56.Cutting Edge Engineering for Modern Geospatial Systems
“The Archaeological Survey of India has tied up with Google to put 100 major Indian monuments and sites on its immersive visual walkthroughs”
Geospatial technology (also known as geomatics) is a multidisciplinary field that includes surveying, geographic information systems (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 interlinked 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, USGS free landsat archive, Global Land Cover Facility (GLCF) and geospatial portals like Microsoft Virtual Earth & Google Earth, are some of the image programs which are available free of cost today. This has been possible due to advances in technology in respect of photogrammetry and remote sensing. The resolution of images being offered today is of 0.5m but with launch of GeoEye-2 third generation commercial earth observation satellite, in mid 2016 images of a resolution of 31cm would be commercially available. Due to 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 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 emphasized that it would provide a boost to applications like 3D visualization, 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 the high-resolution imagery. As per US geological survey, 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 modeling 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 represent 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 are 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 visualization of 3D structures. Global Navigation Satellite System 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) enhance accuracies would be commonly available.
Geographic information system, (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 visualization 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 visualization using GIS has been evolving with high-resolution imagery. It is now headed towards 4D visualization by incorporating time as the fourth dimension.
A vibrant example covering many of the aspects of geomatics covered above was observed during the very recent Nepal earthquake. Organizations like Humanitarian OpenStreetMap (HOT) [The Humanitarian OpenStreetMap Team (HOT) coordinates the creation, production and distribution of free mapping resources to support humanitarian relief efforts in many places around the world], The Standby Task Force (SBTF), and others from the Digital Humanitarian Network (DHN) have also deployed in response to the tragedy. The purpose of the Digital Humanitarian Network (DHNetwork) 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 Qatar Computing Research Institute, QCRI’s MicroMappers platform to crowdsource the analysis of tweets and mainstream media to assess disaster damage & needs; and to Identify where humanitarian groups are deploying. The Micro Mappers CrisisMaps were live and publicly available. The Humanitarian UAV Network (UAViators) was also activated to identify, mobilize, and coordinate UAV assets & 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 crowdsourced analysis. DigitalGlobe, Planet Labs and Sky Box shared their satellite imagery with CrisisMappers, HOT and others in the Digital Humanitarian Network.
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; 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 require 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; miniaturized 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 feed real time data via the internet for various applications. Sensors smaller then .001mm attached to 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 thrust on adaption of such wireless sensor networks for applications 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 standardized sensor service interfaces is underway to create a web of real-time sensors that are accessible and sharable in a uniform way. Commercialization 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, mobile sensor nodes for tracking of movement, the microminiaturized 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 the Geospatial intelligence, (GEOINT) required by national security and military. This is 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 be 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 utilizing 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-in-time data, transactional data, optimized 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 UAV’s 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 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 percent of their time looking at correct data, whereas 80 percent 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.
“The technology would become so pervasive that it becomes a part of you — it will be built in your glasses, on your phone and in your ears.”
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 standardized 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 provide 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.