AI-Enhanced Geomatics Engineering: Innovative Solutions and Applications Using ChatGPT, an Advanced AI Language Model

Year 2025, Volume: 10 Issue: 1, 36 - 45

Fatih Taktak

https://doi.org/10.26833/ijeg.1510209

Abstract

This study investigates the potential of the advanced AI model, ChatGPT, in providing innovative solutions and applications within Geomatics Engineering. ChatGPT enhances data accuracy, improves process efficiency, and supports project management by analyzing large geospatial datasets and interpreting complex information. It offers significant benefits to professionals and students, such as automating routine tasks, providing technical support, and contributing to education by developing users' skills. The case studies presented demonstrate tangible benefits in real-world Geomatics Engineering applications, including timely and budget-compliant project completion, improved accuracy in GIS and remote sensing data analysis, and increased efficiency. Additionally, the integration of ChatGPT has led to notable improvements in environmental monitoring and urban planning projects. In addition to its current applications, future research should focus on deeper integration of ChatGPT with existing technologies such as GIS and remote sensing systems. This will enable more sophisticated data analyses and foster the development of innovative projects in Geomatics Engineering. Furthermore, customizing ChatGPT to specific tasks within Geomatics, such as land use planning, topographic mapping, and boundary delineation, will lead to more precise and efficient solutions. As AI becomes more widespread in the industry, it is crucial to address data security and ethical concerns by establishing robust ethical frameworks that ensure responsible AI implementation and safeguard user data. These advancements will help maximize the potential of ChatGPT and similar AI models in transforming the future of Geomatics Engineering

Keywords

Geomatics Engineering, ChatGPT Applications, Generative Pre-trained Transformer, Multidisciplinary AI Applications, OpenAI

