed to assess forest health, efficacy of management practices, and …

Semantic segmentation is a fundamental task in remote sensing image analysis that aims to
classify each pixel in an image into different land use and land cover (LULC) segmentation …

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in
highland mountainous regions. However, inherent geometric distortions in SAR images …