| Reference (APA) | Purpose & Value | Poster Section |
|---|---|---|
| Advanced Research Projects Agency-Energy. (n.d.). Subsurface intelligence for undergrounding operations: Rapid AI-based geophysical imaging and advanced visualization. https://arpa-e.energy.gov/programs-and-initiatives/search-all-projects/subsurface-intelligence-undergrounding-operations-rapid-ai-based-geophysical-imaging-and-advanced-visualization | Describes PNNL’s plan to turn geophysical data into AI digital twins and augmented-reality visuals. | Original Analysis |
| Ali, A. M. F., Khanjar, A. S. K., Azeev, A. A., & Chiadighikaobi, P. C. (2026). AI-driven and advanced sensing technologies for subsurface oil exploration. iScience, 29(8), Article 116869. https://doi.org/10.1016/j.isci.2026.116869 | Reviews AI and multi-sensor approaches to subsurface mapping, including potential safety benefits and limitations. | Ethical Concerns |
| Al-Yosef, M., & Obeidat, S. (2025, July 20). RespondAI: AI-orchestrated emergency response platform. The Chief AI Officer (CAIO) Hub. https://caio.waiu.org/p/respondai-ai-orchestrated-emergency-response-platform | Describes AI-assisted emergency triage and route planning as potential support for faster response coordination. | Social Perspectives; Ethical Concerns |
| Exodigo. (n.d.). Rotterdam 69kV substation. https://www.exodigo.com/projects/ng-rotterdam | Shows an AI-assisted multi-sensor mapping case that revealed undocumented underground utilities. | Original Analysis |
| Ghyselincks, S., Okhmak, V., Zampini, S., Turkiyyah, G., Keyes, D., & Haber, E. (2026). Synthetic geology: Structural geology meets deep learning. Journal of Geophysical Research: Machine Learning and Computation, 3(1), Article e2025JH000986. https://doi.org/10.1029/2025JH000986 | Demonstrates synthetic geological datasets and AI-generated 3D subsurface models from limited observations. | Technical Perspectives |
| Hu, D., Chen, J., & Li, S. (2022). Reconstructing unseen spaces in collapsed structures for search and rescue via deep learning based radargram inversion. Automation in Construction, 140, Article 104380. https://doi.org/10.1016/j.autcon.2022.104380 | Core rescue study: GAN-enhanced GPR scans, 2D void reconstruction, and reported detection results. | Introduction; Development; Evidence |
| Hu, D., Li, S., Chen, J., & Kamat, V. R. (2019). Detecting, locating, and characterizing voids in disaster rubble for search and rescue. Advanced Engineering Informatics, 42, Article 100974. https://doi.org/10.1016/j.aei.2019.100974 | Establishes radar-based detection and measurement of voids in collapsed rubble before deep-learning methods. | Introduction; Development |
| IDS GeoRadar. (n.d.). Intelligent underground mapping with AiMaps. https://idsgeoradar.com/about-us/case-studies/ground-penetrating-radar-gpr-case-studies/usecase--aimaps-udm | Real-world AI-assisted GPR mapping at UTAS Stadium, which is a utility-mapping example. | Evidence; Case Study |
| Zhang, D., Wang, Z., Tang, Y., Pan, S., & Pan, T. (2024). Three-dimensional reconstruction of retaining structure defects from crosshole ground penetrating radar data using a generative adversarial network. Remote Sensing, 16(21), Article 3995. https://doi.org/10.3390/rs16213995 | Shows GAN-based 3D reconstruction of structural defects; supports the 2024 milestone and future uses. | Development; Future Potential |
| Zou, L., Li, Y., Munisami, K., & Alani, A. M. (2025). Bridging theory and practice: A review of AI-driven techniques for ground penetrating radar interpretation. Applied Sciences, 15(15), Article 8177. https://doi.org/10.3390/app15158177 | Reviews data scarcity, limited generalization, and deployment challenges in AI-assisted GPR. | Ethical Concerns |