References

• Aluga, M. (2023). Application of CHATGPT in civil engineering. East African Journal of Engineering, 6(1), 104-112.
• Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623. https://doi.org/10.1145/3442188.3445922
• Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. https://doi.org/10.48550/arXiv.2108.07258
• Sanchez, T. W. (2023). Planning on the Verge of AI, or AI on the Verge of Planning. Urban Science, 7(3), 70.
• Alogayell, H. M., Kamal, A., Alkadi, I. I., Ramadan, M. S., Ramadan, R. H., & Zeidan, A. M. (2024). Spatial modeling of land resources and constraints to guide urban development in Saudi Arabia’s NEOM region using geomatics techniques. Frontiers in Sustainable Cities, 6, 1370881. https://doi.org/10.3389/frsc.2024.1370881
• Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., ... & Amodei, D. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
• Agbaje, T. H., Abomaye-Nimenibo, N., Ezeh, C. J., Bello, A., & Olorunnishola, A. (2024). Building Damage Assessment in Aftermath of Disaster Events by Leveraging Geoai (Geospatial Artificial Intelligence). World Journal of Advanced Research and Reviews, 23(1), 667-687. https://doi.org/10.30574/wjarr.2024.23.1.2000
• Doğan, Y., & Yakar, M. (2018). GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences, 3(2), 50-55. https://doi.org/10.26833/ijeg.378257
• Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681-694.
• Gabashvili, I. S. (2023). The impact and applications of ChatGPT: A systematic review of literature reviews. arXiv. https://arxiv.org/abs/2305.18086.
• Ghosh, J. K., & da Silva, I. (Eds.). (2019). Applications of geomatics in civil engineering: Select Proceedings of ICGCE 2018 (Vol. 33). Springer.
• Nistor, A. (2024). Development of Geospatial Technologies by Using Artificial Intelligence. FAIMA Business & Management Journal, 12(3), 61-68.
• Gill, S. S., & Kaur, R. (2023). ChatGPT: Vision and challenges. Internet of Things and Cyber-Physical Systems, 3, 262–271. https://doi.org/10.1016/J.IOTCPS.2023.05.004
• Hassani, H., & Silva, E. S. (2023). The role of ChatGPT in data science: How AI-assisted conversational interfaces are revolutionizing the field. Big Data and Cognitive Computing, 7(2), 62. https://doi.org/10.3390/bdcc7020062.
• Lewis, D. W. (2023). Open access: A conversation with ChatGPT. The Journal of Electronic Publishing, 26(1). https://doi.org/10.3998/jep.3891
• Nazir, A., & Wang, Z. (2023). A comprehensive survey of ChatGPT: advancements, applications, prospects, and challenges. Meta-radiology, 100022. https://doi.org/10.1016/j.metrad.2023.100022
• Tsai, M. L., Ong, C. W., & Chen, C. L. (2023). Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT. Education for Chemical Engineers, 44, 71-95. https://doi.org/10.1016/j.ece.2023.05.001
• Alshami, A., Elsayed, M., Ali, E., Eltoukhy, A. E., & Zayed, T. (2023). Harnessing the power of ChatGPT for automating systematic review process: Methodology, case study, limitations, and future directions. Systems, 11(7), 351.https://doi.org/10.3390/systems11070351
• Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023). Impact of the implementation of ChatGPT in education: A systematic review. Computers, 12(8), 153. https://doi.org/10.3390/computers12080153.
• OpenAI, R. (2023). Gpt-4 technical report. arxiv 2303.08774. View in Article, 2(5). https://doi.org/10.48550/arXiv.2303.08774
• Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI.
• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
• Rajesh, K., Sivapragasam, C., & Dargar, S. K. (2024). AI-Enhanced Personalized Learning Practices in Higher Engineering Institutes. Journal of Engineering Education Transformations, Volume No. 37, January 2024 Special Issue, eISSN 2394-1707.
• Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI models: A preliminary review. Future Internet, 15(6), 192. https://doi.org/10.3390/fi15060192.
• Saeed, M. M., Saeed, R. A., Ahmed, Z. E., Gaid, A. S., & Mokhtar, R. A. (2024). AI Technologies in Engineering Education. In AI-Enhanced Teaching Methods (pp. 61-87). IGI Global.
• Samaei, S. R., & Ghahfarrokhi, M. A. (2023). AI-Enhanced GIS Solutions for Sustainable Coastal Management: Navigating Erosion Prediction and Infrastructure Resilience. In 2th International Conference on Creative achievements of architecture, urban planning, civil engineering and environment in the sustainable development of the Middle East.
• Petrocchi, E., Tiribelli, S., Paolanti, M., Giovanola, B., Frontoni, E., & Pierdicca, R. (2023). GeomEthics: Ethical Considerations About Using Artificial Intelligence in Geomatics. In International Conference on Image Analysis and Processing (pp. 282-293). Cham: Springer Nature Switzerland.
• Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications: a review. Applied geomatics, 6, 1-15.
• Pierdicca, R., & Paolanti, M. (2022). GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data. Geoscientific Instrumentation, Methods and Data Systems Discussions, 2022, 1-35. https://doi.org/10.5194/gi-11-195-2022
• Tepeköylü, S. (2016). Mobil Lidar Uygulamaları, Veri İşleme Yazılımları ve Modelleri. Geomatik, 1(1), 1-7.
• Veiga de Moraes, R., & dos Santos, E. L. (2023). ChatGPT: Challenges and Benefits in Software Programming for Higher Education. Sustainability, 16(3), 1245. https://doi.org/10.3390/su16031245
• Gholami, A. (2024). Exploring drone classifications and applications: a review. International Journal of Engineering and Geosciences, 9(3), 418-442. https://doi.org/10.26833/ijeg.1428724
• Bakırman, T., & Sertel, E. (2023). A benchmark dataset for deep learning-based airplane detection: HRPlanes. International Journal of Engineering and Geosciences, 8(3), 212-223. https://doi.org/10.26833/ijeg.1107890
• Retscher, G., Gabela, J., & Gikas, V. (2022). PBeL—A novel problem-based (e-) learning for geomatics students. Geomatics, 2(1), 76-106. https://doi.org/10.3390/geomatics2010006
• Karadeniz, B., Pehlivan, B., Altıntaş, A. F. & Usta, S. (2024). Comparison of Network-RTK and PPP Technique in terms of Position Accuracy. Advanced Geomatics, 4(1), 31-36.
• Tabakoğlu, C. (2024). A Review: Detection types and systems in remote sensing. Advanced GIS, 4(2), 100–105. Retrieved from https://publish.mersin.edu.tr/index.php/agis/article/view/1560
• Makhmudov, R., & Teymurov, M. (2024). Importance of using GIS software in the process of application of Analogue terrains and Counter-approach technologies in water resources assessment. Advanced Remote Sensing, 4(1), 36-45
• Akça, Ş. (2024). Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index. Mersin Photogrammetry Journal, 6(2), 52-59. https://doi.org/10.53093/mephoj.1536466 Demirel, Y., & Türk, T. (2024). Assessment of the Location Accuracy of Points Obtained with A Low-Cost Lidar Scanning System and GNSS Method. Mersin Photogrammetry Journal, 6(2), 60-65. https://doi.org/10.53093/mephoj.1540159
• Pathak, S., Acharya, S., Bk, S., Karn, G., et al. (2024). UAV-based topographical mapping and accuracy assessment of orthophoto using GCP. Mersin Photogrammetry Journal, 6(1), 1-8. https://doi.org/10.53093/mephoj.1350426
• Ayalke, Z., & Şişman, A. (2024). Google Earth Engine kullanılarak makine öğrenmesi tabanlı iyileştirilmiş arazi örtüsü sınıflandırması: Atakum, Samsun örneği. Geomatik, 9(3), 375-390. https://doi.org/10.29128/geomatik.1472160
• Partigöç, N. S., & Dinçer, C. (2024). Coğrafi bilgi sistemleri (CBS) tabanlı afet risk analizi: Denizli ili örneği. Geomatik, 9(1), 27-44. https://doi.org/10.29128/geomatik.1261051
• Tawfeeq, A. F., & Atasever, Ü. H. (2023). Wetland monitoring by remote sensing techniques: A case study of Işıklı Lake. Advanced Remote Sensing, 3(1), 19-26.
• Ertürk, M. A., & Yalçın, C. (2022). Geochemical heat maps in complex geological structures via using QGIS: Maden (Elazığ) district. Advanced GIS, 2(2), 39–45.
• Erdem, N., & Demirel, A. . (2022). The Current State of Use of Satellite-Based Positioning Systems in Turkey. Advanced Geomatics, 2(1), 23–29.
• Siddique, I. (2022). Harnessing artificial intelligence for systems engineering: Promises and pitfalls. European Journal of Advances in Engineering and Technology, 9(9), 67-72.
• Soori, M., Jough, F. K. G., Dastres, R., & Arezoo, B. (2024). AI-based decision support systems in Industry 4.0, A review. Journal of Economy and Technology. https://doi.org/10.1016/j.ject.2024.08.005
• Yilmaz, H. M., Yakar, M., Mutluoglu, O., Kavurmaci, M. M., & Yurt, K. (2012). Monitoring of soil erosion in Cappadocia region (Selime-Aksaray-Turkey). Environmental Earth Sciences, 66, 75-81.
• Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69.
• Yakar, M., & Dogan, Y. (2019). 3D Reconstruction of Residential Areas with SfM Photogrammetry. In Advances in Remote Sensing and Geo Informatics Applications: Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018 (pp. 73-75). Springer International Publishing.