Emerging trends in Geospatial Data Acquisition
The world’s data needs are consistent and ever-changing. These changes are driven by the development of fascinating trends in technology. Notably, newer tech inventions require application-specific data that makes querying seamless. In the geospatial space, data acquisition is fast-evolving from the use of GPS trackers and total stations to more recent devices and application services that maintain accuracy. From drones and IoT to spatial analytics, there is a rise in geospatial technology being integrated into everyday technology across industries and spectrum. Thus, resource gathering packages with trusted capabilities are required.
Today, the biggest cities in the world rely heavily on GIS data for many decisions; making the accuracy of the data a vital element. Notably, remote sensing has been the core of geospatial data gathering. With high end orbiting satellites, drones, and UAV, it remains an ever-reliable source of data acquisition. However, for more application-specific data, remote sensing is limited in its usage. For instance, an attempt to study the poverty rate in the African space, population metrics amongst others, require demographic and socio-economic information to support the remote sensing data. Hence, developing application packages that specifically consider the terrain of study is a no-brainer.
Smart cities, robotics, and automobiles, self-driving vehicles value location-based information. More so, the users of these applications are primarily fascinated by the types of data derived from in-app features. To attain maximum functionality, accurate data is required for precision purposes. These have driven the GIS space to develop technologies that are capable of collecting purpose-modeled data that fit these applications. This article talks more about a few of the recent trends in geospatial data acquisition.
Recent trends in Geospatial Data Acquisition
Mobile Mapping System (MMS): The technology involves the acquisition of spatial data through motion-imagery sensors. The sensors are mounted on mobile equipment on any terrain. The system automatically generates geo-referenced images of high-resolution. More so, they are capable of providing point data and polygon information with accurate referencing. With the advent of cloud storage, many of these systems are now developed with back-end servers, that store uploaded spatial data concurrently. Mobile Mapping systems can be developed with more application-specific methods that ensure homogeneity to acquire data.
Drones and UAVs: Drones and UAVs have quickly become one of the most innovative tools in GIS data acquisition. More importantly, their usage cuts across the full spectrum of GIS applications. Notably, geospatial technology is the primary backbone of the UAVs, as these devices follow a GPS-guided flight plan in their operations. In the past few years, there has been the development of drones that seamlessly conduct topographic surveys of equal accuracy as the measurements collected by conventional mapping methods, in a shorter time. The UAVs and drones are deployed with downward-facing sensors that are capable of recording the slightest terrain details, migration patterns, and other required data. The highlight of these systems as geospatial data acquisition devices lies in the ability to map beyond the land barriers of traditional methods and the vast reduction in required manpower.
Drones and UAVs are often referred to as the dynamic duo in the GIS space.
Miniaturized Sensors: Small is becoming the new big in the geospatial world. With big tech companies becoming increasingly interested in geospatial trends, there is a need to adapt these GIS technologies into small sensors that can be used in smartphones and laptops. Miniaturized sensors are smart electronic devices that can be incorporated into wearables and devices for data collection. These technologies are evident in smartwatches and fitness trackers. Miniaturized sensors often operate with a LiDAR background and as such capable of collecting real-time 3D images with gesture recognition systems. They have gained massive acceptance in recent years and greater incorporation and development of these sensors is in the pipeline.
