Land cover analysis of two university campuses: Examination over satellite images by Chat GPT

Year 2025, Volume: 10 Issue: 1, 124 - 136

Ekrem Bahadır Çalışkan

https://doi.org/10.26833/ijeg.1504721

Abstract

Land Use and Land Cover Analysis are important in detecting the changes in urban areas, rural areas, and focused lands like university campuses. The availability of high-quality satellite images from diverse time sequences makes evaluations for changes by time possible. The analysis methods include insights from remote sensing fields to Artificial intelligence (AI) tools. AI has been significantly developed in the last decades in various fields, and applications of AI on satellite imagery analysis are being influenced. This study explores the capability of Chat GPT, which is one of the leading Language Models and can generate prompts and analysis due to inputs for Land Cover and Use Analysis. Firstly, an unstructured conversation with Chat GPT was held, and then, considering this experience, a land cover change analysis was executed for two university campuses. Besides, the analysis was also re-executed in Colab with codes generated by Chat GPT to seek differences. Two university campuses, Erzurumm Technical University and Adıyaman University, founded in the last two decades, were utilized as case studies. Chat GPT explained the steps and procedure of the analysis in detail generated codes in a defined framework. The analysis results have problems in classifying the land cover; however, the imperviousness change analysis shows most of the construction improvement. The experiment and findings have important implications for future research in Land Cover analysis implementing AI tools.

