Home / Current Issue / Paper 1716084
UAV-Based Pipeline and Corridor Monitoring: A Review of Current Practices and Emerging Technologies
Subject area: Science,Engineering and Technology · Area of research: Pipeline and Corridor Monitoring
DOI: 10.64388/IREV3I10-1716084
Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as transformative tools for monitoring pipelines and infrastructure corridors, offering enhanced efficiency, safety, and data accuracy compared to conventional inspection methods. This review synthesizes current practices in UAV-based monitoring and explores emerging technologies shaping the future of corridor surveillance. Pipelines and utility corridors are critical assets that require continuous inspection to detect leaks, structural faults, vegetation encroachment, and unauthorized activities. Traditional monitoring techniques, including ground patrols and manned aerial surveys, are often labor-intensive, costly, and limited in spatial and temporal coverage. UAV systems equipped with advanced sensors such as high-resolution optical cameras, thermal imagers, multispectral sensors, and LiDAR provide detailed and timely data acquisition capabilities. These technologies enable precise detection of anomalies, including temperature variations indicative of leaks, terrain deformation, and vegetation growth within restricted zones. Integration with Global Navigation Satellite Systems (GNSS) ensures accurate georeferencing, while real-time data transmission enhances situational awareness and rapid response. Geographic Information Systems (GIS) further support data integration, visualization, and spatial analysis, enabling informed decision-making for maintenance and risk management. Recent advancements in artificial intelligence and machine learning have significantly improved automated data processing and anomaly detection. Deep learning models, particularly convolutional neural networks, are increasingly applied for object recognition and classification in UAV imagery, reducing reliance on manual interpretation. Additionally, cloud-based platforms facilitate large-scale data storage, processing, and collaborative analysis across stakeholders. Despite these benefits, several challenges persist, including regulatory restrictions on UAV operations, limited flight endurance, data processing complexity, and cybersecurity concerns. Environmental factors such as weather conditions can also affect data quality and operational reliability. Emerging innovations, including autonomous UAV swarms, edge computing, and hybrid power systems, are expected to address these limitations and enhance monitoring efficiency. This review highlights the growing role of UAVs in modern pipeline and corridor management, emphasizing their potential to improve operational efficiency, reduce risks, and support sustainable infrastructure development. Future research should focus on improving system integration, developing standardized frameworks, and enhancing real-time analytics capabilities to fully leverage UAV technologies in infrastructure monitoring applications.
Keywords
UAV, Pipeline Monitoring, Corridor Inspection, Remote Sensing, GIS, LiDAR, Thermal Imaging, Machine Learning, Infrastructure Surveillance, Geospatial Technologies.
References
[1] Ahmed, K. S., & Odejobi, O. D. (2018). Conceptual framework for scalable and secure cloud architectures for enterprise messaging. IRE Journals, 2(1), 1–15.
[2] Ahmed, K. S., & Odejobi, O. D. (2018). Resource allocation model for energy-efficient virtual machine placement in data centers. IRE Journals, 2(3), 1–10.
[3] Ahmed, K. S., Odejobi, O. D., & Oshoba, T. O. (2019). Algorithmic model for constraint satisfaction in cloud network resource allocation. IRE Journals, 2(12), 1–10.
[4] Ahmed, K. S., Odejobi, O. D., & Oshoba, T. O. (2020). Predictive model for cloud resource scaling using machine learning techniques. Journal of Frontiers in Multidisciplinary Research, 1(1), 173–183.
[5] Aifuwa, S. E., Oshoba, T. O., Ogbuefi, E., Ike, P. N., Nnabueze, S. B., & Olatunde-Thorpe, J. (2020). Predictive analytics models enhancing supply chain demand forecasting accuracy and reducing inventory management inefficiencies. International Journal of Multidisciplinary Research and Growth Evaluation, 1(3), 171–181.
[6] Akidau, T., Bradshaw, R., Chambers, C., et al. (2015). The dataflow model. VLDB, 8(12), 1792–1803. https://doi.org/10.14778/2824032.2824076
[7] Akinola, A. S., Farounbi, B. O., Onyelucheya, O. P., & Okafor, C. M. (2020). Translating finance bills into strategy: Sectoral impact mapping and regulatory scenario analysis. Journal of Frontiers in Multidisciplinary Research, 1(1), 102–111.
