Big Data and Intelligence

¶ … 2003, when the National Geospatial-Intelligence Agency (NGA) was instituted, the concept of geospatial intelligence (GEOINT) was largely at its outset. Its beginnings had been propagated in preceding decades, but the circumstances were finally prime for the discipline to develop, grow and spring new branches with the ability to support a growing community practicing a verified dexterity.[footnoteRef:1] In delineation, geospatial intelligence takes into account taking advantage of and analyzing images and geospatial information to outline, appraise, and visually portray physical features and geographically mentioned activities on Earth.

This is purposed to distinguish the important property of geographical position linked with the data that the National Geospatial Intelligence Agency and the intelligence community generate and analyze.

It is also purposed to lay emphasis on the value-added examinations that the NGA undertakes to generate a specific kind of actionable intelligence.[footnoteRef:2] Taking into consideration that the purpose of geospatial intelligence is to pinpoint and track down anything and any individual on the planet, collecting and examining GEOINT sources and generating prompt, precise, pertinent and actionable intelligence necessitates strong global partnerships, an aspect that has grown in recent periods.

A decade later, GEOINT is in complete blossom.[footnoteRef:3] The progresses in the past decade are considerably outstanding and that is what makes the next decade exceedingly significant. This paper will examine the biggest challenges or limitations to the future of GEOINT in the next decade. [1: Mapping Science Committee. New research directions for the national geospatial-intelligence agency. National Academies Press, 2010.] [2: Flint, Colin. Introduction to geopolitics. Routledge, 2016.] [3: Alderton, Matt.

"The Defining Decade of GEOINT." Trajectory, 2014.] Accomplishing Constant TPED One of the problems or challenges that GEOINT will face in the next decade encompasses accomplishing constant TPED, which includes tasking, processing, exploitation and dissemination. In the contemporary, unrelenting TPED of geospatial intelligence over geographic space and time is fundamental and significant. Nonetheless, prevailing sensor networks through aircrafts and satellites together with database management systems are insufficient to accomplish persistent TPED for numerous reasons.

To begin with, prevailing senor networks were purposed for tracking fixed and permanent targets such as buildings and also the equipment for the military.[footnoteRef:4] They are largely meager in both space and time and it is also time-consuming to shift the sensors in order for them to focus and lay emphasis on the sought after geographic expanse connected to the pertinent time interval.

Moreover, even if a suitable network were utilized, the prevailing databases fail to measure up to the fundamentally greater rates of data and volumes of data that are created by set out sensor arrangements. In a nutshell, the key and significant challenges in accomplishing TPED take into account the efficacious use of sensor networks, spatiotemporal data mining and discovery, and spatiotemporal database management and systems.[footnoteRef:5] [4: Perdikaris, John. Physical Security and Environmental Protection. CRC Press, 2014.] [5: National Academies of Sciences, Engineering, and Medicine.

"Priorities for GEOINT Research at the National Geospatial-Intelligence Agency." (2006).] Compressing Timelines Another important challenge takes into account compressing time lines, purposed for the preparation and dissemination of intelligence. In particular, the timeliness of geospatial intelligence is progressively growing into a significant aspect owing to the rising numbers of mobile targets, among other things. Therefore, the field of geospatial intelligence is making a changeover and evolving from planned targeting to time-sensitive targeting.

In particular, it is becoming progressively more significant to shift toward instantaneous data creation, processing, and distribution to diminish dormancy and underdevelopment in intelligence generation and delivery practices. Nonetheless, the customary geospatial intelligence creation practice is largely dependent on manual explanation of data collected from geospatial sensors and sources. As a result, this is an instantaneous problem and difficulty in GEOINT taking into consideration the rising volume of data from geospatial sensors.

Another significant challenge that will be faced in GEOINT in the next decade will be to quickly and effectively ascertain the practices contained within the intelligence cycle that are most appropriate for computerized processing, which are suitable for human cognition, and which necessitated a mixture of both human and machine backing so as to reduce the GEOINT timeline.

This is imperative for not only prevailing but also future systems.4 Interoperability Interoperability, which encompasses sharing with forces, external partners, and communities on the whole, will be a key challenge for geospatial intelligence in the next decade. This is largely with regard to the fact that the NGA continues to undertake its objective of sharing geospatial intelligence not just with other organizations in the United States, but also with alliance partners and foreign partners. A fitting example encompasses an interchange of triangulation maps amongst coalition forces.

In this case, a source might lay emphasis on landscape maps, where every direction segment is maneuverable by land vehicle, conceivably for the reason that it fundamentally serves missions for the military. Maps from another source may consist of land in addition to direction segments that are based on water for amphibious means of transportation, conceivably because they serve the Marines.

