Optical remote sensing instruments accumulate abundant data from across all of the earth's
land surfaces, making it possible both to understand the effects of climate change and to …

Synthetic aperture radar (SAR) image change detection (CD) aims to automatically
recognize changes over the same geographic region by comparing prechange and …

Several factors influence the performance of land change simulation models. One potentially
important factor is land category aggregation, which reduces the number of categories while …

Change detection refers to extract change information using deep learning or traditional
image processing methods to quantitatively analyze and characterize landmark changes on …

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at
microwave frequencies. These wavelengths are sensitive to the dielectric properties and …

In the following, we describe highly-automated image analysis approaches that help us
classify satellite images, and allow us to monitor dynamical changes in image time series …

During the last years, huge quantities of satellite images are available due to the increased
number of Earth observation (EO) sensors. Thanks to this, the acquisition frequency is …

In spatial computing, data-driven systems process vast data but face challenges due to
complex algorithms and growing datasets, necessitating hardware scaling with higher costs …

Les séries chronologiques d'images satellitaires se sont avérées être un outil efficace pour
surveiller la dynamique de la végétation, les ressources et les effets du changement …

The classification of the land-cover like forest, urban, water/ice, farm-land ie crop, oil-slick is
important for controlling deterioration of the environment and destruction of wetland, for …