Page 1 of 21
European Journal of Applied Sciences – Vol. 13, No. 1
Publication Date: February 25, 2025
DOI:10.14738/aivp.131.18305.
Dukiya, J. J. (2025). Application of Remote Sensing and Geographic Information System in Counter Terrorism in Sub Saharan Africa.
European Journal of Applied Sciences, Vol - 13(1). 376-396.
Services for Science and Education – United Kingdom
Application of Remote Sensing and Geographic Information
System in Counter Terrorism in Sub Saharan Africa
Jaiye Jehoshaphat Dukiya
Department of Transport Management Technology,
Federal University of Technology, Minna
ABSTRACT
That Sub-Saharan Africa is threshing floor for human blood is not an overstatement.
Terrorism of divers nomenclature is everywhere, and they seem to be operating
with impunity under a failed state. This review paper focuses on the capability of
remote sensing and GIS in counter terrorism in Sub Saharan Africa with the
epicentres at Gulf of Guinea and the Horn of Africa (Somali). To this end, the paper
reviewed case studies in use of multi-temporal HSR-5M satellite imagery and
ARCGIS that carry out area surveillance of elected area. While SASTRA resource
search engine was used to access secondary data on Big Data, remote sensing, and
GIS applications in security. The review finds out that Big-Data in GIS environment
and sensors bearing Unmanned Area Vehicle (UAV), and natural birds are veritable
tools for modern enemies’ surveillance and logistic supply for troops. The paper
therefore recommended that all the African countries should invest more in the
security agencies capacity development, remote sensing and GIS hardware and
software for effective territorial security and defence.
Keywords: Big Data, GIS, Remote Sensing, Sub Sahara Africa, Terrorist, UAV.
INTRODUCTION
The continuous rise in terrorism trend is one of the greatest challenges faced in the 21st century
in the world. Cities and villages in particular across developed and developing countries have
had their fair share of the attack in various degrees. Unfortunately, African countries are
becoming the host of rebels and religious fanatics that has bred a large scale of humanitarian
crisis ranging from loss of lives and properties, and Internally Displaced Persons (IDPs) at large
scale. According to Chou (2017) 3.6 million people have lost their lives in the Sub Saharan Africa
due to terrorism and Somalia and Nigeria top the list. In fact, in the global report on Internal
Displaced persons (IDP) of 2018, Sub Saharan Africa recorded about 5.5 million victims as a
result of the Al-Shabab, Boko Haram and the subsets of Al-Qaeda and ISIS armed groups
activities in the region (Feldstein, 2018).
Twenty years after the September 11, 2001 series of airline hijackings and suicide attacks; by
19 militants associated with the al-Qaeda Islamic extremist group targets the world trade
centre in the United States, the global jihad terrorist groups are expanding their war of terror
in large portions of different continent. Insurgency and terrorism in all its forms have
persistently arouse regional rejoinder co operations to eliminate all its menaces. For example,
EU member states adopted the regional coalition approach in the form of Counter-Terrorism
and Counter Insurgency (CT-COIN). Also around the Lake Chard Basin, the Multi-National Joint
Task Force (MNJTF) was revitalized to crush cross-border terrorism (Alexandre, 2021; Vanda
Page 2 of 21
377
Dukiya, J. J. (2025). Application of Remote Sensing and Geographic Information System in Counter Terrorism in Sub Saharan Africa. European Journal
of Applied Sciences, Vol - 13(1). 376-396.
URL: http://dx.doi.org/10.14738/aivp.131.18305
et al, 2017). Although, this same task force according to Bashir and Usman (2021). has been
undermined by the regional negative peculiarities.
Problem and Background of the Study
The issues of terrorism in Sub Sahara Africa have been on the increase more than a decade. In
fact, from the Armed Conflict Location and Event Data Project (ACLED) record, from the year
2010, about 381 attacks that led to 1,394 fatalities occurred involving civilians, and by the year
2020, the figure rose to 7,108 attacks and 12519 fatalities (Mroszczyk.and Abrahms, 2021). In
the global report of Verisk Maplecroft, seven out of the top ten terrorist counties are in Africa
(Raleigh, et al 2010; Brown 2010).
Big Data is currently seen as a panacea to effective crime control globally. The advancement in
Information Technology (IT) has resulted in the present global Big-Data model and all forms of
algorithms that are used in conjunction with RS and GIS for combating terrorist attack. Most
Big Data tools like Integrate.io, Atlas.ti, Analytics, Microsoft HDInsight, Skytree, and Talend have
no spatial attribute data for geo-coding and 3D as in Geographical Information System (GIS)
software like ArcGIS, MapInfo, Ilwis, Idrisi etc.
