Big Data & AI
Big Data & AI
Technological innovation in recent years has made available various sources of geospatial data, such as satellite and aerial images, optical, radar and point clouds data, elevation models, the data collected by UAV drones, video captured by camera systems and sensors, data acquired from smart phones and mobile devices equipped with GPS technology, etc. .
The availability of geospatial data is really wide, so that it comes to Big Data in the geospatial field, wanting to show large aggregations of data, whose size and complexity requires more advanced tools than the traditional ones, at all stages of the process (from the management, updating, through sharing, analysis, and visualization).
The paradigm of Big Data is based on the three "V": volume, velocity and variety, that is, the size of the data produced, the speed with which they are purchased or upgraded and variety of sources. Translating in the "geospatial field", we can say that the paradigm is valid if we consider the enormous volume of data now available globally, the high frequency with which they are updated through new acquisitions and the variety of platforms and sensors used.
The Big Data opens a new scenario of challenges, but also opportunities. If we look at the challenges, big data problems are related to their complexity.
In addition to size, the main challenge in the use of Big Data is related to the difficulty of properly integrate and process data from completely different sources and different in type, characteristics, resolution, size, etc. .
The Big Data opportunities are tied to their enormous intrinsic informational value. Such an approach allows to obtain information of higher quality and value because they are based on the most comprehensive and integrated analysis.