A case study: Making decisions for sustainable university campus planning using GeoAI

Year 2025, Volume: 10 Issue: 1, 22 - 35

Esra Kumaş , Damla Aslan

https://doi.org/10.26833/ijeg.1506265

Abstract

The increasing availability of geospatial data, the development of AI and the availability of large computational capacities have contributed to the growing importance and potential of GeoAI. GeoAI has an important role in advancing traditional AI technologies and developing new ways to solve problems posed by the massive, complex, diverse and ever-increasing nature of geospatial data. Geospatial data is widely used in many scientific fields and applications such as smart cities, transportation, business, public health, public safety, resilience to natural disasters, climate change and many more. Especially because of the huge growth in population and the need to analyse United Nations sustainability impacts oblige the experts to utilize GeoAI. The future vision, sustainable cities and green campuses provide acceleration in the IoT and planning with GeoAI. In this scope this preceding enlightens campus planning by GeoAI as beginning step of the digital twin mechanism. This article is applied to: (1) GeoAI and campus planning techniques; (2) QGIS and KooMap utilization for AI based image recognition; (3) interpreting the output of GeoAI based map and giving sustainability recommendations related with campus planning; (4) Strengths and shortcomings of the research. GeoAI usage is proven as a beneficial way to make decisions on university campus by using automatically recognized satellite images. It is the first step for digital campus management system.