[8] Akinrinoye, O. V., Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2020, July). Redesigning end-to-end customer experience journeys using behavioral economics and marketing automation. Iconic Research and Engineering Journals, 4(1).
[9] Akinrinoye, O. V., Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2015, September). Predictive and segmentation-based marketing analytics framework for optimizing customer acquisition, engagement, and retention strategies. Engineering and Technology Journal, 10(9), 6758–6776.
[10] Akinrinoye, O. V., Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2020). A conceptual framework for improving marketing outcomes through targeted customer segmentation and experience optimization models. IRE Journals, 4(4), 347–357.
[11] Akinrinoye, O. V., Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2020). Strategic integration of Net Promoter Score data into feedback loops for sustained customer satisfaction and retention growth. IRE Journals, 3(8), 379–389.
[12] Akinrinoye, O. V., Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2020). Design and execution of data-driven loyalty programs for retaining high-value customers in service-focused business models. IRE Journals, 4(4), 358–371.
[13] Akinrinoye, O. V., Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2019). Evaluating the strategic role of economic research in supporting financial policy decisions and market performance metrics. IRE Journals, 3(3), 248–258.
[14] Alao, O. B., Nwokocha, G. C., & Filani, O. M. (2020). Vendor Compliance Monitoring and Automated Auditing System for Enhancing Accountability in Global Procurement and Supply Chains.
[15] Aminu-Ibrahim, A. Y., Ogbete, J. C., & Ambali, K. B. (2019). Capital project delivery models for high-risk healthcare infrastructure in developing national health systems. Iconic Research and Engineering Journals, 2(10), 626–649.
[16] Aminu-Ibrahim, A. Y., Ogbete, J. C., & Iwuanyanwu, O. C. (2020). Infrastructure-driven expansion of diagnostic access across underserved and rural healthcare regions. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 691–706.
[17] Aransi, A. N., Nwafor, M. I., Gil-Ozoudeh, I. D. S., & Uduokhai, D. O. (2019). Architectural interventions for enhancing urban resilience and reducing flood vulnerability in African cities. IRE Journals, 2(8), 321–334.
[18] Aransi, A. N., Nwafor, M. I., Uduokhai, D. O., & Gil-Ozoudeh, I. D. S. (2018). Comparative study of traditional and contemporary architectural morphologies in Nigerian settlements. IRE Journals, 1(7), 138–152.
[19] Batistič, S., & van der Laken, P. (2019). Evolution of big data analytics. Journal of Business Research, 103, 358–365. https://doi.org/10.1016/j.jbusres.2019.01.023
[20] Bayeroju, O. F., Sanusi, A. N., Queen, Z., & Nwokediegwu, S. (2019). Bio-Based Materials for Construction: A Global Review of Sustainable Infrastructure Practices.
[21] Chamola, V., Kotesh, P., Agarwal, A., Gupta, N., & Guizani, M. (2021). A comprehensive review of unmanned aerial vehicle attacks and neutralization techniques. Ad hoc networks, 111, 102324.
[22] Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
[23] Chen, M., Mao, S., & Liu, Y. (2014). Big data survey. Mobile Networks and Applications, 19, 171–209. https://doi.org/10.1007/s11036-013-0489-0
[24] Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Business value of analytics. Journal of Business Research, 70, 379–390. https://doi.org/10.1016/j.jbusres.2016.08.011
[25] Dako, O. F., Okafor, C. M., Farounbi, B. O., & Onyelucheya, O. P. (2019). Detecting financial statement irregularities: Hybrid Benford–outlier–process-mining anomaly detection architecture. IRE Journals, 3(5), 312–327.