Keywords

Chat GPT, Colab, Construction Track, Land Cover, University Campus

References

• Macarringue, L. S., Bolfe, É. L., & Pereira, P. R. M. (2022). Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review. Journal of Geographic Information System, 14(01), 1–28. https://doi.org/10.4236/jgis.2022.141001
• Kamrowska-Załuska, D. (2021). Impact of ai-based tools and urban big data analytics on the design and planning of cities. Land, 10(11). https://doi.org/10.3390/land10111209
• Garg, L., Shukla, P., Singh, S. K., Bajpai, V., & Yadav, U. (2019). Land use land cover classification from satellite imagery using mUnet: A modified UNET architecture. VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 4(Visigrapp), 359–365. https://doi.org/10.5220/0007370603590365
• Basheer, S., Wang, X., Farooque, A. A., Nawaz, R. A., Liu, K., Adekanmbi, T., & Liu, S. (2022). Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques. Remote Sensing, 14(19), 1–18. https://doi.org/10.3390/rs14194978
• Agapiou, A. (2021). Land cover mapping from colorized CORONA archived greyscale satellite data and feature extraction classification. Land, 10(8), 1–14. https://doi.org/10.3390/land10080771
• Yuan, Z., Xiong, Z., Mou, L., & Zhu, X. X. (2024). ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models. arxiv.
• Kotan, B., Tatmaz, A., Kılıç, S., & Erener, A. (2022). LST change for 16-year period for different land use classes. Advanced Remote Sensing, 1(1), 38–45.
• Aliyazicioglu, K., Beker, F., Topaloglu, R. H., Bilgilioglu, B. B., & Comert, R. (2021). Temporal monitoring of land use/land cover change in Kahramanmaraş city center. Turkish Journal of Engineering, 5(3), 134–140. https://doi.org/10.31127/tuje.707156
• Yilmaz, O. S., Gülgen, F., Üngör, R. G., & Kadi, F. (2018). Uzaktan Algılama Teknikleri İle Arazi Kullanım Değişimi nin İncele nmesi : Köprübaşı İlçesi Örneği Investigation of Land Use Change with Remote Sensing Techniques , The Case of Köprübaşı District. Geomatik, 3(3), 2332--241. https://doi.org/10.29128/geomatik.410987
• Ahady, A. B., & Kaplan, G. (2022). Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul. International Journal of Engineering and Geosciences, 7(1), 24–31. https://doi.org/10.26833/ijeg.860077
• Nasiri, V., Deljouei, A., Moradi, F., Sadeghi, S. M. M., & Borz, S. A. (2022). Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods. Remote Sensing, 14(9). https://doi.org/10.3390/rs14091977
• Doğan, S., & Buğday, E. (2018). Arazi Örtüsü ve Kullanımının Zamansal ve Mekânsal Değişiminin Yapay Sinir Ağları ile Modellenmesi: Kastamonu Örneği. Journal of Bartin Faculty of Forestry, 20(3), 653–663. https://doi.org/10.24011/barofd.467974
• Demirel, Y., & Türk, T. (2024). Automatic detection of active fires and burnt areas in forest areas using optical satellite imagery and deep learning methods. Mersin Photogrammetry Journal, 6(2), 66–78. https://doi.org/10.53093/mephoj.1575877
• Yakar, M., & Doğan, Y. (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
• Erdoğan, A., Görken, M., Kabadayi, A., & Temizel, S. (2022). Evaluation of green areas with remote sensing and GIS : A case study of Yozgat city center. Advanced Remote Sensing, 2, 58–65.
• Çorakbaş, F. K., & Bektöre, E. M. (2022). A GIS-based method for researching the historical and architectural heritage of the mountainscapes : The c ase of Uludağ / Olympus Monasteries. Cultural Heritage and Science, 3(2), 73–85.
• Aroma, J., & Raimond, K. (2016). An Overview of Technological Revolution in Satellite Image Analysis. Journal of Engineering and Technology, 9(December), 1–6.
• Mansour, R. F., & Alabdulkreem, E. (2023). Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification. Computer Systems Science and Engineering, 44(2), 1161–1169. https://doi.org/10.32604/csse.2023.023307
• Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123. 1520–1528.
• Dos, M. E. (2022). Determination of city change in satellite images with deep learning structures. Advanced Remote Sensing, 2(1), 16–22.
• Tabakoglu, C. (2024). A Review : Detection types and systems in remote sensing. Advanced GIS, 4(2), 100–104.
• Kaynarca, M. (2023). Extraction of building areas with the use of unmanned aerial vehicles , calculation of building roof slopes. Advanced UAV, 3(2), 136–141.
• KARATAŞ, L., & DAL, M. (2023). Deterioration analysis of historical village house structure in Mersin Kanlıdivane archaeological area by UAV method. Mersin Photogrammetry Journal, 5(1), 32–41. https://doi.org/10.53093/mephoj.1290231
• Oruç, M. E. (2021). The possibilities of data usage obtained from UAV. Advanced UAV, 1(1), 15–23.
• Yaşar, O., Yağcı, A. L., Üniversitesi, T., Fakültesi, M., & Bölümü, H. M. (2023). Investigation of the accuracy of ground reference datasets using multi-temporal Sentinel-2 satellite images: A case study with barley and wheat crops. Geomatik, 8(3), 277–292.
• Göksel, Ç., & Balçık, F. B. (2019). Land Use and Land Cover Changes Using Spot 5 Pansharpen Images; a Case Study in Akdeniz District, Mersin-Turkey. Turkish Journal of Engineering, 3(1), 32–38. https://doi.org/10.31127/tuje.444685
• Güven, O., Yıldırım, Ü., Güler, C., & Kurt, M. A. (2024). Land use and land cover classes affected by the possible sea level rise in Mersin city center (Türkiye). Advanced GIS, 4(1), 15–23.
• Yakup, A. E., & Ayazlı, İ. E. (2022). Investigating changes in land cover in high-density settlement areas by protected scenario. International Journal of Engineering and Geosciences, 7(1), 1–8. https://doi.org/10.26833/ijeg.850247
• Hall, O., Dompae, F., Wahab, I., & Dzanku, F. M. (2023). A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications. Journal of International Development, 35(7), 1753–1768. https://doi.org/10.1002/jid.3751
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
• Mondal, S., Das, S., & Vrana, V. G. (2023). How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life. Technologies, 11(2). https://doi.org/10.3390/technologies11020044
• Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(22). https://doi.org/10.1186/s41239-023-00392-8
• Biswas, S. S. (2023). Potential Use of Chat GPT in Global Warming. Annals of biomedical engineering. https://doi.org/10.1007/s10439-023-03171-8
• Jaruga-Rozdolska, A. (2022). Artificial intelligence as part of future practices in the architect’s work: MidJourney generative tool as part of a process of creating an architectural form. Architectus, 3(71), 95–104. https://doi.org/10.37190/arc220310
• Caliskan, E. B. (2023). Interview with Chat GPT to Define Architectural Design Studio Work: Possibilities, Conflicts and Limits. Journal of Design Studio, 5(1), 57–71. https://doi.org/10.46474/jds.1267485
• Abate, N., Visone, F., Sileo, M., Danese, M., Minervino Amodio, A., Lasaponara, R., & Masini, N. (2023). Potential Impact of Using ChatGPT-3.5 in the Theoretical and Practical Multi-Level Approach to Open-Source Remote Sensing Archaeology, Preliminary Considerations. Heritage, 6(12), 7640–7659. https://doi.org/10.3390/heritage6120402
• Mema, B., Basholli, F., & Hyka, D. (2024). Learning transformation and virtual interaction through ChatGPT in Albanian higher education. Advanced Engineering Science, 4, 130–140.
• Zhang, C., & Wang, S. (2023). Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data. arxiv.
• Jiang, Y., & Yang, C. (2024). Is ChatGPT a Good Geospatial Data Analyst? Exploring the Integration of Natural Language into Structured Query Language within a Spatial Database. ISPRS International Journal of Geo-Information, 13(1). https://doi.org/10.3390/ijgi13010026
• Wang, S., Hu, T., Xiao, H., Li, Y., Zhang, C., Ning, H., … Ye, X. (2024). GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review. International Journal of Digital Earth, 17(1), 1–21. https://doi.org/10.1080/17538947.2024.2353122
• Salihoğlu, T., Salihoğlu, G., Özyılmaz Küçükyağcı, P., & Yıldız, M. (2021). Kampüs Tasarımının Öğrencilerin Kampüs Yaşamının Kalitesine Etkisi: Gebze Teknik Üniversitesi Çayırova Kampüsü Master Planı Örneği. Kent Akademisi, 14(4), 975–994. https://doi.org/10.35674/kent.909791
• 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.
• Türeyen, M. N. (2002). Yükseköğretim Kurumları-Kampüsler. İstanbul: Tasarım Yayın Grubu.
• Erkman, U. (1990). Büyüme ve Gelişme Açısından Üniversite Kampüslerinde Planlama ve Tasarım Sorunları. İstanbul: İTÜ Mimarlık Fakültesi.
• Çalışkan, E. B. (2023). Erzurum Teknik Üniversitesi Yerleşkesi: Tasarım Kurgusu ve Gelişimi. In L. G. Kaya (Ed.), Mimarlık, Planlama ve Tasarım Alanında Uluslararası Araştırmalar (pp. 189–210). Ankara: Platanus Publishing. https://doi.org/10.5281/zenodo.7744333
• Çalışkan, E. B. (2023). Adıyaman University Campus Plan : Design , Development and Snapshot after Earthquake. Journal of Architecture, Arts and Heritage, 2(3), 1–23