Keywords

Smart City, Smart Campus, Digital Twin, GeoAI, Urban Planning

References

• Ullah A, Anwar SM, Li J, Nadeem L, Mahmood T, Rehman A, Saba T. Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex and Intelligent Systems. 2024;10(1). 1607-1637. doi.org/10.1007/s40747-023-01175-4
• Li W, Batty M, Goodchild MF. Real-time GIS for smart cities. In International Journal of Geographical Information Science. 2020;34(2):311-324. doi.org/10.1080/13658816.2019.1673397
• Hoang GTT, Dupont L, Camargo M. Application of decision-making methods in smart city projects: A systematic literature review. Smart Cities. 2019;2(3):433-452. doi.org/10.3390/smartcities2030027
• Dong ZY, Zhang Y, Yip C, Swift S, Beswick K. Smart campus: definition, framework, technologies, and services. IET Smart Cities. 2020;2(1):43-54. doi.org/10.1049/iet-smc.2019.0072
• Stephenson J, Yorke M. Capability and quality in higher education. Routledge; 2013. ISBN: 9781315042046. doi.org/10.4324/9781315042046
• Mortaheb R, Jankowski P. Smart city re-imagined: City planning and GeoAI in the age of big data. Journal of Urban Manag. 2023;12(1):4-15. doi: 10.1016/j.jum.2022.08.001.
• Costa DG, Bittencourt JCN, Oliveira F, Peixoto JPJ, Jesus TC. Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches. Sustainability. 2024;16(2):640.
• Fauzi C. A Review Geospatial Artificial Intelligence (Geo-AI): Implementation of Machine Learning on Urban Planning. In: International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023); 2024; Atlantis Press. p. 311-329.
• Mahdavifard M, Ahangar SK, Feizizadeh B, Kamran KV, Karimzadeh S. Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and optical images on Google Earth Engine. International Journal of Engineering and Geosciences. 2023;8(3):239-50.
• Başara A. C., Şişman Y. Landslide susceptibility mapping of Tokat (Turkey) province using weight of evidence and random forest. Advanced GIS. 2021;1(1);1-7.
• Memduhoğlu A. Identifying impervious surfaces for rainwater harvesting feasibility using unmanned aerial vehicle imagery and machine learning classification. Advanced GIS. 2023;3(1); 01-06.
• Yalpır Ş., Yalpır E. Comparative evaluation of the performance of different regression models in land valuation. Advanced GIS. 2024;4(1);10-14.
• Xiao Z, Wang Y, Fu K, Wu F. Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS International Journal of Geo-Information. 2017;6(2):57. doi: 10.3390/ijgi6020057.
• Türk S. T., Balçık F. Rastgele orman algoritması ve Sentinel-2 MSI ile fındık ekili alanların belirlenmesi: Piraziz Örneği. Geomatik. 2023; 8(2):91-98.
• Duman H. S., Başaraner M. Şekil göstergeleri ve topluluk öğrenmesi sınıflandırma algoritmaları ile bina detaylarının şekil karmaşıklık analizi. Geomatik. 2022; 7(3):197-208.
• Günen MA., Beşdok E. Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener. Mersin Photogrammetry Journal. 2023;5(1);1-9.
• Bakırman T, Sertel E. A benchmark dataset for deep learning-based airplane detection: HRPlanes. International Journal of Engineering and Geosciences. 2023;8(3):212-23.
• Dos ME. Determination of city change in satellite images with deep learning structures. Advanced Remote Sensing. 2022;2(1); 16-22.
• Gong X, Yao Q, Wang M, Lin Y. A Deep Learning Approach for Oriented Electrical Equipment Detection in Thermal Images. IEEE Access. 2018;6:41590-41597. doi: 10.1109/ACCESS.2018.2859048.
• Çetin ŞB. Real-ESRGAN: A deep learning approach for general image restoration and its application to aerial images. Advanced Remote Sensing. 2023; 3(2); 90-99.
• Duan Y, Liu S, Hu C, Hu J, Zhang H, Yan Y, Tao N, Zhang C, Maldague X, Fang Q, Ibarra-Castanedo C, Chen D, Li X, Meng J. Automated defect classification in infrared thermography based on a neural network. NDT E International. 2019;107:102147. doi: 10.1016/j.ndteint.2019.102147.
• Yang X, Ye Y, Li X, Lau RYK, Zhang X, Huang X. Hyperspectral image classification with deep learning models. IEEE Transactions on Geoscience and Remote Sensing. 2018;56(9):5408-5423. doi: 10.1109/TGRS.2018.2815613.
• Wen C, Sun X, Li J, Wang C, Guo Y, Habib A. A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. 2019;147:178-192. doi: 10.1016/j.isprsjprs.2018.10.007.
• Uzar M., Öztürk Ş., Bayrak OC., Arda T., & Öcalan NT. Performance analysis of YOLO versions for automatic vehicle detection from UAV images. Advanced Remote Sensing. 2021;1(1);16-30.
• Akram MW, Li G, Jin Y, Chen X, Zhu C, Ahmad A. Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning. Solar Energy. 2020;198:175-186. doi: 10.1016/j.solener.2020.01.055.
• Demirel Y., Türk, T. Automatic detection of active fires and burnt areas in forest areas using optical satellite imagery and deep learning methods. Mersin Photogrammetry Journal. 2024; 6(2); 66-78.
• Balado J, Sousa R, Díaz-Vilariño L, Arias P. Transfer Learning in urban object classification: Online images to recognize point clouds. Automation in Construction 2020;111:103058. doi: 10.1016/j.autcon.2019.103058.
• Vopham T, Hart JE, Laden F, Chiang YY. Emerging trends in geospatial artificial intelligence (GeoAI): Potential applications for environmental epidemiology. In Environmental Health: A Global Access Science Source. 2018;17(1):1-6. doi: 10.1186/s12940-018-0386-x.
• Janowicz K, Gao S, McKenzie G, Hu Y, Bhaduri B. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. In International Journal of Geographical Information Science. 2020;34(4):625-636. doi: 10.1080/13658816.2019.1684500.