Bearing this in mind, if the maps emanating from the two different sources are combined devoid of taking into consideration the dissimilarities in semantic implications, it can give rise to land vehicles misplacing outlet in the course of combats or falling into profound water bodies. Another major challenge comes in the form of having accurate tracking of moving targets in the instances where geopositions are recorded by two varying sources that employ incongruent coordinate systems, data file designs, and map codes.

Moreover, significant problems to be experienced in spatiotemporal interoperability takes into account the role of instantaneous sensor inputs, the issues of coping with inadequate and scarce data, dissimilar ontologies, and ambiguity management, moving targets, and fluctuating profiles in time and space.3 Exploiting all Forms of Intelligence In the next decade there will be extensive changes most of all because of the progressive level of technology. Bearing this in mind, geospatial intelligence will face the challenge of exploiting all forms of intelligence.

In the contemporary world, subsequent to the 9/11 events, the National Geospatial-Intelligence Agency is required to take advantage of all kinds of intelligence to impede and prevent repudiation and deception, be able to track moving targets, and also have accuracy during such targeting. Initially, in geospatial intelligence, this implied synthesis across images, maps, and sensor data. In the present day, this is progressing to encompass fusion through all kinds of intelligence.

Fusion incorporates and encompasses several disciplines, taking into account geographic information science, computer science, physics, spatial statistics, electrical engineering as well as remote sensing. Progressively, this implies employing an interdisciplinary method, particularly as new sources of data, for instance social network data, are assimilated to prevailing sources of data. To begin with, fusion across sources of images alone continues to be a major problem. For instance, individual remote sensing devices are restricted in preventing denial and deception through utilization of facilities below the surface, for instance caves and foxholes.

They are also restricted in their capacity to trace moving objects that are undercover.3 Secondly, prevailing comprehension of multisensory intelligence assimilation is a long way from comprehensive and complete. Therefore, there is a need for future research to cultivate and improve cutting-edge algorithms for relative models that suitably assimilate and examine information from various sensors. Customarily, the NGA has depended upon fusing capacities and dimensions from uniformed and synchronized collections of sensors.

Nonetheless, the information fusion approaches presently used are restricted in their capacity to incorporate information from assortments of sensors that might be varied. In the next decade, it is expected that information fusion approaches and methods will combine dimensions from mixed groups of sensors that are coordinated in time and space.

The main problems that will be faced encompasses the cultivation and advancement of cross-sensor signs of targets to enhance the dependability of target discovery.3 Big Data In the next decade GEOINT will face a significant challenge in the form of big data, which takes into account volume, variety, veracity, and velocity.

In particular, geospatial intelligence is deemed to be the definitive big-data situation where all of these difficulties are challenged and faced head on, with massive volumes of data taking place in several platforms and devices, high-streaming degrees mixed with swift data aging and uniformity of data sets with dissimilar layouts, arrangements, and quality. Data exploitation is a prevailing reality and is expected to grow and expand in the future periods.

Taking into account the massive volume and the sort of data that is being created in the present day, the main difficulty that will be faced in the next ten years encompasses shifting from network centricity to data centricity. Essentially, in the past decade, operations in geospatial intelligence have significantly transformed. Therefore, in the next decade, there will be a huge need for technology that will provide better insight and comprehension of a state of affairs and therefore improve the level of intelligence.

All of this climaxes in the main challenge of making prompt decisions regarding the suitability of data to gain value for efficacious decision making. Moreover, the nature of big data makes it difficult and unbearable for analysts to make decisions regarding the effectiveness of data autonomously. A high magnitude of computerization is necessitated to cast-off data most pertinent to the undertaking at hand and subject it to analytics.[footnoteRef:6] [6: Buxbaum, Peter.

"Geospatial's Big Data Challenge." Intelligence Geospatial Forum, 2015.] The challenge presented in terms of velocity of data is faced in two fronts. To begin with, there is the difficulty of the inward bound data speeds, which makes it problematic in capturing data in a timely manner. The second problem takes into account the outgoing of data. In particular data users aspire to have the capacity to make the most of the data in favor of decision making in real time or close to that.

It is imperative to apply novel technologies in order to achieve this and there is also need for alterations in intelligence tradecraft. Moreover, in the forthcoming years, there is expected to be a progressive increase in the streaming in of geospatial data. In addition, data users want to have the ability to employ such streams of data instantaneously.

This is particularly spot-on with regard to feeds from social media, which are more often than not geo and time-recorded and have demonstrated to be a cradle and basis for gradually more valued intelligence. Another major challenge in this regard is the amalgamation of geospatial data with other kinds of data.