[26] Delen, D., & Demirkan, H. (2013). Analytics as a service. Decision Support Systems, 55(1), 359–363. https://doi.org/10.1016/j.dss.2012.05.044
[27] Dilshad, N., Hwang, J., Song, J., & Sung, N. (2020, October). Applications and challenges in video surveillance via drone: A brief survey. In 2020 International
[28] Dubey, R., Gunasekaran, A., Childe, S. J., et al. (2019). Big data analytics capability in SMEs. Annals of Operations Research, 270, 395–426. https://doi.org/10.1007/s10479-016-2378-4
[29] Farounbi, B. O., Akinola, A. S., Adesanya, O. S., & Okafor, C. M. (2018). Automated payroll compliance assurance: Linking withholding algorithms to financial statement reliability. IRE Journals, 1(7), 341–357.
[30] Filani, O. M., Fasawe, O., & Umoren, O. (2019, August). Financial ledger digitization model for high-volume cash management and disbursement operations. Iconic Research and Engineering Journals, 3(2), 836–851.
[31] Filani, O. M., Nwokocha, G. C., & Alao, O. B. (2020). Digital Spend Analysis Model Enabling Supplier Consolidation to Increase Procurement Efficiency and Strategic Sourcing Performance.
[32] Filani, O. M., Nwokocha, G. C., & Babatunde, O. (2019). Framework for ethical sourcing and compliance enforcement across global vendor networks in manufacturing and retail sectors. Iconic Res Eng J, 3(6), 220-35.
[33] Filani, O. M., Nwokocha, G. C., & Babatunde, O. (2019). Lean Inventory Management Integrated with Vendor Coordination to Reduce Costs and Improve Manufacturing Supply Chain Efficiency. continuity, 18, 19.
[34] Filani, O. M., Okpokwu, C. O., & Fasawe, O. (2020). Capacity Planning and KPI Dashboard Model for Enhancing Supply Chain Visibility and Efficiency.
[35] Filani, O. M., Olajide, J. O., & Osho, G. O. (2020). Designing an integrated dashboard system for monitoring real-time sales and logistics KPIs. Iconic Res Eng J, 4(5), 180-95.
[36] Frempong, D., Ifenatuora, G. P., & Ofori, S. D. (2020). AI-Powered Chatbots for Education Delivery in Remote and Underserved Regions.
[37] Frempong, D., Ifenatuora, G. P., Olateju, M., & Ofori, S. D. Multimodal Instructional Design: Enhancing Language Learning in STEM Education through Diverse Technologies.
[38] Gandomi, A., & Haider, M. (2015). Big data concepts and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
[39] García-Magariño, I., Lacuesta, R., Rajarajan, M., & Lloret, J. (2019). Security in networks of unmanned aerial vehicles for surveillance with an agent-based approach inspired by the principles of blockchain. Ad Hoc Networks, 86, 72-82.
[40] Gil-Ozoudeh, I. D. S., Aransi, A. N., Nwafor, M. I., & Uduokhai, D. O. (2018). Socioeconomic determinants influencing the affordability and sustainability of urban housing in Nigeria. IRE Journals, 2(3), 164–169.
[41] Gil-Ozoudeh, I. D. S., Nwafor, M. I., Uduokhai, D. O., & Aransi, A. N. (2018). Impact of climatic variables on the optimization of building envelope design in humid regions. IRE Journals, 1(10), 322–335.
[42] Grover, V., Chiang, R. H. L., Liang, T.-P., & Zhang, D. (2018). Creating strategic business value from big data analytics. Journal of Management Information Systems, 35(2), 388–423. https://doi.org/10.1080/07421222.2018.1451951
[43] Hashem, I. A. T., Yaqoob, I., Anuar, N. B., et al. (2015). Big data on cloud computing. Information Systems, 47, 98–115. https://doi.org/10.1016/j.is.2014.07.006
[44] Ike, P. N., Aifuwa, S. E., Nnabueze, S. B., Olatunde-Thorpe, J., Ogbuefi, E., Oshoba, T. O., & Akokodaripon, D. (2018). Utilizing Nanomaterials in Healthcare Supply Chain Management for Improved Drug Delivery Systems. medicine (Ding et al., 2020; Furtado et al., 2018), 12, 13.
