Geospatial Encyclopedia

Digital video consists of a sequence of images and often comes bundled with audio data in most file formats as well. Many video formats don’t store each individual frame in it’s entirety, instead periodically using ‘I-frames’ with higher fidelity images that are joined together with ‘P-frames’ approximating the changes between each successive frame in the video. This is advantageous for minimising data storage size however can impact to fidelity of each frame in the video. Video can be visually engaging for content such as promotional and marketing videos however due to the previously mentioned limitations of some file formats and capture methods, extracting frames from video to generate derivative datasets from processes such as photogrammetry isn’t as ideal as using source imagery.

Panoramic video is a type of video where each frame is made up of panoramic images. Live action panoramic video can be created by using omnidirectional cameras to capture multiple perspectives at once and stitching each frame together. Rendering virtual environments can be another method to generate panoramic video. Video players that support panoramic video, including VR headsets, often allow the viewer to control the direction of the camera as the video plays.

As the name suggests, 3D point clouds are made up of many individual points in 3D space. These points can contain a variety of properties/attributes and can be coloured to represent the physical assets they represent or other information in their attributes such as the points elevation or classification (the type of object it is a part of). Point clouds can be generated directly from equipment such as lidar scanners or as a derivative dataset from processes such as photogrammetry

A Digital Elevation Model (DEM) is a digital representation of the terrain surface defined by a set of points relative to a 2D reference surface. It approximates a part or the whole of the continuous terrain surface using a set of discrete points for any given horizontal location. Heights are vertical distances between terrain points and some reference surface, such as mean sea level, geoid, and ellipsoid, or local geodetic datum. The 2D points are typically given as geodetic coordinates (latitude and longitude) or planar coordinates (North and East values). DEMs usually assign a single unique height value to each 2D point, so they cannot describe vertical terrain features with overhangs (e.g., cliffs). DEMs are therefore “2.5D” rather than truly 3D models of the terrain. Due to every x coordinate only having a single associated elevation, these models can be represented in both raster and vector formats. A closely related term is Digital Surface Model (DSM), which is sometimes used synonymously with DEM, but often as an umbrella term to describe both DTM and DSM

A Digital Terrain Model (DTM) is a type of Digital Elevation Model (DEM) where each point represents the approximation of the bare ground of the terrain. While the elevation of bare ground is easily determined for features such as open patches of soil, some filtering and constraints are required to model the assumed bare earth of areas where buildings and other complex geographic features such as cliffs exist.

Geospatial control is a special form of geospatial geometry that ties the location of a datasets geospatial geometry to its counterpart. Control points are objects or entities with information considered to be the “truth” that serve as references in different ways. They are used to accurately georeference and calibrate images, and to align digital maps or other geospatial datasets with real-world recorded measurements. Checkpoints, on the other hand, are not used for processing and calibrating models or datasets, but rather to assess the accuracy of a dataset after its generation by comparing its true predetermined locations with the associated geometry in the dataset.

Point cloud registration is the process of spatially aligning multiple structured point clouds with one another. The source point clouds can come directly from lidar scanning equipment or as derivative datasets from photogrammetric processing. Using lidar and photogrammetric point clouds together can leverage the benefits of both technologies within a single dataset. Typically, terrestrial lidar scanners will produce one point cloud for each setup location of the scanner which will then need to be joined with the other scan perspectives of an asset. There are some lidar scanners that use SLAM (simultaneous localisation and mapping) to continuously capture and align lidar points during capture. All capture methods still require optimisation, cleaning and calibration with geospatial control during post processing to get the best quality point clouds.

A wide range of industries and niches can benifit from geospatial data, each with its own set of relevant and useful analysis. At the core of each of these analysis are similar geospatial measurements and methodologies. For example, volumetric measurements of features within a dataset related to earthwork could be used for stockpile or cut and fill reporting. The same volumetric measurement methodologies could be applied to built infrastructure for machinery clearance analysis.