Urban environments are increasingly complex, dynamic, and data-intensive, requiring advanced spatial intelligence to support proactive, evidence-based governance. Current smart city and urban informatics platforms are limited by static datasets, siloed architectures, and underutilised AI capabilities. This study proposes and demonstrates a novel AIoT-enabled platform architecture for built environment mapping and spatial decision support. Anchored in platform urbanism, the architecture integrates high-resolution imagery, pretrained deep learning models from the ArcGIS Living Atlas, iterative workflows in ArcGIS Pro, and interactive dissemination via ArcGIS Experience Builder. The platform is demonstrated through building footprint detection in three Brisbane suburbs using the Building Footprint Extraction Australia model. Suburb-level processing enhances computational efficiency, while analytical extensions support footprint change detection, flood exposure assessment, and land-use zoning overlays. Results indicate that the platform transforms manual, fragmented processes into automated, reproducible, and dynamic workflows directly applicable to urban planning. Although demonstrated for building footprints, the architecture is scalable to other urban features, including roads, parcels, and solar panels. Limitations include dependence on high-resolution imagery and pretrained models, highlighting opportunities for future work in multi-model integration, real-time data streams, and developing AI models tailored to diverse urban contexts. By bridging cutting-edge AI innovation with operational governance needs, the proposed platform offers a replicable pathway for embedding AI-enabled spatial intelligence into smart city management.
| [1] |
Ahmed S, El-Shazely A, Ahmed W. Deep learning for building footprint extraction using UAV-based orthoimages. Journal of the Indian Society of Remote Sensing, 2025, 53(4): 1243-1262
|
| [2] |
Alahi M, Sukkuea A, Tina F, Nag A, Kurdthongmee W, Suwannarat K, Mukhopadhyay SC. Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: Recent advancements and future trends. Sensors, 2023, 23(11): 5206
|
| [3] |
Almalki F, Alsamhi S, Sahal R, Hassan J, Hawbani A, Rajput N, Saif A, Morgan J, Breslin J. Green IoT for eco-friendly and sustainable smart cities: Future directions and opportunities. Mobile Networks and Applications, 2023, 28(1): 178-202
|
| [4] |
Anthony B. Decentralized AIoT based intelligence for sustainable energy prosumption in local energy communities: A citizen-centric prosumer approach. Cities, 2024, 152: 105198
|
| [5] |
Barns SBarns S. Making sense of platform intermediation. Platform Urbanism: Negotiating Platform Ecosystems in Connected Cities, 2020, Springer Singapore: 99-125
|
| [6] |
Belli L, Cilfone A, Davoli L, Ferrari G, Adorni P, Di Nocera F, Dall’Olio A, Pellegrini C, Mordacci M, Bertolotti E. IoT-enabled smart sustainable cities: Challenges and approaches. Smart Cities, 2020, 3(3): 1039-1071
|
| [7] |
Bibri S, Jagatheesaperumal S. Harnessing the potential of the metaverse and artificial intelligence for the internet of city things: Cost-effective XReality and synergistic AIoT technologies. Smart Cities, 2023, 6(5): 2397-2429
|
| [8] |
Bibri S, Huang J. Artificial intelligence of things for sustainable smart city brain and digital twin systems: Pioneering environmental synergies between real-time management and predictive planning. Environmental Science and Ecotechnology, 2025, 26: 100591
|
| [9] |
Bibri S, Huang J, Jagatheesaperumal S, Krogstie J. The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environmental Science and Ecotechnology, 2024, 20: 100433
|
| [10] |
Bibri S, Huang J, Krogstie J. Artificial intelligence of things for synergizing smarter eco-city brain, metabolism, and platform: Pioneering data-driven environmental governance. Sustainable Cities and Society, 2024, 108: 105516
|
| [11] |
Cao H, Wachowicz M. The design of an IoT-GIS platform for performing automated analytical tasks. Computers, Environment and Urban Systems, 2019, 74: 23-40
|
| [12] |
Caprotti F, Liu D. Platform urbanism and the Chinese smart city: The co-production and territorialisation of Hangzhou City Brain. GeoJournal, 2022, 87(3): 1559-1573
|
| [13] |
Cavalcante, E., Batista, T., Oliveira, M., Pereira, J., Ribeiro, V., & Oliveira, M. (2022). A multidimensional approach for logistics routing in the smart territory. Companion Proceedings of the Brazilian Symposium on Information Systems (SBSI), 350–357%@ 0000–0000.
|
| [14] |
Cesario, E. (2023). Big data analytics and smart cities: applications, challenges, and opportunities. Frontiers in Big Data,6.
|
| [15] |
Chen X, Li Z, Peter D, Slowik A. Circular economy oriented future building information processing: PSO for CNN approach. Applied Soft Computing, 2023, 149(9): 111013
|
| [16] |
Cina E, Elbasi E, Elmazi G, AlArnaout Z. The role of AI in predictive modelling for sustainable urban development: Challenges and opportunities. Sustainability, 2025, 17(11): 5148
|
| [17] |
Costa D, Bittencourt J, Oliveira F, Peixoto J, Jesus T. Achieving sustainable smart cities through geospatial data-driven approaches. Sustainability, 2024, 16(2): 640
|
| [18] |
Dabove, P., Daud, M., & Olivotto, L. (2024). Revolutionizing urban mapping: deep learning and data fusion strategiesfor accurate building footprint segmentation. Scientific Reports, 14(1), 13510.