[45] Inmon, W. H. (2005). Building the data warehouse. Wiley. https://doi.org/10.1002/047172798
[46] Jagadish, H. V., Gehrke, J., Labrinidis, A., et al. (2014). Big data challenges. Communications of the ACM, 57(7), 86–94. https://doi.org/10.1145/2611567
[47] Kelleher, J. D., & Tierney, B. (2018). Data science foundations. MIT Press. https://doi.org/10.7551/mitpress/11101.001.0001
[48] Kimball, R., & Ross, M. (2013). Data warehouse toolkit. Wiley. https://doi.org/10.1002/9781118530801
[49] Kyere Yeboah, B., & Enow, O. F. (2018). Conceptual framework for reliability-centered maintenance programs in electricity distribution utilities. Iconic Research and Engineering Journals, 2(3), 140–153.
[50] Kyere Yeboah, B., & Enow, O. F. (2019). Policy model for root cause failure analysis integration in high-voltage grid management. Iconic Research and Engineering Journals, 2(12), 549–562
[51] Kyere Yeboah, B., & Ike, P. N. (2020). Programmatic strategy for renewable energy integration: Lessons from large-scale solar projects. International Journal of Multidisciplinary Research and Growth Evaluation, 1(3), 306–315. https://doi.org/10.54660/IJMRGE.2020.1.3.306-315
[52] Michael, O. N., & Ogunsola, O. E. (2019). Determinants of access to agribusiness finance and their influence on enterprise growth in rural communities. Iconic Research and Engineering Journals, 2(12), 533–548.
[53] Michael, O. N., & Ogunsola, O. E. (2019). Strengthening agribusiness education and entrepreneurial competencies for sustainable youth employment in Sub-Saharan Africa. IRE Journals. ISSN: 2456-8880.
[54] Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2020). Big data analytics capabilities and firm performance: A resource-based view. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.103169
[55] Nii-Okai, E. (2020). Ghana's Mining Industry, A Still Potential for Future Investment. A Still Potential for Future Investment (August 25, 2020).
[56] Nwafor, M. I., Ajirotutu, R. O., & Uduokhai, D. O. (2020). Framework for integrating cultural heritage values into contemporary African urban architectural design. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 394–401
[57] Nwafor, M. I., Giloid, S., Uduokhai, D. O., & Aransi, A. N. (2018). Socioeconomic determinants influencing the affordability and sustainability of urban housing in Nigeria. Iconic Research and Engineering Journals, 2(3), 154–169.
[58] Nwafor, M. I., Giloid, S., Uduokhai, D. O., & Aransi, A. N. (2019). Architectural interventions for enhancing urban resilience and reducing flood vulnerability in African cities. Iconic Research and Engineering Journals, 2(8), 321–334.
[59] Nwafor, M. I., Uduokhai, D. O., & Ajirotutu, R. O. (2020). Multi-criteria decision-making model for evaluating affordable and sustainable housing alternatives. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 402–410
[60] Nwafor, M. I., Uduokhai, D. O., & Ajirotutu, R. O. (2020). Spatial planning strategies and density optimization for sustainable urban housing development. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 411–419
[61] Nwafor, M. I., Uduokhai, D. O., Giloid, S., & Aransi, A. N. (2018). Comparative study of traditional and contemporary architectural morphologies in Nigerian settlements. Iconic Research and Engineering Journals, 1(7), 138–152.
[62] Nwafor, M. I., Uduokhai, D. O., Giloid, S., & Aransi, A. N. (2018). Impact of climatic variables on the optimization of building envelope design in humid regions. Iconic Research and Engineering Journals, 1(10), 322–335.
[63] Nwafor, M. I., Uduokhai, D. O., Giloid, S., & Aransi, A. N. (2019). Quantitative evaluation of locally sourced building materials for sustainable low-income housing projects. Iconic Research and Engineering Journals, 3(4), 568–582.
[64] Nwafor, M. I., Uduokhai, D. O., Giloid, S., & Aransi, A. N. (2019). Developing an analytical framework for enhancing efficiency in public infrastructure delivery systems. Iconic Research and Engineering Journals, 2(11), 657–670.