The increasing number of terrorist attacks has however prompted the need for the acquisition
of real-time information in a dedicated format that can help in effectively countering terrorist
threats (Ingber, 2019; Gartenstein et al, 2020). The information needed by security operatives
exists in multiple forms and formats, cutting across different geographical location, physical,
socio-economic, institutions, and organizations. The socio-political undertow in sourcing and
integration of data from these different sources by security operatives often require huge
resources and time using traditional methods (Coskun et al., 2008). Remote Sensing (RS) and
GIS have proven to be very effective in filling the gap created by thetraditional methods in
analysing big data. In fact, Artificial Intelligent AI is now being deployed to predict future
terrorism at both national and global levels ((Uddinet al, 2021; Python et al, 2021; Buffa et al,
2022; Voukelatou et al, 2022). It is against this background that this paper examines the inter- operability of RS, GIS and Big-Data deployment in terrorist attack mitigation in Nigeria and Sub
Saharan Africa countries.
RELATED LITERATURE AND CONCEPTUAL FRAMEWORK
Technology generally according to Wolfendale (2021) simply means any human made artefact,
including everything from basic tools, specific invented devices, to complex socio-technological
systems that involve a range of concepts and associations that may not be explicit, but shape
our moral thinking, mediate moral decisions that instill norms. Therefore, many recent
researchers accomplished the spatio-temporal assemblage capacity using spatial on-line
analytical processing (SOLAP) systems (Gonzalez and Gonzalez, 2013; Ahmed, 2008) as a data
cube thereby extracting the needed data from the huge dataset. In extracting data with some
geographic features to identify or tag the targeted areas of interest like terrorist camps
monitoring, multi-temporal and multispectral high resolution RS image data are processed in
GIS laboratory (Lizhe et al 2016; Lizhe et al 2017; Weitao et al 2018).
The Syria civil war is another good example where satellite data was used to target and destroy
archaeological site around the Abbasid Palace, while RS techniques were used to estimate the
Page 3 of 21
Services for Science and Education – United Kingdom 378
European Journal of Applied Sciences (EJAS) Vol. 13, Issue 1, February-2025
oil production of ISIS in resource-rich regions for geopolitics and energy security (Moision and
Harle 2006; Benítez 2007; Hagenlocher 2012; Hu and Ge 2013; Casana and Laugier 2017; Do et
al 2018; Xu et al 2018; Hansen-Lewis 2018)
Yassine et al (2021) opined that the Spatial-feature Remote Sensing Data Cube (SRSDC) often
used in conflict regions is a data cube that is aimed at providing scalable multi-dimensional data
analysis for large scale RS data. SRSDC as GIS Software has the potential of storing, retrieving,
managing and translating features that are amenable to information query operations. The long
time-series in the RS techniques helps in performance evaluation of feature data cube for
regional distribution.
Remote Sensing (RS) Technology
RS can be defined as the measurement or acquisition of information of a phenomenon or an
object of interest by a recording device without physical contact with the object (Coskun, 1995)
cited by Coskun et al, (2008). The use of RS can be traced back to 1950s when camera and
electronic sensors were mounted on spacecraft to capture imageries for military operation
purposes. RS system is made up of sensors that may be active (generating their own radian
energy line Radars) and passive (natural light dependent sensors) in operation. These sensors
operate in different regions of the Electromagnetic spectrum from the ultraviolet to microwave
with the capacity to collect large amount of information about the earth's surface daily
(Malgorzata, 2010; Dukiya, 2014). The uniqueness of this technology includes the following:
a) Collection of real-time data and information extraction;
b) Effective for direct electro-transmission to action locations and receiving stations;
c) Low-cost comparative advantage over large area coverage;
d) High potential for day and night operations via thermal infrared and microwave sensors.
e) Repetitive and synoptic coverage of study areas.
Generally, the usability and application of imageries acquired through an aerial or satellite
platforms :(Radar or Lidar optic sensors) depends on the following three parameters (Prata et
al 1995; USGS, 2019):
1. Pixel values of surface features refer to the spatial resolution that ranges from 300m to
tens of centimetre
2. Electromagnetic regions of sensor operation refer to the spectral resolution (from blue
to infrared); and
3. radiometric resolution that refer to the ability to recognize feature brightness variations
(from 256 to 64000 level)
Arockia and Subhashri (2017), identify three main stages in RS operation which are: as a)
Remote Sensing Acquisition Unit (RSDU), b) Data Processing Unit (DPU) and c) Data Analysis
Decision Unit (DADU) as illustrated in Fig. 1. Technological advancement in RS and GIS
applications includes satellite TV for PC photo analysis, gradient and side detection, and
gradient intensity for effective intra prediction (Tsai et al, 2008; Paul et al, 2010; Dugane and
Raut, 2014).