|
| [19] |
D’Amico G, L’Abbate P, Liao W, Yigitcanlar T, Ioppolo G. Understanding sensor cities: Insights from technology giant company driven smart urbanism practices. Sensors, 2020, 20(16): 4391
|
| [20] |
Dahmane W, Ouchani S, Bouarfa H. Smart cities services and solutions: A systematic review. Data and Information Management, 2025, 9(2): 100087
|
| [21] |
Dritsas E, Trigka M. Remote sensing and geospatial analysis in the big data era: A survey. Remote Sensing, 2025, 17(3): 550
|
| [22] |
Esri. (2025). Building footprint extraction - Australia. Esri. Retrieved 26 September from https://qutaus.maps.arcgis.com/home/item.html?id=4e38dec1577b4b7da5365294d8a66534
|
| [23] |
Farahani L, Izadpanahi P, Tucker R. The death and life of Australian suburbs: Relationships between social activity and the physical qualities of Australian suburban neighbourhood centres. City, Culture and Society, 2022, 28: 100426
|
| [24] |
Forkan A, Kang Y, Marti F, Banerjee A, McCarthy C, Ghaderi H, Costa B, Dawod A, Georgakopolous D, Jayaraman P. AIoT-citysense: AI and IoT-driven city-scale sensing for roadside infrastructure maintenance. Data Science and Engineering, 2024, 9(1): 26-40
|
| [25] |
Ghahremani A, Adams S, Norton M, Khoo S, Kouzani A. Advancements in AI-driven detection and localisation of solar panel defects. Advanced Engineering Informatics, 2025, 64: 103104
|
| [26] |
Ghimire S, Bhandari B, Casillas-Pérez D, Deo RC, Salcedo-Sanz S. Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia. Engineering Applications of Artificial Intelligence, 2022, 112: 104860
|
| [27] |
Hossain S, Yigitcanlar T, Nguyen K, Xu Y. Platform urbanism for resident safety: A real-time predictive microclimate risk monitoring and alert system. Urban Climate, 2025, 61: 102445
|
| [28] |
Hou K, Diao X, Shi H, Ding H, Zhou H, de Vaulx C. Trends and challenges in AIoT/IIoT/IoT implementation. Sensors, 2023, 23(11): 5074
|
| [29] |
Jagatheesaperumal S, Bibri S, Huang J, Rajapandian J, Parthiban B. Artificial intelligence of things for smart cities: Advanced solutions for enhancing transportation safety. Computational Urban Science, 2024, 4(1): 10
|
| [30] |
Kuguoglu B, van der Voort H, Janssen M. The giant leap for smart cities: Scaling up smart city artificial intelligence of things (AIoT) initiatives. Sustainability, 2021, 13(21): 12295
|
| [31] |
Leszczynski A. Glitchy vignettes of platform urbanism. Environment and Planning d: Society and Space, 2020, 38(2): 189-208
|
| [32] |
Liao C, Li Y, Guo R, Li X. Artificial intelligence for spatial analysis in cities. Cities (Guildford, England), 2025, 167: 106334
|
| [33] |
Liu P, Liu X, Liu M, Shi Q, Yang J, Xu X, Zhang Y. Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network. Remote Sensing (Basel, Switzerland), 2019, 11(7): 830
|
| [34] |
Muhammed D, Ahvar E, Ahvar S, Trocan M, Montpetit M, Ehsani R. Artificial intelligence of things (AIoT) for smart agriculture: A review of architectures, technologies and solutions. Journal of Network and Computer Applications, 2024, 228: 103905
|
| [35] |
Oostwegel L, Schorlemmer D, Guéguen P. From footprints to functions: A comprehensive global and semantic building footprint dataset. Scientific Data, 2025, 12(1): 1699
|
| [36] |
Pan X, Mavrokapnidis D, Ly H, Mohammadi N, Taylor J. Assessing and forecasting collective urban heat exposure with smart city digital twins. Scientific Reports, 2024, 14(1): 9653
|
| [37] |
Patton S, Pojani D. Some like it hot? Unequal provision of tree shading in Australian subtropical suburbs. Australian Planner, 2022, 58(1–2): 1-10
|
| [38] |
Pereira J, Batista T, Cavalcante E, Souza A, Lopes F, Cacho N. A platform for integrating heterogeneous data and developing smart city applications. Future Generation Computer Systems, 2022, 128: 552-566
|
| [40] |
Pise A, Almuzaini K, Ahanger T, Farouk A, Pant K, Pareek P, Nuagah S. Enabling artificial intelligence of things (AIoT) healthcare architectures and listing security issues. Computational Intelligence and Neuroscience, 2022, 2022(1): 8421434
|
| [41] |
Ramalho M, Rossetti R, Cacho N, Souza A. SmartGC: A software architecture for garbage collection in smart cities. International Journal of Bio-Inspired Computation, 2020, 16(2): 79-93
|
| [42] |
Repette P, Sabatini-Marques J, Yigitcanlar T, Sell D, Costa E. The evolution of city-as-a-platform: Smart urban development governance with collective knowledge-based platform urbanism. Land, 2021, 10(1): 33
|
| [43] |
Shaamala A, Yigitcanlar T, Nili A, Nyandega D. Machine learning applications for urban geospatial analysis: A review of urban and environmental studies. Cities, 2025, 165: 106139
|
| [44] |
Sharma NK, Saharia M. DeepSARFlood: Rapid and automated SAR-based flood inundation mapping using vision transformer-based deep ensembles with uncertainty estimates. Science of Remote Sensing, 2025, 11: 100203
|
| [45] |
Sharma, G., Raghuwanshi, S., Suhail, M., Narwaria, T., & Hasan, A. (2025). The role of digital twins in toward sustainable urbanization: The case of smart cities. In W. Leal Filho, S. Kautish, & V. Gupta (Eds.), Metaverse and Sustainability: Business Resilience Towards Sustainable Development Goals (pp. 341–359). Springer Nature Switzerland.
