Geospatial Technology Trends To Keep An Eye On In 2022

More sensors now incorporate both lidar and photogrammetry, enabling time savings associated with the capacity to capture both modes in a single trip and the ability to produce colorized point clouds and other valuable outputs.

FREMONT, CA: Lidar, GIS, and other mapping and geospatial technologies are increasing and maturing in the context of a second location revolution. Mapping has come a long way through gaining a better grasp of the environment, more precisely recording real-world situations, and improving data processing.

Common data environments and the cloud: Lidar has a long history of innovation, and each year brings new sensor advancements, new scanning techniques, and new pattern recognition capabilities. What has lagged are innovative workflows for improving data collecting and reducing the time and labor costs associated with data collection.

Like many other complicated and large-scale datasets, Lidar processing is ripe for disruptive innovation. Distributed computing, cloud computing, and shared data environments are at the forefront of computer processing. Thus, the next stage appears to be for the geospatial industry to determine the most effective method to exploit these new technologies for their gain. Process data on the cloud to improve scalability and eliminate reliance on a single piece of hardware at the office. Costs can also be managed on an as-needed basis with on-demand processing models rather than paying for expensive single-seat user licenses.

While a few businesses have specialized in the cloud-based processing of point clouds and other geospatial data, the opportunity for optimizing processing workflows remains.

Hybrid sensors, hybrid methods, hybrid workflows:  While both imagery and lidar scans have value, the increasing number of methods in which these two data streams are collected and combined implies that geospatial information has even more possibilities. Hybrid is the year 2022's buzzword, and it's only getting started.

More sensors now incorporate both lidar and photogrammetry, enabling time savings associated with the capacity to capture both modes in a single trip and the ability to produce colorized point clouds and other valuable outputs. In addition to systems that incorporate both lidar and passive imaging, different sensor combinations can also be used to create a hybrid workflow. Combinations of topobathymetric lidar, multi-spectral sensors, oblique cameras, and other specialized lidars can obtain the best data in the shortest amount of time.

Additionally, when aerial images and data are acquired concurrently, image and data processing can be enhanced, as the aerial image data generates the point cloud, digital surface models, and 3D meshes that the processes output. Lidar can assist in correcting for narrow conditions where occlusion is a concern, as the lasers can penetrate more deeply and farther than images can.

From the manufacturer's perspective, sensor manufacturers such as RIEGL are fully committed to the hybrid approach. Many are developing adaptable sensors that can be used for various applications, including coastal mapping and forestry management.