In recent years, with the increasing number of Earth observation satellites and the
popularization and application of various sensors, remote sensing data have shown a rapid …

The incredible growth in Remote Sensing (RS) data volume, with high spectral-spatial-
temporal resolutions, has been utilized in various application domains. With the rapid …

Massive, large-region coverage, multi-temporal, multi-spectral remote sensing (RS) datasets
are employed widely due to the increasing requirements for accurate and up-to-date …

Geospatial big data plays a major role in the era of big data, as most data today are
inherently spatial, collected with ubiquitous location-aware sensors such as mobile apps …

The incredible increase in the volume of data emerging along with recent technological
developments has made the analysis processes which use traditional approaches more …

As we have entered an era of high resolution earth observation, the RS data are undergoing
an explosive growth. The proliferation of data also give rise to the increasing complexity of …

Multi-area and multi-faceted remote sensing (SAR) datasets are widely used due to the
increasing demand for accurate and up-to-date information on resources and the …

As we have entered the era of “big data,” the capability of Earth observations has been
dramatically increased and reached an unprecedented level by widely accessible remotely …

Currently, remote sensing is widely used in environmental monitoring applications, mostly
air quality mapping and climate change supervision. However, satellite sensors occur …

This article gives a survey of state-of-the-art methods for processing remotely sensed big
data and thoroughly investigates existing parallel implementations on diverse popular high …