Using computer vision for equitable property valuation in urban areas
DOI:
https://doi.org/10.33448/rsd-v14i7.48995Keywords:
Artificial Intelligence, Computer Vision, Property Valuation, Urban Areas, Equitable Assessment, Smart Cities.Abstract
The objective of this article is to present a study on the use of computer vision based on AI (Artificial Intelligence) for equitable property valuation in urban areas. This systematic literature review explores the emerging field of applying Artificial Intelligence, particularly computer vision, to improve equity, accuracy, and efficiency in property valuation in urban environments. Drawing on a wide range of academic papers—including discussions on AI in smart cities, computer vision applications, and systematic review methodologies—this study summarizes the current knowledge on the potential of AI-driven computer vision to overcome the limitations of conventional methods and contribute to more equitable urban development.
References
Ahn, M. J., & Chen, Y.-C. (2020). Artificial intelligence in government. Proceedings of the 21st Annual International Conference on Digital Government Research. https://doi.org/10.1145/3396956.3398260
Al-Ansi, A. M., Garad, A., Jaboob, M., & Al-Ansi, A. (2024). Elevating e-government: Unleashing the power of AI and IoT for enhanced public services. SSRN. https://doi.org/10.2139/ssrn.4883206
Antwi, B. O., Adelakun, B. O., & Eziefule, A. O. (2024). Transforming financial reporting with AI: Enhancing accuracy and timeliness. International Journal of Advanced Economics, 6(6), 205-223. https://doi.org/10.51594/ijae.v6i6.1229
Ardabili, B. R., Pazho, A. D., Noghre, G. A., Neff, C., Bhaskararayuni, S. D., Ravindran, A., Reid, S., & Tabkhi, H. (2023). Understanding policy and technical aspects of AI-enabled smart video surveillance to address public safety. arXiv. https://doi.org/10.48550/arxiv.2302.04310
Bibri, S. E., Huang, J., Jagatheesaperumal, S. K., & Krogstie, J. (2024). The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review. Environmental Science and Ecotechnology, 20, 100433. https://doi.org/10.1016/j.ese.2024.100433
Chen, Y.-C., Ahn, M. J., & Wang, Y. (2023). Artificial intelligence and public values: Value impacts and governance in the public sector. Sustainability, 15(6), 4796. https://doi.org/10.3390/su15064796
Crossetti, M. da G. O. (2012). Revisão integrativa de pesquisa na enfermagem o rigor cientifico que lhe é exigido. Revista Gaúcha de Enfermagem, 33(2), 8-9. https://doi.org/10.1590/S1983-14472012000200001
Gupta, R., Reebadiya, D., & Tanwar, S. (2021). 6G-enabled edge intelligence for ultra-reliable low latency applications: Vision and mission. Computer Standards & Interfaces, 77, 103521. https://doi.org/10.1016/j.csi.2021.103521
Hamirul, Darmawanto, Elsyra, N., & Syahwami. (2023). The role of artificial intelligence in government services: A systematic literature review. Open Access Indonesia Journal of Social Sciences, 6(3), 998-1003. https://doi.org/10.37275/oaijss.v6i3.163
Himeur, Y., Sayed, A. N., Alsalemi, A., Bensaali, F., & Amira, A. (2023). Edge AI for internet of energy: Challenges and perspectives. Internet of Things, 25, 101035. https://doi.org/10.1016/j.iot.2023.101035
Liu, Y., Yang, D., Wang, Y., Liu, J., Liu, J., Boukerche, A., Sun, P., & Song, L. (2024). Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models. ACM Computing Surveys, 56(7), 1-38.