• Cugurullo F. Urban Artificial Intelligence: From Automation to Autonomy in the Smart City. Frontiers in Sustainable Cities. 2020;2(38). doi: 10.3389/frsc.2020.00038.
• Arundel S. T., Li W., Wang S. GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning. Transactions in GIS. 2020;24(3):556-572. doi: 10.1111/tgis.12633.
• Döllner J. Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2020;88(1):15-24.
• Gao S. Geospatial artificial intelligence (GeoAI). Vol. 10. New York: Oxford University Press; 2021.
• Liu P, Biljecki F. A review of spatially-explicit GeoAI applications in Urban Geography. International Journal of Applied Earth Observation and Geoinformation. 2022;112:102936. doi: 10.1016/j.jag.2022.102936.
• Pierdicca R, Paolanti M. GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data. Geoscientific Instrumentation, Methods and Data Systems. 2022;11(1):195-218. doi: 10.5194/gi-11-195-2022.
• Alastal AI, Shaqfa AH. GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case). Journal of Data Analysis and Information Processing. 2022;10(2):110-126.
• Ang KLM, Seng JKP, Ngharamike E, Ijemaru GK. Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches. ISPRS International Journal of Geo-Information. 2022;11(2):85. doi: 10.3390/ijgi11020085.
• Song Y, Kalacska M, Gašparović M, Yao J, Najibi N. Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping. International Journal of Applied Earth Observation and Geoinformation. 2023;120:103300. doi: 10.1016/j.jag.2023.103300.
• Instituting UI GreenMetric, Guidelines. Instituting UI GreenMetric, Guidelines. 2024. Available from: https://greenmetric.ui.ac.id/publications/guidelines/2024/english [Accessed 20 Aug 2024].
• 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..
• Karasan A, Kutlu Gündoğdu F, Aydın S. Decision-making methodology by using multi-expert knowledge for uncertain environments: Green metric assessment of universities. Environment, Development and Sustainability. 2023;25(8):7393-7422.
• Boiocchi R, Ragazzi M, Torretta V, Rada E. Critical Analysis of the GreenMetric World University Ranking System: The Issue of Comparability. Sustainability. 2023;15(2):1343. doi: 10.3390/su15021343.
• Sari MW, Ciptadi PW, Hardyanto RH. Study of smart campus development using internet of things technology. In: IOP Conference Series: Materials Science and Engineering. 2017;190(1):012032. IOP Publishing.
• Galleli B, Teles NEB, Santos JARD, Freitas-Martins MS, Hourneaux Junior F. Sustainability university rankings: a comparative analysis of UI GreenMetric and the Times Higher Education World University Rankings. International Journal of Sustainability in Higher Education. 2022;23(2):404-25.
• Atıcı KB, Yasayacak G, Yıldız Y, Ulucan A. Green University and academic performance: an empirical study on UI GreenMetric and World University Rankings. Journal of Cleaner Production. 2020;250:119517. https://doi.org/10.1016/j.jclepro.2020.125289
• Ariesanti A, Sutanto A, Bidayati U. Online disclosure of university environmental responsibility: A case of Indonesia. In: 3rd International Conference on Accounting, Management and Economics 2018 (ICAME 2018); 2018 Dec 5-7; Atlantis Press; 2019. p. 401-9.
• Rochim AF, Sari RF. Study on the correlation of web repository ranking to the green campus ranking of Indonesian Universities. In: Proceedings of the International Conference on Information Technology and Electrical Engineering (ICITACEE); 2015 Nov 18-19; IEEE. p. 148-52. doi.org/10.1109/ICITACEE.2015.7437788
• Polin K, Yigitcanlar T, Limb M, Washington T. The making of smart campus: a review and conceptual framework. Buildings. 2023;13(4):891. doi:10.3390/buildings13040891.
• Mahariya, S. K., Kumar, A., Singh, R., Gehlot, A., Akram, S., Twala, B., Iqbal, M. I., & Priyadarshi, N. (2023). Smart Campus 4.0: Digitalization of University Campus with Assimilation of Industry 4.0 for Innovation and Sustainability. Applied Sciences and Engineering Technology, 32(1), 120-138. doi.org/10.37934/araset.32.1.120138
• Silva-da-Nóbrega, P. I., Chim-Miki, A. F., & Castillo-Palacio, M. (2022). A Smart Campus Framework: Challenges and Opportunities for Education Based on the Sustainable Development Goals. Sustainability, 14(15), 9640. doi.org/10.3390/su14159640
• Paspatis A, Fiorentzis K, Katsigiannis Y, Karapidakis E. Smart Campus Microgrids towards a Sustainable Energy Transition—The Case Study of the Hellenic Mediterranean University in Crete. Mathematics. 2022;10(7):1065. doi:10.3390/math10071065.
• Alnoman A. A Framework for Technology-based Student-centered Learning in Smart Campus. In: 2022 Advances in Science and Engineering Technology International Conferences (ASET); 2022 Jun 14-17. IEEE; 2022. p. 1-4.
• Liu Z. Planning and Practice of Smart Campus in Higher Vocational Colleges from the Perspective of Education Informatization 2.0. In: 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE); 2021 Dec 17-19. IEEE; 2021. p. 497-500. doi.org/10.1109/ICBDIE52740.2021.00119
• Chen X. Exploration and Practice of the Construction of Smart Campus—Taking Sichuan University as an Example. In: Proceedings of the 4th International Conference on Digital Technology in Education; 2020 Oct 14-16. p. 36-41. doi.org/10.1145/3429630.3429647
