Geospatial Analysis


Interpret mapping data to help you visualize, question, and analyze patterns and trends by utilizing special purpose UAS equipment.

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Applications for Drones

  •   Remote sensing and photogrammetry
  •   Earthquake surveying and mapping
  •   Storm and hurricane mapping
  •   Tidal mapping
  •   Industrial zone mapping
  •   Excavation site mapping
  •   Surveying and mapping of floods
  •   Surveying and mapping of landslides
  •   Vegetation mapping
  •   Geographic Information System (GIS)
  •   Geophysical mapping and investigation
  •   Coastal area mapping
  •   Infrastructure 3D mapping
  •   Volcanic eruption 3D mapping

There is a big data angle to drones, especially in the context of location intelligence and geographic information systems (GIS). Drones are emerging as a terrific way to gather image data from the air.

An UAS can capture images with resolutions down to one inch and deliver that data within hours, compared to the days typically required by manned aircraft.



GIS Professionals Work More Efficiently

Remote sensing from a drone is performed much closer to the earth with precise flight paths for increased accuracy and high-resolution data. Drones are frequently used in the geospatial field because they are fast to deploy, affordable, and can produce high quality datasets.

With a mapping drone you can capture accurate aerial imagery and transform it into 2D orthomosaic maps and 3D models of sites. Due to the altitudes at which drones fly, cloud cover is not an issue, meaning fewer weather delays and less unusable imagery.



Data Processing Methods for 3D GIS

Data quality depends on the equipment in the payload, and improves by using a gimbal, which is a stabilizing mechanism. LiDAR data generally needs to be processed before it is useful to collectors. Here are some typical data processes:

  • LiDAR to 3D: Remote sensing technology that measures distance by analyzing the reflected light from a laser. LiDAR data has X, Y, and Z information associated with every point and can be visualized in 3D.
  • Stereo to 3D: Dense redundant images of one area from multiple locations can be processed to create a 3D point cloud. 3D point cloud clusters can be generated from both LiDAR and images processed through a stereo method.
  • 3D point cloud clusters: Large datasets composed of many points defined by X, Y, and Z coordinates, which are intended to represent the three dimensional surface of an object.

Whatever field you work in – forestry, asset management, environmental protection, agriculture, humanitarian, remote sensing – drones can provide very real benefits, providing accurate data, quickly and cost-effectively.

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