Lotlikar, P., & Mohs, J. N. (2021). Examining the role of artificial intelligence on modern auditing techniques. Strategic Management Quarterly, 9(2). https://doi.org/10.15640/smq.v9n2a1
Margetts, H. (2022). Rethinking AI for good governance. Daedalus, 151(2), 360-371. https://doi.org/10.1162/daed_a_01922
Messaoudi, M. D., Ménélas, B.-A. J., & Mcheick, H. (2022). Review of navigation assistive tools and technologies for the visually impaired. Sensors, 22(20), 7888. https://doi.org/10.3390/s22207888
Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
Ochoa-Ruiz, G., Angulo-Murillo, A. A., Ochoa, A., Aguilar-Lobo, L. M., Vega-Fernández, J. A., & Natraj, S. (2020). An asphalt damage dataset and detection system based on RetinaNet for road conditions assessment. Applied Sciences, 10(11), 3974. https://doi.org/10.3390/app10113974
Pan, Y., & Zhang, L. (2020). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517. https://doi.org/10.1016/j.autcon.2020.103517
Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia de pesquisa científica. Editora da UAB/NTE/UFSM.
Reades, J., De Souza, J., & Hubbard, P. (2018). Understanding urban gentrification through machine learning. Urban Studies, 56(5), 922-942. https://doi.org/10.1177/0042098018789054
Rjab, A. B., & Mellouli, S. (2019). Artificial intelligence in smart cities. Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance. https://doi.org/10.1145/3326365.3326400
Roberts, A., & Goullette, S. (1996). CCTV surveillance: The local authority's role - A review of current practice. *Proceedings of the Institution of Civil Engineers - Municipal Engineer, 115*(2), 61-67. https://doi.org/10.1680/imuen.1996.28908
Sagayam, K. M., Winston, J. J., Wahab, M. H. A., Bhushan, B., Ambar, R., & Poad, H. M. (2021). Combination of thermal and sRGB imaging techniques for advanced surveillance system. Annals of Emerging Technologies in Computing, 5(5), 27-33. https://doi.org/10.33166/aetic.2021.05.003
Shah, N., Bhagat, N., & Shah, M. (2021). Crime forecasting: A machine learning and computer vision approach to crime prediction and prevention. Visual Computing for Industry, Biomedicine, and Art, 4(1). https://doi.org/10.1186/s42492-021-00087-9
Spencer, B. F., Hoskere, V., & Narazaki, Y. (2019). Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030
Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. (2023). A survey on optimization techniques for edge artificial intelligence (AI). Sensors, 23(3), 1279. https://doi.org/10.3390/s23031279
Valle-Cruz, D., Ruvalcaba-Gómez, E. A., Sandoval-Almazán, R., & Criado, J. I. (2019). A review of artificial intelligence in government and its potential from a public policy perspective. Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 91-99). ACM. https://doi.org/10.1145/3325112.3325242
Vanky, A., & Le, R. (2023). Urban-semantic computer vision: A framework for contextual understanding of people in urban spaces. AI & Society, 38(3), 1193-1207. https://doi.org/10.1007/s00146-022-01625-6
Vijeikis, R., Raudonis, V., & Dervinis, G. (2022). Efficient violence detection in surveillance. Sensors, 22(6), 2216. https://doi.org/10.3390/s22062216
Wang, D., Lu, C.-T., & Fu, Y. (2023). Towards automated urban planning: When generative and ChatGPT-like AI meets urban planning. arXiv. https://doi.org/10.48550/arxiv.2304.03892
Yiğitcanlar, T., Agdas, D., & Degirmenci, K. (2022). Artificial intelligence in local governments: Perceptions of city managers on prospects, constraints and choices. AI & Society, 38(3), 1135-1150. https://doi.org/10.1007/s00146-022-01450-x
Yiğitcanlar, T., & Cugurullo, F. (2020). The sustainability of artificial intelligence: An urbanistic viewpoint from the lens of smart and sustainable cities. Sustainability, 12(20), 8548. https://doi.org/10.3390/su12208548
Yiğitcanlar, T., David, A., Li, W., Fookes, C., Bibri, S. E., & Ye, X. (2024). Unlocking artificial intelligence adoption in local governments: Best practice lessons from real-world implementations. Smart Cities, 7(4), 1576-1625. https://doi.org/10.3390/smartcities7040064
Yiğitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473. https://doi.org/10.3390/en13061473
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nailson Martins Dantas Landim; Humberto Xavier de Araújo; Leonardo de Andrade Carneiro; Gentil Veloso Barbosa
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
