Geospatial technology, also known as geomatics, is a multi-disciplinary field that includes surveying, Geographic Information System (GIS), Global Navigation Satellite System (GNSS), mapping, remote sensing, photogrammetry and geodesy. Today, the diverse applications of geomatics span security and intelligence, automated mapping, environmental management, telecommunication business, utilities, precision farming, video surveillance cameras, and RFID (radio frequency identification) tags. This highlights the inter-linked dependencies of geomatics upon communication and information technologies.
With over 20 countries owning remote sensing satellites, it is now feasible for the users to access high-resolution satellite images. OrbView3 data, United States Geological Survey (USGS) free Landsat archive, Global Land Cover Facility (GLCF), and geospatial portals like Microsoft Virtual Earth and Google Earth, are someof the image programmes which are available free of cost today. This has been possible due to the advances in technology in respect of photogrammetry and remote sensing. The resolution of images being offered today is 0.5m, but, with launch of GeoEye third-generation commercial earth observation satellite, by mid-2016, images of a resolution of 31cm would be commercially available. Due to the grant of recent approvals, it will not be restricted by the existing 50cm resolution restrictions. The imaging sensor is manufactured by ITT Exelis, and the satellite is owned by DigitalGlobe, post the merger of GeoEye and DigitalGlobe. With GeoEye-2 joining GeoEye-1 and IKONOS, its customers would have the facility of daily revisits. It need not be emphasised that it would provide a boost to applications like 3D visualisation, disaster management, feature extraction & analysis, and infrastructure management.
A key area of remote sensing is the extraction of information and features -buildings, roads, Digital Elevation Model data, etc — from high-resolution imagery. As per USGS, LiDAR (Light Detection and Ranging) is a remote sensing technology that collects 3-dimensional point clouds of the Earth’s surface. This technology is being used for a wide range of applications including high-resolution topographic mapping and 3-dimensional surface modelling, as well as infrastructure and biomass studies. Airborne LiDAR instrumentation uses a laser scanner with up to 400,000 pulses of light per second. The laser transmits pulses and records the time delay between a light pulse transmission and reception to calculate elevation values. These values are integrated with information from the aircraft’s Global Positioning System (GPS) and orientation (pitch, roll, and yaw) data from inertial measurement technology to produce point cloud data. Each data point is recorded with precise horizontal position, vertical elevation, and other attribute values. Point cloud data represents the elevation of landscape features, including crops, forests, roads, railways, airports, bare earth, mountains, valleys, lakes, rivers, glaciers, buildings, and other urban development.
In surveying, most of the data collected today is in digital format, which is compatible with geospatial formats. The survey data can also be digitally streamed to other users. Point clouds are created using 3D laser scanners, which contain multitudes of accurate survey points. This point cloud is then processed to provide visualisation of 3D structures. GNSS is central to geospatial technology. It is also the backbone of collection of data and imagery using unmanned systems. With the availability of GLONASS, Beidou, GALILEO, QZSS, and Indian Regional Navigation Satellite System (IRNSS) enhanced accuracies would be commonly available.
Geographic Information System (GIS)
GIS has evolved from desktop to distributed GIS. Which is further evolving from mobile GIS to Web GIS, and now into cloud GIS. Developments in information and digital technologies have accelerated Web distribution and visualisation of geospatial information. Volunteered geographic information (VGI), ambient geographic information (AGI), and Map mashups are being used to share geo-information on the Web. This has become an invaluable resource for geospatial intelligence, real time data collection, analysis of mobile call records and disaster relief, etc. The 3D visualisation using GIS has been evolving with high-resolution imagery. It is now headed toward 4D visualisation by incorporating time as the fourth dimension
A vibrant example detailing many of the aspects of geomatics covered above was observed during the recent Nepal earthquake. Organisations like Humanitarian OpenStreetMap (HOT), the Standby Task Force (SBTF) and others from the Digital Humanitarian Network (DHN) were deployed in response to the tragedy. The purpose of DHN is to leverage digital networks in support of 21st century humanitarian response. At the request of the UN Office for the Coordination of Humanitarian Affairs (OCHA), the SBTF is using the MicroMappers platform of Qatar Computing Research Institute (QCRI) to crowd source the analysis of tweets and mainstream media to assess disaster damage and needs; and to identify where humanitarian groups are deploying. The MicroMappers Crisis Maps were live and publicly available. The Humanitarian UAV Network (UAViators) was also activated to identify, mobilise, and coordinate UAV assets and teams. Several professional UAV teams were in Kathmandu.
The UAV pilots produced high-resolution imagery, oblique imagery, and 3D point clouds. UAViators pushed this imagery to both HOT and MicroMappers for rapid crowd-sourced analysis. DigitalGlobe, Planet Labs and Skybox shared their satellite imagery with CrisisMappers, HOT and others in the DHN. The core contributing disciplines of GIS comprise: geography, which contributes to geospatial technologies by providing methods of analysis and ways to consider and solve geographical problems; cartography, which deals with the production and study of maps and charts; statistics, since most of the data are represented as numbers, and many of the queries and analysis rely on statistical techniques; and databases and data structures, which are critical to the storage and manipulation of geographic information. This is important because voluminous databases (big data) need to be designed specifically for dealing with spatial queries, spatial indexing, and other specific capabilities that are required to manage geographic information.