[65] Nwafor, M. I., Uduokhai, D. O., Ifechukwu, G. O., Stephen, D., & Aransi, A. N. (2019). Quantitative Evaluation of Locally Sourced Building Materials for Sustainable Low-Income Housing Projects.
[66] Nwafor, M. I., Uduokhai, D. O., Ifechukwu, G. O., Stephen, D., & Aransi, A. N. (2019). Developing an Analytical Framework for Enhancing Efficiency in Public Infrastructure Delivery Systems.
[67] Nwokocha, G. C., Alao, O. B., & Filani, O. M. (2020). Supplier Risk Mitigation and Resilience Framework Incorporating Data Analytics, Multi-Sourcing, and Proactive Vendor Development Strategies.
[68] Odejobi, O. D., & Ahmed, K. S. (2018). Performance evaluation model for multi-tenant Microsoft 365 deployments under high concurrency. IRE Journals, 1(11), 92–107.
[69] Odejobi, O. D., & Ahmed, K. S. (2018). Statistical model for estimating daily solar radiation for renewable energy planning. IRE Journals, 2(5), 1–12.
[70] Odejobi, O. D., Hammed, N. I., & Ahmed, K. S. (2019). Approximation complexity model for cloud-based database optimization problems. IRE Journals, 2(9), 1–10.
[71] Odejobi, O. D., Hammed, N. I., & Ahmed, K. S. (2020). IoT-Driven Environmental Monitoring Model Using ThingsBoard API and MQTT.
[72] Ogbete, J. C., Aminu-Ibrahim, A. Y., & Ambali, K. B. (2020). Sustainable materials selection and energy efficiency strategies for modern medical laboratory facilities. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 674–690.
[73] Oguntegbe, E. E., Farounbi, B. O., & Okafor, C. M. (2019). Conceptual model for innovative debt structuring to enhance mid-market corporate growth stability. IRE Journals, 2(12), 451–463.
[74] Oguntegbe, E. E., Farounbi, B. O., & Okafor, C. M. (2019). Empirical review of risk-adjusted return metrics in private credit investment portfolios. IRE Journals, 3(4), 494–505.
[75] Oguntegbe, E. E., Farounbi, B. O., & Okafor, C. M. (2019). Framework for leveraging private debt financing to accelerate SME development and expansion. IRE Journals, 2(10), 540–554.
[76] Oguntegbe, E. E., Farounbi, B. O., & Okafor, C. M. (2020). Strategic capital markets model for optimizing infrastructure bank exit and liquidity events. Journal of Frontiers in Multidisciplinary Research, 1(2), 121–130.
[77] Okesiji, A., Oyasiji, O., Elebe, O., Imediegwu, C. C., Filani, O. M., Umana, A. U., & Umar, M. O. (2020). Blockchain-Enabled E-Governance: A Model for Enhancing Transparency in Developing Economies.
[78] Olatunde-Thorpe, J., Aifuwa, S. E., Oshoba, T. O., & Ogbuefi, E. (2020). Metadata-driven access controls: Designing role-based systems for analytics teams in high-risk industries. International Journal of Multidisciplinary Research and Growth Evaluation, 1(3), 143–162.
[79] Omolayo, O., Okare, B. P., Taiwo, A. E., & Aduloju, T. D. Transformer-based language models for clinical text mining: A systematic review of applications in diagnostic decision support, risk stratification, and electronic health record summarization.
[80] Omotayo, O. O., Kuponiyi, A., & Ajayi, O. O. (2020). Telehealth expansion in post-COVID healthcare systems: challenges and opportunities. Iconic Research and Engineering Journals, 3(10), 496-513.
[81] Oshoba, T. O., Aifuwa, S. E., Ogbuefi, E., & Olatunde-Thorpe, J. (2020). Portfolio Optimization with Multi-Objective Evolutionary Algorithms-Balancing Risk, Return, and Sustainability Metrics.
[82] Oshoba, T. O., Hammed, N. I., & Odejobi, O. D. (2019). Secure identity and access management model for distributed and federated systems. IRE Journals, 3(4), 1–18.
