International Journal of Analytical, Experimental and Finite Element Analysis
Volume 12 · Issue 4 · December 2025 · pp. 19–43
Review Article · Peer Reviewed
Received: September 25, 2025 · Accepted: November 27, 2025 · Published: December 30, 2025
DOI: 10.26706/ijaefea.4.12.20251201
Open Access · CC BY 4.0

A Comprehensive Review of Remote Sensing Applications for Monitoring Soil Properties and Geotechnical Performance in Construction Projects

Ahmed Muhammad Dakhil*

College of Computer Science and Information Technology, Wasit University, Wasit, Alkut, Iraq.

*Corresponding author: Ah.adkheel@uowasit.edu.iq

Download PDF Cite this article Certificate Volume 12 Issue 4

Abstract

This review article discusses the novel application of remote sensing (RS) technology in geotechnical monitoring and soil property evaluation in modern construction. Although standard geotechnical tests are precise, they are frequently not able to consider spatial variability and provide real-time data on massive projects based on point sampling and manual inspection. To address these issues, this paper discusses various next-generation sensing systems, including satellite Interferometric Synthetic Aperture Radar (InSAR), aerial Light Detection and Ranging (LiDAR), and Unmanned Aerial Vehicles (UAVs) with hyperspectral and thermal sensors. Findings of the study indicate that such platforms have a significant enhancement of precision of monitoring of crucial geotechnical parameters (e.g., soil moisture, compaction, and soil deformation) and, consequently, enhance operational efficiency and cost-effectiveness. Moreover, it can be combined with Artificial Intelligence and Machine Learning methods to make accurate predictions of soil behaviour and landslides. The review indicates that the move towards Smart Geotechnics where remote sensing data are used alongside traditional borehole samples and laboratory testing is vital in strengthening and maintaining infrastructure. Remote sensing bridges the gap between historical geotechnical information and live monitoring, offering a holistic approach to risk management and improved project outcomes.

Keywords

Soil properties Remote sensing Geotechnical monitoring Construction projects Review

References

  1. G. Albeaino and M. Gheisari, "Trends, benefits, and barriers of unmanned aerial systems in the construction industry: a survey study in the United States," J. Inf. Technol. Constr., vol. 26, pp. 84–109, 2021.
  2. M. Al-Arafat et al., "Geotechnical challenges in urban expansion: Addressing soft soil, groundwater, and subsurface infrastructure risks in mega cities," J. Geotech. Eng., 2021.
  3. N. Casagli et al., "Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning," Remote Sensing, vol. 17, no. 1, pp. 1–23, 2020.
  4. P. J. V. D’Aranno et al., "Remote sensing technologies for linear infrastructure monitoring," Int. Arch. Photogrammetry, Remote Sens. Spatial Inf. Sci., XLII-2/W11, pp. 443–450, 2020.
  5. A. A. Firoozi and A. A. Firoozi, "Application of machine learning in geotechnical engineering for risk assessment," in Machine Learning and Data Mining Annual Volume 2023, pp. 112–125, 2023.
  6. A. A. Firoozi and A. A. Firoozi, "Smart geotechnics: Enhancing infrastructure resilience with IoT and AI," Geotechnical Data Analytics, 2023.
  7. L. Hu et al., "Updating active deformation inventory maps in mining areas by integrating InSAR and LiDAR datasets," Remote Sensing, vol. 15, no. 674, 2022.
  8. K. Lee, J. Park, and G. Hong, "Prediction of water content in subgrade soil in road construction using hyperspectral information obtained through UAV," Automation in Construction, vol. 158, p. 105214, 2022.
  9. M. Rezania, "Editorial: Bridging tradition and innovation: A prelude to the Journal of Machine Learning and Data Science in Geotechnics," J. Mach. Learn. Data Sci. Geotechnics, vol. 1, no. 1, pp. 1–5, 2021.
  10. B. Satipaldy et al., "Geotechnology in the age of AI: The convergence of geotechnical data analytics and machine learning," Geotech. Geol. Eng., vol. 39, pp. 456–472, 2023.
  11. M. A. Shahin, "Progression of artificial intelligence/machine learning in geotechnical engineering," J. Machine Learn. Data Sci. Geotechnics, vol. 1, pp. 15–30, 2022.
  12. M. Shi et al., "Intelligent compaction system for soil-rock mixture subgrades: Real-time moisture-CMV fusion control and embedded edge computing," Automation in Construction, vol. 169, p. 105822, 2021.
  13. T. K. Soon et al., "Challenges and barriers for unmanned aerial vehicle implementation in Malaysian infrastructure projects," Int. J. Built Environ. Sustain., vol. 11, pp. 45–58, 2021.
  14. V. Vivaldi et al., "Airborne combined photogrammetry—infrared thermography applied to landslide remote monitoring," Remote Sensing, vol. 14, no. 5410, 2022.
  15. W. Xiao and W. Tian, "Remote sensing identification and hazard assessment methods for spoil sites," Remote Sensing, vol. 16, p. 342, 2022.
  16. A. S. Almohsen, "Challenges facing the use of remote sensing technologies in the construction industry: A review," J. Constr. Eng. Technol., vol. 13, pp. 145–152, 2021.
  17. S.-E. Chen, P. Sumitro, and C. Boyle, "Remote sensing techniques for geo-problem applications," Remote Sensing Techniques, vol. 22, no. 3, pp. 98–106, 2022.
  18. S. Glinka, "Sat4BIM4D: The concept of using satellite remote sensing to monitor construction progress in conjunction with BIM," J. Constr. Innov., vol. 35, pp. 54–65, 2023.
  19. J. L. Huere-Peña et al., "Innovations in soil health monitoring: Role of advanced sensor technologies and remote sensing," Sensors, vol. 24, no. 3584, 2023.
  20. Á. Ní Bhreasail et al., "Remote sensing for proactive geotechnical asset management of England’s strategic roads," Remote Sensing, pp. 59–73, 2022.
  21. "RS & GIS in Geotechnical Engineering: A comprehensive overview and applications," Geotechnical Engineering Review, vol. 29, no. 6, pp. 221–233, 2023.
  22. R. Salunke et al., "Near-surface soil moisture characterization in Mississippi’s highway slopes using machine learning methods and UAV-captured infrared and optical images," Sensors, vol. 23, no. 7482, 2023.
  23. M. Tzouvaras et al., "Synergy of advanced processing techniques using Copernicus SAR and optical satellite imagery to detect ground displacements," Int. Arch. Photogrammetry, Remote Sens. Spatial Inf. Sci., 2023.
  24. A. W. Lyda et al., "Airborne light detection and ranging derived deformation from the Mw 6.0 South Napa Earthquake," Int. Arch. Photogrammetry, Remote Sens. Spatial Inf. Sci., pp. 411–417, 2022.
  25. T. Wang et al., "The rise, collapse, and compaction of Mt. Mantap from the North Korean nuclear test," Science, vol. 361, pp. 166–170, 2018.