Page 4 of 21
379
Dukiya, J. J. (2025). Application of Remote Sensing and Geographic Information System in Counter Terrorism in Sub Saharan Africa. European Journal
of Applied Sciences, Vol - 13(1). 376-396.
URL: http://dx.doi.org/10.14738/aivp.131.18305
Figure 1: Remote Sensing Big Data Architecture.
Source: Arockia and Subhashri (2017).
There are wide variety of satellite imageries from different countries’ platforms; that ranges
from France SPOT, US Landsat series, Ikonos, Quick birds, SRTM, NigeriaSat-X and many others.
The space satellites acquire images that are downloaded at the countries ground receiving
station as illustrated in Fig 2.
Figure 2: Remote Sensing system
Page 5 of 21
Services for Science and Education – United Kingdom 380
European Journal of Applied Sciences (EJAS) Vol. 13, Issue 1, February-2025
On a small scale, as part of RS activities, unmanned area vehicle (UAV) are used by researchers
and security agents to carrying out reconnaissance survey of enemy territory, forces fly over
war zones, drop supplies to troops, and release bombs over enemy zone. (Microsoft Encarta,
2009). UAV are used to carry sensors for aerial surveillance of enemy locations and activities
since they are unmanned, while modern researches have leverage on the life birds and made
them to bear sensors that image enemy territory unaware as illustrated in Fig. 3a, b. The use of
drones as a means of killing suspected and known members of Al Qaeda and other terrorist and
militant organisations began under the Bush administration, expanded under the Obama
administration (Kaag and Kreps, 2014), and expanded further under the Trump administration
where as of May 18, 2020, 40 air-strikes had been launched against Somalia (Wolfendale, 2021).
Figure 3: Sensor bearing UAV and Birds for RS operations.
Source: Kaag and Kreps (2014) www.dronepoweredsolutions.com
The advancement in RS technology has helped security operatives achieved a highly favourable
and decisive decision in all their operations. For example, in the 1991 Gulf War, RS was vital to
the operation desert storm's (US Army) success in defeating the enemies. According to the US
Army's Geographic Unit, RS satellites were constantly used in providing infrared and
microwave visual data to its troops (NOAA, 2017). Alharith and Samak (2018) assert that, land
and air commands of the US military relied on information from geostationary satellite for
reconnaissance mission, spatial decision support system for ammunition selection, troop’s
reorientation, and movement of equipments. This example revealed that the integration of RS
technology into the operations of the military would go a long way to curtail the activities of
terrorist organisation within the Sub Saharan Region.
Geographic Information System (GIS) in Combating Terrorism
Terrorist groups make use of different technology and tools to support their operation (Furnell
and Warren, 1999). Alharith and Samak (2018) opined that part of the tools used by terrorists
is the internet. In curtailing the activities of terrorist there is a need to employ the use of GIS
because of its ability to harness the power and versatility of the internet. GIS can be defined as
a multilevel computer system that is capable of capturing, storing, analysing, and displaying
geographically referenced information (Prata et al, 1995; USGS, 2019). This implies that GIS has
the ability to identify data on the earth's surface in relation to its location, (Dukiya, 2011). In
the fight against terrorism, GIS can be applied in four stages that is, monitoring/surveillance,
Preparedness, Response and Mitigation (Deogawanka, 2015; Michael, 2021). The GIS
operations depend on referenced geographical and attribute data for spatial analysis queries
Page 6 of 21
381
Dukiya, J. J. (2025). Application of Remote Sensing and Geographic Information System in Counter Terrorism in Sub Saharan Africa. European Journal
of Applied Sciences, Vol - 13(1). 376-396.
URL: http://dx.doi.org/10.14738/aivp.131.18305
and predictions as illustrated in Fig. 4. GIS applications are generally for environmental analysis
and decision making in disaster management and sustainable development.
Figure 4: GIS model.
Source: After Abbas, and Fasona (2012)
In GIS environment, data are gathered, processed and presented in maps either in raster or
vector format, and risk assessment is done on the maps produced in order to analyse and
determine potential targets spots/locations (Prakash, 2000; Temfli et al 2009; Mccartney, &
Mehta, 2020). Data are shared in a simpler format for easy comprehension in preparation for
the third stage (i.e, Responses), while prediction of activities; combat operation and execution
are examined at the fourth stage (Mitigation). The stages in GIS implementation for counter- terrorism is as indicated in Fig. 5.
Page 11 of 21
Services for Science and Education – United Kingdom 386
European Journal of Applied Sciences (EJAS) Vol. 13, Issue 1, February-2025
area with 505 incidences., and Maiduguri the state capital had the highest. Also, in assessing the
impacts of bomb blast in the State, Buffering tool in ArcGIS was used to generate buffer map as
a guide in evacuating residents within the trouble zones. Fig.8 shows a vulnerability buffer map
indicating blast radius.
