Pipelines are essential for energy supply stability. However, many of them face both natural and anthropogenic hazards. For pipeline hazard assessment, remote sensing provides regular, large-scale analysis, especially in hard-to-access areas. Cloud-based platforms like Google Earth Engine (GEE) enable efficient processing of large geospatial datasets. Also, GEE allows creating a web application with a custom GUI for specific thematic tasks. Thus, the aim of this study is to develop a GEE-based web application for automated pipeline hazard assessment utilizing multi-source remote sensing data. The proposed framework incorporates both natural and infrastructure parameter groups. The natural group includes terrain slope from SRTM V3, land surface temperature from MODIS, and accumulated precipitation from ERA5-Land. The infrastructure group consists of distance layers to roads, railways, and power transmission lines. Processing of this data implies normalization of input parameters and computing their weighted sum with expert-defined weighted coefficients. For the experiment, the developed application was tested on a section of the Urengoy–Pomary–Uzhgorod pipeline in the mountainous terrain of the Ukrainian Carpathians. As a result, the obtained pipeline hazard map indicates the following distribution of hazard classes: 13.93% low hazard, 49.11% medium hazard, 34.50% high hazard, and 2.46% very high hazard. For validation, the obtained map was overlaid with 198 documented landslides. The analysis demonstrated high consistency, since most landslides occurred within the high and very high hazard zones. Thus, the developed tool can support spatial decision-making for pipeline monitoring and hazard mitigation.
Apostolov, A. A., Yelistratova, L. A., Romanciuc, I. F., & Zakharchuk, I. (2021). Identifying potential landslide areas by employing the erosion relief index and meteorological criteria in Ukraine. Revue Roumaine de Géographie / Romanian Journal of Geography, 65(2), 125–141. http://www.rjgeo.ro/issues/revue%20roumaine%2065_2/apostolov%20et%20al..pdf
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2). https://doi.org/10.1029/2005rg000183
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Mahmoud, A. A., & Hasan, R. (2025). A comprehensive survey on pipeline monitoring technologies: advancements, challenges, market opportunities and future directions. Journal of Pipeline Science and Engineering, 100353. https://doi.org/10.1016/j.jpse.2025.100353
Muñoz Sabater, J. (2019). ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.e2161bac
Popov, M. O., Тopolnytskyi, М. V., Titarenko, O. V., Stankevich, S. Α., & Аndreiev, А. A. (2020). Forecasting gas and oil potential of subsoil plots via co-analysis of satellite, geological, geophysical and geochemical information by means of subjective logic. WSEAS TRANSACTIONS ON COMPUTER RESEARCH, 8, 90–101. https://doi.org/10.37394/232018.2020.8.11
Popov, M., Stankevich, S., Kozlova, A., Piestova, I., Lubskiy, M., Titarenko, O., Svideniuk, M., Andreiev, A., Lysenko, A., & Singh, S. K. (2021). Long-Term satellite data time series analysis for land degradation mapping to support sustainable land management in Ukraine. In Advances in geographical and environmental sciences (pp. 165–189). https://doi.org/10.1007/978-981-16-4768-0_11
Shtohryn, L., Anikeyev, S., Kuzmenko, E., & Bagriy, S. (2021). Reflection of the activity of landslide processes in the regional gravitational and magnetic fields (on the example of the Transcarpathian region). GEODYNAMICS, 1(30)2021(1(30)), 65–77. https://doi.org/10.23939/jgd2021.01.065
Silva, A., Evangelista, L., Ferreira, C., Valença, J., & Mendes, M. P. (2024). Towards resilient pipeline infrastructure: lessons learned from failure analysis. Discover Applied Sciences, 6(11). https://doi.org/10.1007/s42452-024-06273-7
Titarenko, O., Andreiev, A., Artiushyn, L., & Bondarenko, A. (2026). Integrated geospatial assessment of geodynamic hazard along the pipelines in the Ukrainian Carpathians. Geofizicheskiy Zhurnal, 48(2). https://doi.org/10.24028/gj.v48i2.353442
Wan, Z., Hook, S., & Hulley, G. (2021). MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V061 [Dataset]. In Earth Observing System Data and Information System. https://doi.org/10.5067/modis/mod11a2.061
Zhang, H., Feng, Q., Yan, B., Zheng, X., Yang, Y., Chen, J., Zhang, H., & Liu, X. (2023). State of the art of oil and gas pipeline vulnerability assessments. Energies, 16(8), 3439. https://doi.org/10.3390/en16083439