With the increasing interest in large-scale, high-resolution and real-time geographic
information system (GIS) applications and spatial big data processing, traditional GIS is not …

The rapid growth of satellites orbiting the planet is generating massive amounts of data for
Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms …

Clustering trajectory data is an important way to mine hidden information behind moving
object sampling data, such as understanding trends in movement patterns, gaining high …

Brain data processing has been embracing the big data era driven by the rapid advances of
neuroscience as well as the experimental techniques for recording neuronal activities …

The data processing capability of existing remote sensing system has not kept pace with the
amount of data typically received and need to be processed. Existing product services are …

Density-based spatial clustering of applications with noise (DBSCAN) is a density-based
clustering algorithm that has the characteristics of being able to discover clusters of any …

This book provides the users with quick and easy data acquisition, processing, storage and
product generation services. It describes the entire life cycle of remote sensing data and …

Recently, methods of scene classification that are based on deep learning have become
increasingly mature in remote sensing. However, training an excellent deep learning model …

The mean shift image segmentation algorithm is very computation-intensive. To address the
need to deal with a large number of remote sensing (RS) image segmentations in real-world …

Cloud computing, an important source of computing power for the scientific community,
requires enhanced tools for an efficient use of resources. Current solutions for workflows …