Figure 8: Multiple Bomb Locations with their Respective Blast Radius
Source: After. Mustafa (2020)
Anderson and Lochery. (2008) earlier reported that he UN Institute for Training and Research
(UNITAR) also used Moderate Resolution Imaging Spectroradiometer (MODIS) images to
identify the areas showing signs of violence occurrences following Kenyan National Election
violence.s. While Madden et al (2009) used a combined data from personal narrates with GIS
data to assess mass atrocities in the northern part of Uganda. In fact, night time RS imageries
are equally very effective in the Study of conflict regions as demonstrated by Jiang et al. (2017)
and Levin et al. (2018). They both reported the use of night-time light data to study the rate of
armed conflicts growth over the last decade; where Suomi National Polar-Orbiting Partnership
Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS) has been used to evaluate the
crisis in Sana, A study of the extent of conflict area in which settlements were burnt up using
RS and GIS was also carried out by Marx et al. (2013; 2019). in which they used change detection
in the near-nifrared reflectance] of multi-tempora images to determine levels of fire conflict
damage in the Rakhine state of Myanmar. Apart from the G5 nations like USA, Japan, France,
UK, and Russia, Iraq is another country that has demonstrated the effectiveness of GIS in
analysing the spread and spatial pattern of insurgency overa period of time (Brown et al., 2004).
Generally, according to Wolfendale (2021), the deployment of technology in the fight against
terrorism simply means any human made artifact, including everything from basic tools,
specific invented devices, to complex socio-technological systems that involve a range of
concepts and associations that may not be explicit, but shape our moral thinking, mediate moral
decisions that instil norms. Therefore, although, the enormous benefits of RS and GIS are yet to
be fully explored by security operatives in the Sub-Saharan Africa in the face of increasing
domineering activities of terrorism in the region, the process of geometry (polygons/polylines)
created on satellite imagery was however used to evaluate the damages done by Boko Haram
Page 14 of 21
389
Dukiya, J. J. (2025). Application of Remote Sensing and Geographic Information System in Counter Terrorism in Sub Saharan Africa. European Journal
of Applied Sciences, Vol - 13(1). 376-396.
URL: http://dx.doi.org/10.14738/aivp.131.18305
The Challenge of adopting SR GIS and Big Data in Sub-Saharan Africa
Though the roles of RS, GIS, and Big Data in combating terrorism cannot be over emphasized
(Yu, 2018) the issues that are acting as impediment to their usage in the Sub-Saharan Africa
and Nigeria in particular are as follows:
1. Paucity of data in relevant format and Poor database Management: data
management has always been an issue in developing countries because of the use of
obsolete equipments. The issue of unavailability of data and poor data management has
being hindering the effective utilization of RS and GIS. In fact one can easily download
archive satellite images of western countries than accessing imageries from Nigeria
satellite. Data acquisition in Sub Saharan Africa is always marred by long bureaucratic
process (Oarhe, 2013).
2. Inadequate Skilled Personnel: for effective utilization of RS, GIS and Big Data tools, it
is important to have adequate skilled personnel who are versatile in manipulating of
software and equipments that will aid data collection in countering terrorism. Personnel
with these capacities tend to be inadequate amidst security operatives in the Sub
Saharan Africa (Onapajo, 2013).
3. Poor funding: little or no funds are often allocated to relevant research institutes and
this affect the quality of research carried out in these institutes. The use of unlicensed
cracked software is prevalent in Nigeria for instance; obsolete equipment in combating
terrorism is consequential to the funds made available by the government. Inadequate
funding has the bane of military ineffectiveness in the Sub Saharan Africa.
4. Poor coordination of allied agencies: As at now, no Sub Saharan African country has
a well defined formidable national Search and Rescue operation (SAR) with modern
equipment. The research technology institutes are not well integrated with the national
defence institute; neither do they sponsor research in the nations, universities of
technology as a feeder to national security (Onapajo, 2013).
5. Over politicking of issue: In the words of professor Eskotoyo emeritus, ‘there is no
democracy in Africa but plutocracy’. All issues are marred by politicking to the detriment
of the commonersas also observed by (Onuoha, 2012). This then causes lip service to
threatening issues, for instance, Boko haram in Nigeria started as one state affair and
was handled as such (Onapajo, 2013).
6. Cancer worm of misappropriation of fund: this is not unconnected to the value
system in continent and is seen as a "monsters" that has eaten deep into the fabrics of
the sub-Saharan States (U.S. State Department.,2014). For instance, as reported in some
of the national dailies, millions of dollars were misappropriated by the then National
Security Adviser (Sambo Dasuki) of Nigeria. The act of fund mismanagement in Sub
Saharan Africa countries is an impediment in building a robust digital database from RS
and GIS technology (Siegle, 2013).
