Like many catch phrases, the concept of “Big Data” comes with multiple definitions. From the GIS viewpoint, big data describes data sets that are so large—both in volume and complexity—that they require advanced tools and skills for management, processing and analysis. Such huge data sets can be a lot of work, but the extra effort pays off substantially. Geospatial big data provides detail and contextual information that provides immediate and long-term value across multiple disciplines and applications.
Geospatial big data can include information from an assortment of sensors and data collection methods. Points and features with their associated attributes can be gathered using handheld or survey-grade GNSS, dedicated field computers or even smartphones. These data sets are small compared to other techniques, but they provide very high levels of precision and detail and can be updated rapidly. Mobile mapping systems combine lidar, imaging, GNSS and other sensors to capture large quantities of 3D information. The data is then fused to develop comprehensive models and databases. Data collected from airborne and satellite platforms range from imagery and lidar to multi-spectral remote sensing.