[83] Oshoba, T. O., Hammed, N. I., & Odejobi, O. D. (2020). Blockchain-enabled compliance and audit trail model for cloud configuration management. Journal of Frontiers in Multidisciplinary Research, 1(1), 193–201.
[84] Osuashi Sanni, J., Ajiga, D., & Atima, M. E. (2020). Analytical models addressing measurement challenges of marketing return on investment. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 636–648.
[85] Osuashi Sanni, J., Ajiga, D., & Atima, M. E. (2020). Data-driven brand positioning frameworks: Resolving differentiation challenges in regulated professional markets. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 649–660.
[86] Osuashi Sanni, J., Ajiga, D., & Atima, M. E. (2020). Systematic review of product management strategies in mobile network rollouts across emerging markets. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 661–673.
[87] Oziri, S. T., Arowogbadamu, A. A.-G., & Seyi-Lande, O. B. (2020). Predictive analytics applications in reducing customer churn and enhancing lifecycle value in telecommunications markets. International Journal of Multidisciplinary Futuristic Development, 1(02), 40–49.
[88] Oziri, S. T., Seyi-Lande, O. B., & Arowogbadamu, A. A. G. (2019). Dynamic tariff modeling as a predictive tool for enhancing telecom network utilization and customer experience. Iconic Research and Engineering Journals, 2(12), 436-450.
[89] Oziri, S. T., Seyi-Lande, O. B., & Arowogbadamu, A. A. G. (2020). End-to-end product lifecycle management as a strategic framework for innovation in telecommunications services. International Journal of Multidisciplinary Evolutionary Research, 1(2), 54-64
[90] Provost, F., & Fawcett, T. (2013). Data science and decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
[91] Saltz, J., & Shamshurin, I. (2016). Big data team process methodologies. IEEE Big Data. https://doi.org/10.1109/BigData.2016.7840650
[92] Sanusi, A. N., Bayeroju, O. F., & Nwokediegwu, Z. Q. S. (2020). Conceptual model for low-carbon procurement and contracting systems in public infrastructure delivery. Journal of Frontiers in Multidisciplinary Research, 1(2), 81-92.
[93] Sanusi, A. N., Bayeroju, O. F., & Nwokediegwu, Z. Q. S. (2020). Framework for applying artificial intelligence to construction cost prediction and risk mitigation. Journal of Frontiers in Multidisciplinary Research, 1(2), 93-101.
[94] Sanusi, A. N., Bayeroju, O. F., Queen, Z., & Nwokediegwu, S. (2019). Circular Economy Integration in Construction: Conceptual Framework for Modular Housing Adoption.
[95] Sculley, D., Holt, G., Golovin, D., et al. (2015). Hidden technical debt in machine learning systems. NeurIPS. https://doi.org/10.48550/arXiv.1606.05386
[96] Seyi-Lande, O. B., Arowogbadamu, A. A. G., & Oziri, S. T. (2018). A comprehensive framework for high-value analytical integration to optimize network resource allocation and strategic growth. Iconic Research and Engineering Journals, 1(11), 76-91.
[97] Seyi-Lande, O. B., Arowogbadamu, A. A. G., & Oziri, S. T. (2020). Geomarketing analytics for driving strategic retail expansion and improving market penetration in telecommunications. International Journal of Multidisciplinary Futuristic Development, 1(2), 50-60.
[98] Seyi-Lande, O. B., Arowogbadamu, A. A.-G., & Oziri, S. T. (2020). Geo-marketing analytics for driving strategic retail expansion and improving market penetration in telecommunications. International Journal of Multidisciplinary Futuristic Development, 1(2), 50–60.
[99] Seyi-Lande, O. B., Oziri, S. T., & Arowogbadamu, A. A. G. (2018). Leveraging business intelligence as a catalyst for strategic decision-making in emerging telecommunications markets. Iconic Research and Engineering Journals, 2(3), 92-105.
[100] Seyi-Lande, O. B., Oziri, S. T., & Arowogbadamu, A. A. G. (2019). Pricing strategy and consumer behavior interactions: Analytical insights from emerging economy telecommunications sectors. Iconic Research and Engineering Journals, 2(9), 326-340.