RECOMMENDATIONS
To ameliorate the problems of RS, GIS, and Big Data utilization in combating terrorism in Sub- Saharan Africa, the following recommendations are made:
1. There should be adequate coordination and harnessing of the nucleated research
outputs in each country and the region in general to effectively manage and curtail
terrorism in Sub Sahara Africa. There are lots of cut-edge researches across the research
Page 16 of 21
391
Dukiya, J. J. (2025). Application of Remote Sensing and Geographic Information System in Counter Terrorism in Sub Saharan Africa. European Journal
of Applied Sciences, Vol - 13(1). 376-396.
URL: http://dx.doi.org/10.14738/aivp.131.18305
Ahmed T.O. (2008). Spatial on-line analytical processing (solap): Overview and current trends. In 2008
International Conference on Advanced Computer Theory and Engineering, pages 10951099.
Alharith A. A. S. And Samak, Y. A. A. (2018). Fighting Terrorism More Effectively with the Aid of GIS: Kingdom of
Saudi Arabia Case Study. American Journal of Geographic Information System, 7(1): 15 -31
Amnesty International (2015). Our Job is To Shoot, Slaughter and Kill’ Boko Haram’s Reign of Terror in North- East Nigeria. Index: AFR 44/1360/2015
An J. H., Dodis Y., and Rabin T. (2002). On the security of joint signature and encryption. In Advances in
Cryptology - EUROCRYPT 2002, volume 2332 of Lecture Notes in Computer Science, pages 83–107. Springer- Verlag.
Anderson, D.M.; Lochery, E. (2008). Violence and exodus in Kenya’s rift valley, 2008: Predictable and
preventable? J. East. Afr. Stud.y, 2, 328–343. [CrossRef]
Arockia P. S, Subhashri. K. (2017). Big Data Architecture for Remote Sensing Applications International Research
Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 10 e-ISSN: 2395-0056. www.irjet.net
Avtar, R.; Kouser, A.; Kumar, A.; Singh, D.; Misra, P.; Gupta, A.; Yunus, A.P; Kumar, P.; Johnson, B.A.; Dasgupta, R.;
et al. (2021). Remote Sensing for International Peace and Security: Its Role and Implications. Remote Sens, 13,
439. https://doi.org/10.3390/rs13030439
Avtar, R.; Kouser, A.; Kumar, A.; Singh, D.; Misra, P.; Gupta, A.; Yunus, A.P; Kumar, P.; Johnson, B.A.; Dasgupta, R.;
et al. (2021). Remote Sensing for International Peace and Security: Its Role and Implications. Ronald Estoqu
(Edit) Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing,
Special Issue 13, 439. https://doi.org/10.3390/rs13030439Academic
Bashir Bala & Usman A. Tar (2021) Regional Cooperation in West Africa: Counter-Terrorism and Counter- Insurgency, African Security, 14:2, 186-207, DOI: 10.1080/19392206.2021.1929747
Bello-Orgaz G., Jung J. J., and Camacho D., (2016) “Social big data: Recent achievements and new challenges,” Inf.
Fusion, vol. 28, pp. 45–59.
Benítez, P.C.; McCallum, I.; Obersteiner, M.; Yamagata, Y. (2007). Global potential for carbon sequestration:
Geographical distribution, country risk and policy implications. Ecol. Econ., 60, 572–583. [CrossRef]
Brown, D., Dalton, J., & Hoyle, H., (2004). Spatial forecast methods for terrorist events in urban environments,
Springer-Verlag Berlin Heidelberg
Brown, W. (2010). Africa now at the heart of global terrorism threat, according to a new index. The Guardian, 11
December. https://www.telegraph.co.uk/global-health/terror-and-security/africa-now-heart-globalterrorism- threat-according-new-index/
Buffa C, Sagan V, Brunner G, Phillips Z. (2022). Predicting Terrorism in Europe with Remote Sensing, Spatial
Statistics, and Machine Learning. ISPRS International Journal of Geo-Information; 11(4):211.
https://doi.org/10.3390/ijgi11040211
Casana, J.; Laugier, E.J. (2017). Satellite imagery-based monitoring of archaeological site damage in the Syrian
civil war. PLoS ONE, 12, e0188589. [CrossRef]
Chen M., Mao S., and. Liu Y, (2014). “Big data: A survey,” Mobile Network. Applications, vol. 19, no. 2, pp. 171–
209,
Corrigan, D. Zikopoulos P. Parasuraman K, Deutsch T., Deroos D. and Giles J. (2012), Harness the Power of Big
Data the IBM Big Data Platform. 1st ed. New York, NY, USA: McGraw-Hill.
