.Exploring Role of GeoAI in Urban Governance Towards Supporting Sustainable Development

Nassar, Eman; G. Elgazouly, Hassan; M. Elnaggar, Aly; M. Ayyad, Samy

doi:10.21608/inp.2024.399980

	.Exploring Role of GeoAI in Urban Governance Towards Supporting Sustainable Development
المجلة المصرية للتنمية والتخطيط
Volume 32, Issue 4, December 2024, Page 101-118 PDF (5.54 MB)
Document Type: المقالة الأصلية
DOI: 10.21608/inp.2024.399980
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Authors
Eman Nassar^* ¹; Hassan G. Elgazouly²; Aly M. Elnaggar²; Samy M. Ayyad²
¹Ministry of Housing, Utilities and Urban Communities
²Faculty of Engineering, Alexandria University
Abstract
Governance refers to the structures and processes that are designed to ensure accountability, transparency, responsiveness, rule of law, stability, equity and inclusiveness, empowerment, and broad-based participation. Urban governance is the procedure through which stakeholders, including local, regional, and national governments, choose how to plan, fund, and manage urban regions in many countries. Urban governance systems are currently unfit for proper governance purposes and need critical reforms to enable sustainable and inclusive urban development. Urbanization is a global phenomenon, although it develops even more rapidly in developing nations like Egypt. Unplanned growth, rising immigration, and a quickly growing population are the key drivers of urbanization. One of the most significant issues confronting developing countries is the problem of urban sprawl in agricultural areas, which has an environmental impact on several levels. This paper introduces an innovative approach to manage, monitor, and control urban sprawl on agricultural lands using spatial artificial intelligence GeoAI. Tasa village was selected as study area, which is located in Sahel Selim city, Assiut governorate, Egypt. Three satellite images were employed for the study area to monitor change detection from 1998 to 2020.
Keywords
Sustainable development; Governance; Artificial Intelligence (AI); GeoAI