[101] Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Analytics-driven decision transformation. MIS Quarterly Executive. https://doi.org/10.2139/ssrn.2486483
[102] Umoren, O., Didi, P. U., Balogun, O., Abass, O. S., & Akinrinoye, O. V. (2019). Linking macroeconomic analysis to consumer behavior modeling for strategic business planning in evolving market environments. IRE Journals, 3(3), 203-213.
[103] Umoren, O., Didi, P. U., Balogun, O., Abass, O. S., & Akinrinoye, O. V. (2019). Linking macroeconomic analysis to consumer behavior modeling for strategic business planning in evolving market environments. IRE Journals, 3(3), 203-213.
[104] Van der Aalst, W. (2016). Process mining. Springer. https://doi.org/10.1007/978-3-662-49851-4
[105] Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-f., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
[106] Watson, H. J. (2017). Decision support evolution. MIS Quarterly Executive. https://doi.org/10.2139/ssrn.3055245
[107] Yeboah, B. K., & Ike, P. N. (2020). Conceptual Program for Workforce Training and Leadership Development in Reliability Engineering.
[108] Zaharia, M., Chowdhury, M., Franklin, M., Shenker, S., & Stoica, I. (2016). Apache Spark. Communications of the ACM, 59(11), 56–65. https://doi.org/10.1145/2934664
How to cite this paper
@article{1716084,
author = {Delali Dagodzo, Miracle Chiamaka Ahiaeke Patrick},
title = {UAV-Based Pipeline and Corridor Monitoring: A Review of Current Practices and Emerging Technologies},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {3},
number = {10},
pages = {574-597},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716084.pdf},
abstract = {Unmanned Aerial Vehicles (UAVs) have emerged as transformative tools for monitoring pipelines and infrastructure corridors, offering enhanced efficiency, safety, and data accuracy compared to conventional inspection methods. This review synthesizes current practices in UAV-based monitoring and explores emerging technologies shaping the future of corridor surveillance. Pipelines and utility corridors are critical assets that require continuous inspection to detect leaks, structural faults, vegetation encroachment, and unauthorized activities. Traditional monitoring techniques, including ground patrols and manned aerial surveys, are often labor-intensive, costly, and limited in spatial and temporal coverage. UAV systems equipped with advanced sensors such as high-resolution optical cameras, thermal imagers, multispectral sensors, and LiDAR provide detailed and timely data acquisition capabilities. These technologies enable precise detection of anomalies, including temperature variations indicative of leaks, terrain deformation, and vegetation growth within restricted zones. Integration with Global Navigation Satellite Systems (GNSS) ensures accurate georeferencing, while real-time data transmission enhances situational awareness and rapid response. Geographic Information Systems (GIS) further support data integration, visualization, and spatial analysis, enabling informed decision-making for maintenance and risk management. Recent advancements in artificial intelligence and machine learning have significantly improved automated data processing and anomaly detection. Deep learning models, particularly convolutional neural networks, are increasingly applied for object recognition and classification in UAV imagery, reducing reliance on manual interpretation. Additionally, cloud-based platforms facilitate large-scale data storage, processing, and collaborative analysis across stakeholders. Despite these benefits, several challenges persist, including regulatory restrictions on UAV operations, limited flight endurance, data processing complexity, and cybersecurity concerns. Environmental factors such as weather conditions can also affect data quality and operational reliability. Emerging innovations, including autonomous UAV swarms, edge computing, and hybrid power systems, are expected to address these limitations and enhance monitoring efficiency. This review highlights the growing role of UAVs in modern pipeline and corridor management, emphasizing their potential to improve operational efficiency, reduce risks, and support sustainable infrastructure development. Future research should focus on improving system integration, developing standardized frameworks, and enhancing real-time analytics capabilities to fully leverage UAV technologies in infrastructure monitoring applications.},
keywords = {UAV, Pipeline Monitoring, Corridor Inspection, Remote Sensing, GIS, LiDAR, Thermal Imaging, Machine Learning, Infrastructure Surveillance, Geospatial Technologies.},
month = {April},
doi = {https://doi.org/10.64388/IREV3I10-1716084}
}