Page 17 of 21
Services for Science and Education – United Kingdom 392
European Journal of Applied Sciences (EJAS) Vol. 13, Issue 1, February-2025
Chou, S. (2017). More than 75 Percent of Terrorist Attack in 2016 Took Place in just 10 Countries. Retrieved from
https://www.pri.org/stories/2017-07-14 Accessed 10/12/2018.
Coskun, H. G., Gokce, U., and Alganci, U. (2008). The Role of Remote sensing and GIS for Security. Integration of
Information for Environmental Security. Pp 323 - 337 Doi:10.1007/978-1-4020-6575-0
Deogawanka, S. (2015). How G.I.S supports the Fight against Terrorism. Retrieved from
https://www.gislounge.om/gis-supports-fight-terrorism Accessed 10/12/2018.
Do, Q.-T.; Shapiro, J.N.; Elvidge, C.D.; Abdel-Jelil, M.; Ahn, D.P.; Baugh, K.E.; Hansen-Lewis, J.; Zhizhin, M.; Bazilian,
M.D. (2018). Terrorism, geopolitics, and oil security: Using remote sensing to estimate oil production of the
Islamic State. Energy Res. Soc. Sci. 44, 411–418. [CrossRef]
Dong X. L., Gabrilovich, E. Murphy K., Dang V., Horn W., Lugaresi C., Sun S., and Zhang W. (2015), “Knowledge- based trust: Estimating the trustworthiness of web sources,” Proc. VLDB Endow., (8), 9, pp. 938–949.
Dugane R. A. and Raut A. B. (2014). A survey on Big Data in real-time International Journal of Recent Innovation
Trends Computations Commun., vol. 2, no. 4 pp. 794–797, Apr. 2014.
Dukiya J. J. (2013). Spatial Analysis of Police Station/Post Distribution in the Pursuance of Urban Security in
Nigeria. In the International Journal of Engineering and Sciences (IJES), Vol. 2 No. 7, pp35-41. ISSN(e)2319-1813
http://www.theijes.com.
Dukiya J J. ZAGI, B. A. (2016). Emergency Ambulance Service Scheme in Road Traffic Accident Rescue Operation;
the Case of FCT Abuja, Nigeria. Lagos Journal of Environmental Studies Faculty of Environmental Sciences,
University of Lagos, Nigeria. Volume 8, No 1, www.ljes.unilag.edu.ng.
Dukiya J. J. (2011). Application of Remote Sensing to Animal Disease Surveillance. In Vom Journal of Veterinary
Science, Vol. 8. Pg 25-32.
Dukiya J. J. (2014). Space Technology in transport Disaster Search and Rescue operation: The Challenge for
Africa. International Journal of Advanced Remote Sensing and GIS, 3(1) 457 – 475 http://technical.cloud- journals.com/index.php/ijarsg/article/view/Tech-156. ISSN 2320-0243
Freudiger J., Rane S., Brito A. E., and Uzun E. (2014), “Privacy preserving data quality assessment for high-fidelity
data sharing,” in Proceedings of the ACM Workshop on Information Sharing & Collaborative Security. New York,
NY, USA: ACM, pp. 21–29.
Faizrahnemoon M., Schlote A., Maggi L., Crisostomi E., Robert S., (2015). A big-data model for multi-modal public
transportation with application to macroscopic control and optimisation, International Journal of Control 1–28.
Feldstein, S. (2018). Do Terrorist Trends in Africa Justify the U.S. Military's Expansion? Carnegie Endowment for
International Peace. Retrieved from https://www.carnegieendowment.org
Furnell, S.M. and Warren, M. J. (1999). Computer hacking and Cyber Terrorism: The Real Threats in the
Millenium. Computer and Security 18, 28 -34
Gisgeography (2018). 1000 GIS Application and Uses: How is GIS Changing the World? Retrieved from
http://www.gisgeography.com/gisapplication-uses Accessed 10/12/2018.
