- Multi-sensor GIS Data Collection
- Laser, Radar, LiDAR Scanning
- Mobile Mapping; Geo-Located Sensors; Crowd-sourced
- Data Collection; GNSS Tracking
- Data Mining, Data Visualization and Pattern Recognition
Did you know…
INNOVIM scientists and engineers have been working with Geospatial data for decades. These data are traditionally collected using ground surveying, photogrammetry and remote sensing, and more recently through laser, radar and LiDAR scanning, mobile mapping, geo-located sensors, geo-tagged web contents, volunteer geographic information (VGI), global navigation satellite system (GNSS) tracking and other
Geospatial big data is characterized by the following:
- Volume: Petabyte archives for remotely sensed imagery data, ever increasing volume of real time sensor observations and location-based social media data.
- Variety: map data, imagery data, geotagged text data, structured and unstructured data, raster and vector data, all these different types of data – many with complex structures.
- Velocity: imagery data with frequent revisits at high resolution, continuous streaming of sensor observations, Internet of Things (IoT), real-time GNSS trajectory and social media data all require matching the speed of data generation and the speed of data
processing to meet demand.
- Veracity: Level of accuracy varies depending on data sources, raising issues on quality assessment of source data and how to “statistically” improve the quality of analysis results.
- Visualization: Provides valuable procedures to impose human thinking into big data analysis. Visualizations help analysts identifying patterns (such as outliers and clusters), leading to new hypotheses as well as efficient ways to partition the data for further computational analysis. Visualizations also help end users to better grasp and communicate dominant patterns and relationships that emerge from the big data analysis.
- Visibility: The emergence of cloud computing and cloud storage has made it possible to now efficiently access and process geospatial big data in ways that were not previously possible.
INNOVIM brings years of experience supporting NASA, NOAA and USGS in the state-of-the-art methodological, theoretical, and technical development of end-to-end data processing, complex modelling, data mining and analyzing, pattern recognition, and visualizing geospatial big data.