Scalable Geospatial Data Platforms for Utility Asset Monitoring and Risk Mitigation
Keywords:
geospatial data infrastructure, utility asset monitoring, risk assessment, LiDAR, unmanned aerial vehicles, convolutional neural network, spatial database, cloud computing, distributed processing, flood exposure, pipeline integrity, spatial indexingAbstract
For organisations that need to keep an eye on linear and networked utility infrastructure like electricity transmission lines, pipelines and water distribution systems, this deluge of data—derived from sensors—has posed a challenge and an opportunity. This paper presents a synthesis of the literature of scalable geospatial data platforms, unmanned aerial vehicle (UAV) and Light Detection and Ranging (LiDAR) inspection tools, and spatial statistical risk assessment techniques, and proposes an integrated system for monitoring and risk mitigation of utility assets. Evidence from the distributed computing research community with spatial data shows that using specialized data structures can cut the latency of a range query on a dataset of tens of millions of objects from about two hundred seconds to two seconds compared to traditional distributed processing engines. Comparative database benchmarking shows that document-oriented, non-relational stores can be 3 to 6 times faster on point and compound queries than relational spatial extensions, and that relational systems can be a similar speed on radius-based queries. Nearly ninety-three percent of defects are detected and nearly ninety-two percent are correctly identified by deep-learning inspection systems configured with UAV imagery, while the average error for defect-analysis is less than ten centimetres using the LiDAR-based clearance anomaly detection. The National-scale Flood Exposure characterization of critical infrastructure shows sectoral variation in flood exposure ranging from 2.7% to 27.1% of critical infrastructure within the 100-year floodplain, highlighting significant spatial disparities in exposure. The synthesis also demonstrates that cloud-based parallel processing reduces simulation runs from 12 days to two hours. The results are combined into a multi-layered ingest, distributed storage, spatial indexing, analytics, and risk-prioritisation architecture. The utility challenges, remaining interoperability, computational expense, and data governance issues are explored, as are implications for utility operators, regulators, and infrastructure planners.
Downloads
References
Armenakis, C., & Nirupama, N. (2013). Prioritization of disaster risk in a community using GIS. Natural Hazards, 66(1), 15–29. https://doi.org/10.1007/s11069-012-0167-8
Bartoszewski, D., Piórkowski, A., & Lupa, M. (2019). The comparison of processing efficiency of spatial data for PostGIS and MongoDB databases. In S. Kozielski, D. Mrozek, P. Kasprowski, B. Małysiak-Mrozek, & D. Kostrzewa (Eds.), Beyond databases, architectures and structures. Paving the road to smart data processing and analysis (pp. 291–302). Springer. https://doi.org/10.1007/978-3-030-19093-4_22
Bonvicini, S., Antonioni, G., & Cozzani, V. (2018). Assessment of the risk related to environmental damage following major accidents in onshore pipelines. Journal of Loss Prevention in the Process Industries, 56, 505–516. https://doi.org/10.1016/j.jlp.2018.11.005
Cevik, E., & Topal, T. (2003). GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology, 44(8), 949–962. https://doi.org/10.1007/s00254-003-0838-6
Chen, C., Yang, B., Song, S., Peng, X., & Huang, R. (2018). Automatic clearance anomaly detection for transmission line corridors utilizing UAV-borne LiDAR data. Remote Sensing, 10(4), 613. https://doi.org/10.3390/rs10040613
Eldawy, A., & Mokbel, M. F. (2015). SpatialHadoop: A MapReduce framework for spatial data. In 2015 IEEE 31st International Conference on Data Engineering (pp. 1352–1363). IEEE. https://doi.org/10.1109/ICDE.2015.7113382
Goodchild, M. F. (2020). Building geospatial infrastructure. Geo-spatial Information Science, 23(1), 1–9. https://doi.org/10.1080/10095020.2019.1698274
Hou, P., Yi, X., & Dong, H. (2020). A spatial statistic based risk assessment approach to prioritize the pipeline inspection of the pipeline network. Energies, 13(3), 685. https://doi.org/10.3390/en13030685
Huang, W., Raza, S. A., Mirzov, O., & Harrie, L. (2019). Assessment and benchmarking of spatially enabled RDF stores for the next generation of spatial data infrastructure. ISPRS International Journal of Geo-Information, 8(7), 310. https://doi.org/10.3390/ijgi8070310
Kadhim, N. R., Abdulrazzaq, K. A., & Hashim, A. (2021). The management of water distribution network using GIS application case study: AL-Karada area. Journal of Physics: Conference Series, 1895, 012038. https://doi.org/10.1088/1742-6596/1895/1/012038
Qiang, Y. (2019). Flood exposure of critical infrastructures in the United States. International Journal of Disaster Risk Reduction, 39, 101240. https://doi.org/10.1016/j.ijdrr.2019.101240
Rahman, M. R., & Thakur, P. K. (2018). Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India. The Egyptian Journal of Remote Sensing and Space Sciences, 21(S1), S37–S41. https://doi.org/10.1016/j.ejrs.2017.10.002
Rodriges Zalipynis, R. A. (2018). ChronosDB: Distributed, file based, geospatial array DBMS. Proceedings of the VLDB Endowment, 11(10), 1247–1261. https://doi.org/10.14778/3231751.3231754
Siddiqui, Z. A., Park, U., Lee, S., Jung, N., Choi, M., Lim, C., & Seo, J. (2018). Robust powerline equipment inspection system based on a convolutional neural network. Sensors, 18(11), 3837. https://doi.org/10.3390/s18113837
Wang, L., Chen, Z., Hua, D., & Zheng, Z. (2019). Semantic segmentation of transmission lines and their accessories based on UAV-taken images. IEEE Access, 7, 80829–80839. https://doi.org/10.1109/ACCESS.2019.2923024
Wang, Y., Chen, Q., Liu, L., Zheng, D., Li, C., & Li, K. (2017). Supervised classification of power lines from airborne LiDAR data in urban areas. Remote Sensing, 9(8), 771. https://doi.org/10.3390/rs9080771
Wu, Q., Yang, H., Wei, M., Remil, O., Wang, B., & Wang, J. (2018). Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud. ISPRS Journal of Photogrammetry and Remote Sensing, 143, 57–71. https://doi.org/10.1016/j.isprsjprs.2018.04.024
Yang, C., Yu, M., Hu, F., Jiang, Y., & Li, Y. (2017). Utilizing cloud computing to address big geospatial data challenges. Computers, Environment and Urban Systems, 61, 120–128. https://doi.org/10.1016/j.compenvurbsys.2016.10.010
Yang, J., & Kang, Z. (2018). Voxel-based extraction of transmission lines from airborne LiDAR point cloud data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(11), 3892–3904. https://doi.org/10.1109/JSTARS.2018.2869542
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