Gartenstein-Ross D, Clarke CP, Shear M (2020) Terrorists and technological innovation. Lawfare,
https://www.lawfareblog.com/terrorists-and-technological-innovation
Gorsevski, V.; Kasischke, E.S.; Dempewolf, J.; Loboda, T.; Grossmann, F. (2012). Analysis of the Impacts of armed
conflict on the Eastern Afromontane forest region on the South Sudan—Uganda border using multitemporal
Landsat imagery. Remote Sens. Environ., 118, 10–20. [CrossRef]
Page 19 of 21
Services for Science and Education – United Kingdom 394
European Journal of Applied Sciences (EJAS) Vol. 13, Issue 1, February-2025
Michael Clarke (2021). “No Cracks, no Blind Spots, no Gaps”: Technologically-Enabled Counter-terrorism and
Mass Repression in Xinjiang, China’ in Adam Henschke, ·Alastair Reed, Scott Robbins, and Seumas Miller (Edit)
Advanced Sciences and Technologies for Security Applications. Emerging Challenges at the Frontiers of Counter- Terrorism. Springer Nature Switzerland AG ISBN 978-3-030-90220-9 ISBN 978-3-030-90221-6 (eBook),.
https://doi.org/10.1007/978-3-030-90221-6
Mohamed N. and Al-Jaroodi, J. (2014). “Real-time big data analytics: Applications and challenges,” in Proc. Int.
Conf. High Perform. Comput. Simulation, pp. 305–310.
Moisio, S.; Harle, V. (2006). The limits of geopolitical remote sensing. Eurasian Geogr. Econ., 47, 204–210.
[CrossRef]
Mroszczyk J..and Abrahms M. (2021). Terrorism in Africa: Explaining the Rise of Extremist Violence against
Civilians. E-International Relations ISSN 2053-8626 https://www.e-ir.info/2021/04/09/terrorism-in-africa- explaining-the-rise-of-extremist-violence-against-civilians
Mustafa I. A. (2020). A Gis Tool for The Management of The Nature of Insurgency in Borno State. International
Journal of Advanced Research. ISSN: 2320-5407 8(10), 993-1001. Http://Dx.Doi.Org/10.21474/Ijar01/11926
NCC Monthly subscriber statistics Report, August 2014. http://www.ncc.gov.ng;
Nwanga, M. E., Onwuka, E. N., Aibinu, A. M. and Ubadike, O. C. (2015) “Impact of Big Data Analytics to Nigerian
mobile Phone Industries”. the proceeding of the 201 Intl’ conference on Industrial Engineering and Operations
Management (IEOM).
Nwanga, M. E., Onwuka, E. N., Aibinu, A. M. and Ubadike, O. C. (2014). Leveraging Big Data in Enhancing National
Security in Nigeria International Journal of Developments in Big Data and Analytics 1 (1), 70—84
Nance, M. W. 2014. Terrorist Recognition Handbook: A Practitioner's Manual for Predicting and Identifying
Terrorist Activities. Third ed. Boca Raton, Florida: CRC Press
National Oceanic and Atmospheric Administration (NOAA), (2017). Satellite Data Services: An Introduction,
NOAA, ttps//www.noaasis.noaa.gov/NOAASIS/ml/satservices.html
Oarhe, O. (2013). “Responses of the Nigerian Defense and Intelligence Establishments to the Challenge of Boko
Haram.” In Boko Haram: Anatomy of a Crisis, ed. I. Mantzikos, 85-91. Bristol: e-International Relations.
Onapajo, H. (2013). “Why Nigeria is not Winning the Anti-Boko Haram War.” In Boko Haram: Anatomy of a Crisis,
ed. I. Mantzikos, 85-91. Bristol: e-International Relations.
Onuoha, F. C. (2012). "The Audacity of the Boko Haram: Background, Analysis and Emerging Trend." Security
Journal 25 (2): 134-151.
Paul, A., Bharanitharan K., and Wang J.-F., (2010). “Region similarity based edge detection for motion estimation
in H.264/AVC”.
Pech, L.; Lakes, T. (2017). The impact of armed conflict and forced migration on urban expansion in Goma:
Introduction to a simple method of satellite-imagery analysis as a complement to field research. Appl. Geogr., 88,
161–173. [CrossRef]
Pierre G. (2014). Big data and Earth observation new challenges in remote sensing images interpretation. ICube
CNRS - Université de Strasbourg
Prata, A. J., V. Casellescoll, C., Sobrino, J. A., & Ottle, C. (1995). Thermal remote sensing of land surface
temperature from satellites: current status and future prospects. Remote Sensing Reviews, 12(3–4), 175–224.
https://doi.org/10.1080/02757259509532285
Page 20 of 21
395
Dukiya, J. J. (2025). Application of Remote Sensing and Geographic Information System in Counter Terrorism in Sub Saharan Africa. European Journal
of Applied Sciences, Vol - 13(1). 376-396.
URL: http://dx.doi.org/10.14738/aivp.131.18305
Python A, Bender A, Nandi AK, Hancock PA, Arambepola R, Brandsch J, et al. (2021). Predicting non-state
terrorism worldwide. Science advances; 7(31): eabg4778. https://doi.org/10.1126/sciadv.abg4778 PMID:
34330703
Raleigh, C., Linke, A., Hegre, H. and Karlsen, J. (2010). Introducing ACLED: An Armed Conflict Location and Event
Dataset. Journal of Peace Research, 47(5), pp. 651-660.