• Rha JY, Lee JM, Li HY, Jo EB. From a literature review to a conceptual framework, issues and challenges for SMART Campus. Journal of Digital Convergence. 2016;14(4):19-31.
• Thar SAAB, Tun ST. GIS application: Mapping and Area Analysis for MAEU Campus. International Journal of Current Innovations in Advanced Research. 2018;1-7.
• Kinoti KD, Nyaga NP. Understanding universities land policy and their implementation strategies in Kenya: The case of Karatina University main campus, Kenya. International Journal of Landscape Planning and Architecture. 2018; 8:13.
• Hwang WH, Wiseman PE. Geospatial methods for tree canopy assessment: A case study of an urbanized college campus. Arboriculture & Urban Forestry. 2020;46(1):51-65. doi:10.48044/jauf.2020.005
• Bozdağ A, Gümüş MG, Gümüş K, Durduran SS. Evaluating spatial legibility of the university campus using GIS. Aksaray University Journal of Science and Engineering. 2020;4(2):127-147.
• Haq ES, Panduardi F. GIS-based area suitability analysis for development planning purposes campus: Case study Banyuwangi State Polytechnic. Journal of Engineering and Scientific Research. 2020;2(2):78-84.
• Zaki SA, Othman NE, Syahidah SW, et al. Effects of urban morphology on microclimate parameters in an urban university campus. Sustainability. 2020;12(7):2962. doi.org/10.3390/su12072962
• Papua O, Kumaat JC, Runtuwene JPA, Rompas PTD. In Proceedings of the 7th Engineering International Conference on Education, Concept and Application on Green Technology, Semarang, Indonesia: SCITEPRESS-Science and Technology Publications. 2020;330-334. doi.org/10.5220/0009010803300334
• Srivastava A, Chinnasamy P. Investigating impact of land-use and land cover changes on hydro-ecological balance using GIS: insights from IIT Bombay, India. SN Applied Sciences. 2021;3(3):343. https://doi.org/10.1007/s42452-021-04328-7
• Shao YB. Baselining UBC Vancouver Campus Urban Forest and Land Use: Developing and Validating Up-to-date Tree Inventory and Land Classification Map of the UBC Vancouver Campus. Vancouver: University of British Columbia Library; 2021. doi.org/10.14288/1.0400162
• Hong D, Han Z, Yao J, Gao L, Zhang B, Plaza A, Chanussot J. SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers. IEEE Transactions on Geoscience and Remote Sensing. 2022;60:1-15. doi.org/10.1109/TGRS.2021.3130716
• Ikara AI, Umar YH, Dauda BD. Mapping of Geotechnical Properties Using ArcGIS: A Case Study of Abubakar Tafawa Balewa University Bauchi, Gubi Campus. Iconic Research and Engineering Journals. 2022;5(9):1-5.
• Anushya A. 3D Reality Planner for University of Hail Using ArcGIS. International Journal of Computer Science Engineering (IJCSE). 2020;9(1):2319-7323.
• Çelik, M. Ö., & Yakar, M. (2023). Arazi kullanımı ve arazi örtüsü değişikliklerinin uzaktan algılama ve cbs yöntemi ile izlenmesi: Mersin, Türkiye örneği. Türkiye Coğrafi Bilgi Sistemleri Dergisi, 5(1), 43-51..
• Çetin Z, Yastıklı N. Automatic detection of single street trees from airborne LiDAR data based on point segmentation methods. International Journal of Engineering and Geosciences. 2022;8(2):129-137.
• Özaydın E, Amirgan B, Taşkın G, Musaoğlu N. Derin öğrenme uygulamalarında kullanılan uzaktan algılama verilerinden oluşturulmuş açık kaynaklı bina veri setleri: Karşılaştırmalı değerlendirme. Geomatik. 2024;9(1):1-11.
• Li R, Guan Q. Environmental campus: Managing campus parking meters using GPS and GIS. ISWREP 2011 - Proceedings of 2011 International Symposium on Water Resource and Environmental Protection. 2011.
• Wu ZZ, Wang XF, Zou L, Xu LX, Li XL, Weise T. Hierarchical object detection for very high-resolution satellite images. Applied Soft Computing. 2021;113:107885.
• Huawei Investment & Holding Co., Ltd. Annual report. 2023. Available from: https://img.corrierecomunicazioni.it/wp-content/uploads/2024/03/29124138/annual_report_2023_en.pdf. Accessed on 20 Aug 2024.
• Huawei Technologies Co., Ltd. Striding Towards the Intelligent World White Paper: Cloud Computing. 2022. Available from: https://www-file.huawei.com/-/media/corp2020/pdf/giv/striding-towards-the-intelligent-world/the_intelligent_world_cloud_computing_en.pdf. Accessed on 20 Aug 2024.
• World Economic Forum. How to build smart, zero carbon buildings - and why it matters. 2021. Available from: https://www.weforum.org/agenda/2021/09/how-to-build-zero-carbon-buildings/#:~:text=The%20potential%20energy%20savings%20from%20smart%20buildings%20is,ventilation%2C%20can%20save%20an%20additional%205-10%25%20in%20energy. Accessed on 20 Aug 2024.
• Lange ISG, Rodrigues CN. Urban Green Spaces: Combining Goals for Sustainability and Placemaking. EuropeNow-a journal of research & art. 2021;(Special feature).
• Haq SMA. Urban Green Spaces and an Integrative Approach to Sustainable Environment. Journal of Environmental Protection. 2011;2(5):601-608. ISBN:9780429155680.
• World Economic Forum. 7 Innovative Projects Making Cities More Sustainable. 2020. https://www.weforum.org/agenda/2020/09/cities-sustainability-innovation-global-goals/ (accessed on 20.08.2024).
• United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development: Resolution / Adopted by the General Assembly. 2015. https://digitallibrary.un.org/record/3923923?v=pdf (accessed on 20.08.2024).
• Cugurullo F. Urban Artificial Intelligence: From Automation to Autonomy in the Smart City. Frontiers in Sustainable Cities. 2020;2(38). doi.org/10.3389/frsc.2020.00038