|
| [46] |
Shi W, Goodchild M, Batty M, Li Q, Liu X, Zhang A. Prospective for urban informatics. Urban Informatics, 2022, 1(1): 2
|
| [47] |
Song T, Cai J, Chahine T, Li L. Towards smart cities by internet of things (IoT): A silent revolution in China. Journal of the Knowledge Economy, 2021, 12(2): 1-17
|
| [48] |
Song J, Zhu A, Zhu Y. Transformer-based semantic segmentation for extraction of building footprints from very-high-resolution images. Sensors, 2023, 23(11): 5166
|
| [50] |
Ullah, A., Khan, S., Ullah, I., Mahmood, T., Nawazish, S., Ali, Z., & Rehman, A. (2025). Toward sustainable smart cities: applications, challenges, and future directions. International Journal of Data Science and Analytics.
|
| [53] |
Viswambharan, V., & Singh, R. (2025). Pretrained nodels in ArcGIS: Comparing task-specific and generalized vision-language models. https://www.esri.com/arcgis-blog/products/arcgis/geoai/task-and-generalized-pretrained-models
|
| [54] |
Vítor G, Rito P, Sargento S, Pinto F. A scalable approach for smart city data platform: Support of real-time processing and data sharing. Computer Networks, 2022, 213: 109027
|
| [55] |
Wolf, K., Stiles, J., Miller, H., Dawson, R., Mills, J., Blythe, P., & Morley, J. (2025). Building enduring smart city data platforms to provide urban management support: lessons learnt from UK Urban Observatories and the US Smart Columbus Operating System. Frontiers in Sustainable Cities,7.
|
| [56] |
Wu AN, Biljecki F. Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability. Landscape and Urban Planning, 2021, 214: 104167
|
| [57] |
Wu P, Zhang Z, Peng X, Wang R. Deep learning solutions for smart city challenges in urban development. Scientific Reports, 2024, 14(1): 5176
|
| [58] |
Yao F, Wang Y. Towards resilient and smart cities: A real-time urban analytical and geo-visual system for social media streaming data. Sustainable Cities and Society, 2020, 63: 102448
|
| [59] |
Ye X, Goodchild M. Toward ethical geodesign in the urban digital twin era. Journal of Planning Education and Research, 2025,
|
| [60] |
Ye X, Bai W, Wang W, Huang X. Enhancing population data granularity: A comprehensive approach using lidar, poi, and quadratic programming. Cities, 2024, 152: 105223
|
| [61] |
Ye, X., Li, S., Gao, G., Retchless, D., Cai, Z., Newman, G., ... & Duffield, N. (2024b). 3D visualization of hurricane storm surge impact on urban infrastructure. Urban Informatics, 3(1), 9.
|
| [62] |
Yigitcanlar T, Desouza K, Butler L, Roozkhosh F. Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 2020, 13(6): 1473
|
| [63] |
Yigitcanlar T, Degirmenci K, Butler L, Desouza K. What are the key factors affecting smart city transformation readiness? Evidence from Australian cities. Cities, 2022, 120: 103434
|
| [64] |
Yigitcanlar T, Hossain T, Abdulrazzaq S, Ye X. Quantum AI urbanism: Redefining the future of artificial intelligence in cities. Journal of Urban Technology, 2025, 32(3): 1-14
|
| [65] |
Zhang T, Zhao Y, Jia W, Chen M. Collaborative algorithms that combine AI with IoT towards monitoring and control system. Future Generation Computer Systems, 2021, 125: 677-686
|
| [66] |
Zhuang Y, Cenci J, Zhang J. Review of big data implementation and expectations in smart cities. Buildings, 2024, 14(12): 3717
|
Funding
Queensland University of Technology
RIGHTS & PERMISSIONS
The Author(s)