References
References Alastal, A. I., & Shaqfa, A. H. (2022). 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, 10(2), 110-126. DOI: 10.4236/jdaip.2022.102007. Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020). A systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised and unsupervised learning for data science, 3-21. doi.org/10.1007/978-3-030-22475-2_1. Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular systems biology, 12(7), 878. https://doi.org/10.15252/msb.20156651. Bren d’Amour, C., Reitsma, F., Baiocchi, G., Barthel, S., Güneralp, B., Erb, K. H., ... & Seto, K. C. (2017). Future urban land expansion and implications for global croplands. Proceedings of the National Academy of Sciences, 114(34), 8939-8944. https://doi.org/10.1073/pnas.1606036114. Campbell, A., Both, A., & Sun, Q. C. (2019). Detecting and mapping traffic signs from Google Street View images using deep learning and GIS. Computers, Environment and Urban Systems, 77, 101350. https://doi.org/10.1016/j.compenvurbsys.2019.101350. Carlsson, M. K. (2013). Stratified, destratified, and hybrid GIS: organizing a cross-disciplinary territory for design (Doctoral dissertation, Massachusetts Institute of Technology). http://hdl.handle.net/1721.1/81657. De Guimarães, J. C. F., Severo, E. A., Júnior, L. A. F., Da Costa, W. P. L. B., & Salmoria, F. T. (2020). Governance and quality of life in smart cities: Towards sustainable development goals. Journal of Cleaner Production, 253, 119926. https://doi.org/10.1016/j.jclepro.2019.119926. Dou, Y., & Kuang, W. (2020). A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment, 706, 135828. https://doi.org/10.1016/j.scitotenv.2019.135828. Dupras, J., & Alam, M. (2015). Urban sprawl and ecosystem services: a half century perspective in the Montreal area (Quebec, Canada). Journal of environmental policy & planning, 17(2), 180-200. https://doi.org/10.1080/1523908X.2014.927755. Gonzales-Inca, C., Calle, M., Croghan, D., Torabi Haghighi, A., Marttila, H., Silander, J., & Alho, P. (2022). Geospatial Artificial Intelligence (GeoAI) in Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends. Water, 14(14), 2211. https://doi.org/10.3390/w14142211. Hepinstall-Cymerman, J., Coe, S., & Hutyra, L. R. (2013). Urban growth patterns and growth management boundaries in the Central Puget Sound, Washington, 1986–2007. Urban Ecosystems, 16, 109-129. https://doi.org/10.1007/s11252-011-0206-3. Dardas A. (2020). GeoAI Series #2: The Birth and Evolution of GeoAI. https://resources.esri.ca/education-and-research/geoai-series-2-the-birth-and-evolution-of-geoai. Huang, D., Huang, J., & Liu, T. (2019). Delimiting urban growth boundaries using the CLUE-S model with village administrative boundaries. Land Use Policy, 82, 422-435. https://doi.org/10.1016/j.landusepol.2018.12.028. Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2. Jianping, L., Minrong, L., Jinnan, W., Jianjian, L., Hongwen, S., & Maoxing, H. (2013). Global environmental issues and human wellbeing. Report on Global Environmental Competitiveness (2013), 3-21. https://doi.org/10.1007/978-3-642-54678-5_1. Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625-636. https://doi.org/10.1080/13658816.2019.1684500. Lewis, D. J., Yang, X., Moise, D., & Roddy, S. J. (2021). Dynamic synergies between China’s belt and road initiative and the UN’s sustainable development goals. Journal of International Business Policy, 4, 58-79. https://doi.org/10.1057/s42214-020-00082-6. Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173-189. https://doi.org/10.1016/j.envsoft.2013.12.008. Li, W., & Hsu, C. Y. (2022). GeoAI for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography. ISPRS International Journal of Geo-Information, 11(7), 385. https://doi.org/10.3390/ijgi11070385. Nassar E. T., Elgazouly H. G., A. Elnaggar M. and Ayyad S. M., Leveraging Deep Learning and IoT Big Data Analytics for The Determination of Development Priorities Utilizing GeoAI in The National Project for The Development of the Egyptian Rural Villages - Decent Life "Hayah Karima", 2023 International Telecommunications Conference (ITC-Egypt), Alexandria, Egypt, 2023, pp. 73-78, doi: 10.1109/ITC-Egypt58155.2023.10206340. Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4, 51-62. DOI: 10.20544/HORIZONS.B.04.1.17. P05. Natale, S., & Ballatore, A. (2020). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence, 26(1), 3-18. https://doi.org/10.1177/1354856517715164. Nguyen, C., Sagan, V., Bhadra, S., & Moose, S. (2023). UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping. Sensors, 23(4), 1827. https://doi.org/10.3390/s23041827. Pramanik, P. K. D., Pal, S., & Choudhury, P. (2018). Beyond automation: the cognitive IoT. artificial intelligence brings sense to the Internet of Things. Cognitive Computing for Big Data Systems Over IoT: Frameworks, Tools and Applications, 1-37. https://doi.org/10.1007/978-3-319-70688-7_1. Ryan, M. (2020). In AI we trust: ethics, artificial intelligence, and reliability. Science and Engineering Ethics, 26(5), 2749-2767. https://doi.org/10.1007/s11948-020-00228-y. Salem, M., Tsurusaki, N., & Divigalpitiya, P. (2020). Land use/land cover change detection and urban sprawl in the peri-urban area of greater Cairo since the Egyptian revolution of 2011. Journal of Land Use Science, 15(5), 592-606. https://doi.org/10.1080/1747423X.2020.1765425. Shao, Z., Sumari, N. S., Portnov, A., Ujoh, F., Musakwa, W., & Mandela, P. J. (2021). Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data. Geo-spatial Information Science, 24(2), 241-255. https://doi.org/10.1080/10095020.2020.1787800. Taulli, T. (2019). Artificial Intelligence basics: A non-technical introduction. Apress. https://doi.org/10.1007/978-1-4842-5028-0. Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179. Zhang, D., Xu, J., Zhang, Y., Wang, J., He, S., & Zhou, X. (2020). Study on sustainable urbanization literature based on Web of Science, scopus, and China national knowledge infrastructure: A scientometric analysis in CiteSpace. Journal of cleaner production, 264, 121537. https://doi.org/10.1016/j.jclepro.2020.121537.
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