Further, GIS requires the use of specific geometric data and, therefore, store the information either as points, lines and area objects (vector data format) or as a grid of values (raster format). Programming is also inherent to GIS since the user needs to consider what they are trying to achieve in their analysis task, and then string together a series of actions to achieve the needed outcome.
The rapid advances in various engineering disciplines have enabled availability of wireless communication networks; low-power, short-range radio- based communication networks; miniaturised computing and storage platforms running on battery power for months at a time; micro sensors and novel sensor materials; and lastly, real time data delivery. These have enabled development of intelligent and adaptive sensor platforms, which can be fed real time data via the Internet for various applications. Sensors smaller then .001mm attached to micro-electromechanical (MEMS) sensors nodes as small as 1mm3 are available today. They are made out of silicon, polymers, or metals such as gold, titanium, or platinum. The micro sensors use standard interfaces to attach to MEMS computing devices. These can be operated as a collaborative– networked system. Wireless Sensor Networks (WSN) are a group of MEMS devices running on batteries with short-range communication links. They are un-tethered and have very small processing power. Each may have many micro sensors (‘pixie dust’). Currently, there is a thrust on the adaptation of such WSN for applications, such as geosensor networks (GSN). However, this has led to challenges in development of algorithms for collaborative event processing, spatial computation, in-network analysis and so on. Work on standardised sensor service interfaces is underway to create a web of real-time sensors that are accessible and sharable in a uniform way. Commercialisation of such GSNs is being undertaken by Dust Networks (‘Smart Dust’). A software system called TinyOS has been developed which has a very low memory footprint, and is available as open source software. Interestingly, since GIS involves diverse applications like continuous monitoring, real-time event detection and mobile sensor nodes for tracking of movement, the microminiaturised sensor nodes are not likely to replace the mini, midi or large sized sensor nodes. The selection of the preferred platform would depend upon the activity to be observed, and it is likely that a combination of such devices may have to be deployed.
GIS is a critical component of geospatial intelligence (GEOINT) required by national security and military. Thisis the intelligence about the human activity derived from the exploitation and analysis of imagery and geospatial information. GEOINT describes, assesses, and visually depicts physical features and geographically-referenced activities. Geospatial intelligence can simply be defined as, data, information, and knowledge gathered about adversaries that can be referenced to a particular location on, above, or below the earth’s surface. The basic information that is required by any commander in today’s networked warfare is accurate position of his own units, location of the enemy and his reserves, location of supporting units and placing of other miscellaneous assets. With this knowledge, a commander today can effectively carry out his mission by optimally utilising the available firepower and resources. Thus, ‘situational awareness’ is crucial to any mission, it comprises tasking, collection, processing, exploitation, and dissemination. Rapid technological advances in sensor based, smart, and networked combat systems is pushing the military to adopt commercially available emerging technologies and adapt them for its use. To understand and react to real time tactical situations, commanders have to manage and control big data environment comprising of, historical or point-intime data, transactional data, optimised data for inquiry, unpredictable pattern of data, and ad-hoc use of the system. The military has been collecting data at humongous levels since the induction of unmanned vehicles with sensors. Thus, major issues faced by the military today involve availability of ever-increasing volumes of sensor data from integral sources like UAVs and other national assets. Providing a comprehensive situational awareness is dependent upon the accuracy and integration of data received from multiple types of sensors, as well as from intelligence sources. The screens and software tools do not have interoperability as of now. Due to security considerations, ISR data from different sources is stored in different locations with varying access levels. This leads to incomplete analysis. Single network domain providing access to data at multiple levels of security classification is not yet available. Analysts currently spend only 20% of their time looking at correct data, whereas 80% of the time is spent looking for the correct data. Some of the companies working in this field with the US military to provide a common operating picture are Modus Operandi, Palantir Technologies, SAP’s Hana platform, Oracle, Teradata, Leidos, and SYNTASA.
GIS today is rapidly moving towards an overarching use through applications, such as location-based services (LBS). LBS centers on the delivery of data and information services, tailored to the location and context of a mobile user. LBS is, thus, convergence of GIS, spatial databases, and the Internet. This has put more emphasis on interaction, usability, mobility, and portability. It also implies usability of GIS in more mobile and diverse situations and for an ever-expanding range of applications in the real geographic environment and in real time. It has also introduced requirements for further research into spatial cognition, which is central to gaining insights into how users of ubiquitous geospatial technologies are going to be able to interact with them. Mobile devices allow obtaining of geographical information at any location in real time but their size limits the amount of information which can be displayed or communicated. Design guidelines are still not available regarding a person’s spatial awareness and cognitive abilities with respect to use of GIS. Lastly, intense research today is focused on the concept of ‘Sensor Web’, which is defined as an infrastructure which enables an interoperable usage of sensor resources by enabling their discovery, access, tasking, as well as eventing, and alerting within the Sensor Web in a standardised way. This makes Sensor Web as powerful with respect to sensor resources as the World Wide Web is for information. Sensor Web is based upon the sensor web enablement (SWE) initiative, which lays down specifications, and, in turn, provides the functionality to integrate sensors into Spatial Data Infrastructures (SDI). This enables coupling available sensor data with other resources like maps, raster and vector data. There is no better way to conclude this article then to quote Ed Parsons, Geospatial Technologist at Google — ‘The fact that now any individual using the Web can produce a map, publish it, and potentially reach an audience of millions is truly ground-breaking.’