Ram Avtar, Asma Kouser, Ashwani Kumar, Deepak Singh, Prakhar Misra, Ali P. Yunus, Pankaj Kumar, and Andi
Besse Rimba (2021). Remote Sensing for International Peace and Security: Its Role and Implications. Ronald C.
Estoque (Edit) Special Issue, Assessing Sustainability over Space and Time: The Emerging Roles of GIS and
Remote Sensing. 13, 439. https://doi.org/10.3390/rs13030439
Rob de Wijk (2021). Contributions from the Military Counterinsurgency Literature for the Prevention of
Terrorism. Alex P. Schmid (edit) Handbook of Terrorism Prevention and Preparedness. International Centre for
Counter-Terrorism – The Hague (ICCT) Press. DOI: 10.19165/2020.6.01, ISSN: 2468-0486, ISBN:
9789090339771
Schoepfer, E.; Kranz, O.; Addink, E.; Coillie, F. (2010). Monitoring natural resources in conflict using an object- based multi-scale image analysis approach. Proc. GEOBI.
Sheppard S. A. and Terveen L. (2011). “Quality is a verb: The operationalization of data quality in a citizen
science community,” in Proceedings of the 7th International Symposium on Wikis and Open Collaboration. New
York, NY: ACM, 2011, pp. 29–38.
Siegle, J. (2013). “Boko Haram and the Isolation of Northern Nigeria: Regional and International Implications.” In
Boko Haram: Anatomy of a Crisis, ed. I. Mantzikos, 85-91. Bristol, UK: e-International Relations
Snijders C., Matzat U., Reips U.-D. (2012), Big data: Big gaps of knowledge in the field of internet science,
International Journal of Internet Science 7 (1) 1–5.
Tanala M. (2015). Prospective study on the potential of big data, Quarterly Report of RTRI 56 (1) 5–9.
Tsai A.-C., Paul A., Wang J.-C., and Wang J.-F. (2008). “Intensity gradient technique for efficient intra prediction in
H.264/AVC”.
Uddin MI, Zada N, Aziz F, Saeed Y, Zeb A, Ali Shah SA, et al. (2021). Prediction of future terrorist activities using
deep neural networks. Complexity. https://doi.org/10.1155/2020/1373087
United Nations (2015). The United Nations Global Counter-Terrorism Strategy, Plan of Action to Prevent Violent
Extremism, Report of the Secretary-General, (A 70/674- Ingber S (2019) Global effort begins to stop social media
from spreading terrorism. NPR, April 29. https://www.npr.org/2019/04/24/716712161/global-effort-begins- to-stop-social-media-from-spreading-terrorism.
U.S. State Department. (2014). "Boko Haram and U.S. Counterterrorism Assistance to Nigeria.".
http://www.state.gov/r/pa/prs/ps/2014/05/226072.htm.
USGS. (2019). Landsat 8 Data Users Handbook. Nasa, 8(November), 114.
https://landsat.usgs.gov/documents/Landsat8DataUsersHandbook.pdf
Venkat N. G., Subbaiyan J, and Dhana R. (2015). Data Management Issues in Big Data Applications. ResearchGate
https://www.researchgate.net/publication/275333117
Voukelatou V, Miliou I, Giannotti F, Pappalardo L. (2022). Understanding peace through the world news. EPJ Data
Science. 11(1):2. https://doi.org/10.1140/epjds/s13688-022-00315-z PMID: 35079561
Weitao C., Xianju L., Haixia H., et al (2018). Assessing different feature sets’ effects on land cover classification in
complex surface-mined landscapes by Ziyuan-3 satellite imagery. Remote Sensing.
Page 21 of 21
Services for Science and Education – United Kingdom 396
European Journal of Applied Sciences (EJAS) Vol. 13, Issue 1, February-2025
Xu H. (2015). “What are the most important factors for accounting information quality and their impact on ais
data quality outcomes?” J. Data and Information Quality, (5), 4, pp. 14:1–14:22.
Xu, Y.; Knudby, A.; Côté-Lussier, C. (2018). Mapping ambient light at night using field observations and high- resolution remote sensing imagery for studies of urban environments. Build. Environ. 145, 104–114. [CrossRef]
Yassine S., Fadoua B., Aouad S., and Aberrahim M. (2021). Cloud Computing in Remote Sensing: Big Data Remote
Sensing Knowledge Discovery and Information Analysis. International Journal of Advanced Computer Science
and Applications (IJACSA), (12), 5.www.ijacsa.thesai.org
Yu M. (2018). Big Data in Natural Disaster Management: A Review. MDPI https://www.mdpi.com