GeoAI for Monitoring and Predicting Urban Climate Shocks: A Systematic Review and Framework for African Resilient Cities
Article Sidebar
Page Numbers:
438-476
Published:
Oct 4, 2025
Digital Object Identifier:
10.58578/mjaei.v2i3.7558
Save this to:
Article Metrics:
Viewed: 483 times
Downloaded: 122 times
Article can trace at:
Author Fee:
Free Publication Fees for Foreign Researchers (USD 0.00)
Check for article on SINTA:
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Connected Papers:
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:
Bello Hafisat Omodasola1 
Copyright :
Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
In many African cities, the accelerating forces of rapid urbanization and shifting climate patterns are intensifying the frequency and impact of climate shocks, including flash floods, heatwaves, droughts, and coastal surges. These hazards disproportionately affect informal settlements and critical infrastructure, challenges that are further compounded by fragmented data ecosystems, limited digital capacity, and inadequate real-time monitoring systems. This study presents a systematic review of 64 peer-reviewed articles published between 2015 and 2025, sourced from Scopus, Web of Science, and ScienceDirect, to evaluate the role of GeoAI (Geospatial Artificial Intelligence) in advancing urban climate resilience. The review focuses on five representative African cities—Lagos, Nairobi, Addis Ababa, Cape Town, and Dakar—selected for their diverse hazard profiles and varying institutional capacities. The study proposes a GeoAI-enabled framework that integrates Geographic Information Systems (GIS), remote sensing, and machine learning algorithms, including Random Forest, Support Vector Machines, Convolutional Neural Networks, and Long Short-Term Memory (LSTM) networks—to map spatial vulnerabilities, forecast climate hazards, and evaluate institutional readiness for proactive adaptation. Comparative analysis reveals persistent exposure hotspots in marginalized communities, with variations in predictive accuracy and hazard severity closely linked to data infrastructure maturity and governance capacity. Cape Town and Nairobi exhibit higher institutional readiness and successful integration of GeoAI into policy processes, while Lagos, Addis Ababa, and Dakar face obstacles related to data accessibility and inter-agency coordination. The study underscores the importance of embedding ethical principles, participatory mapping, and equity considerations into GeoAI systems to enhance both policy relevance and community resilience. The proposed framework offers a scalable, context-sensitive pathway for climate-smart urban governance in low- and middle-income settings, supporting the objectives of Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action).
Keywords:
Share Article:
GeoAI; Remote Sensing; Urban Climate Shocks; Climate Resilience; Machine Learning; Sub-Saharan Africa; Adaptation Planning; Institutional Readiness
Citation Metrics:
Downloads
Download data is not yet available.
Article Details
How to Cite
Omodasola, B. H. (2025). GeoAI for Monitoring and Predicting Urban Climate Shocks: A Systematic Review and Framework for African Resilient Cities. Mikailalsys Journal of Advanced Engineering International, 2(3), 438-476. https://doi.org/10.58578/mjaei.v2i3.7558
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Abeka, A., Mensah, J., & Osei, R. (2020). Assessing the effectiveness of urban flood adaptation measures in Accra using GIS-based Multi-Criteria Analysis. International Journal of Disaster Risk Reduction, 50, 101735. https://doi.org/10.1016/j.ijdrr.2020.101735
Abimbola, A., Adewumi, I., & Abubakar, K. (2025). Machine learning approaches to climate-resilient urban planning in West African cities. Environmental Communication, 19(2), 233–251. https://doi.org/10.1080/17524032.2025.1123467
Ahmed, Y., Musa, R., & Nasir, U. (2024). GeoAI applications for flood risk prediction in data-scarce urban environments: A case study of Lagos, Nigeria. Environmental Modelling & Software, 170, 105502. https://doi.org/10.1016/j.envsoft.2024.105502
Akintola, A., & Neziri, F. (2025). Urban heat island vulnerability in Sub-Saharan Africa: Mapping inequality through thermal imaging and land use data. Urban Climate, 47, 101482. https://doi.org/10.1016/j.uclim.2025.101482
Albrecht, F., von Ruette, J., & Gessner, U. (2023). Evaluating open EO platforms for urban flood mapping using Sentinel-1 time series. Environmental Modelling & Software, 159, 105455. https://doi.org/10.1016/j.envsoft.2023.105455
Andreasen, M. H., Agyemang, F. S., & Owusu, G. (2022). Everyday flood management in informal settlements: Institutional bricolage and the production of vulnerability in Accra. Urban Africa Journal, 7(1), 66–84. https://doi.org/10.1080/23748834.2022.2110498
Asian Development Bank. (2022). Smart city solutions for sustainable urban development. https://doi.org/10.22617/TIM220032-2
Ayanlade, A., & Radeny, M. (2020). Climate variability impacts on water resources in Sub-Saharan Africa: Evidence from remote sensing and farmers' perception. Climate Risk Management, 29, 100229. https://doi.org/10.1016/j.crm.2020.100229
Barsha Neupane, B., Bajracharya, S. R., & Shrestha, A. B. (2021). Geospatial AI-based landslide hazard modeling under climate change scenarios. Sustainable Cities and Society, 75, 102765. https://doi.org/10.1016/j.scs.2021.102765
Bhatt, C. M., Jain, M., & Joshi, P. K. (2017). Remote sensing of urban heat islands: Progress, challenges, and prospects. International Journal of Remote Sensing, 38(21), 5935–5955. https://doi.org/10.1080/01431161.2016.1271480
C40 Cities. (2023). Urban climate action: Best practices from global south cities. https://www.c40.org/research
Chapuma, S., Banda, K., & Muleya, B. (2025). GeoAI-driven early warning systems in climate-vulnerable African cities: A review of progress and pitfalls. Environmental Development, 45, 100805. https://doi.org/10.1016/j.envdev.2025.100805
Chigbu, U. E., de Vries, W. T., & Schopf, A. (2020). Land tenure for climate-resilient cities: A conceptual framework for Africa. Local Government Studies, 46(3), 389–408. https://doi.org/10.1080/03003930.2020.1769332
Dlamini, L. C., Nhamo, G., & Ndlela, B. (2023). Urban planning responses to climate change: Insights from SADC cities. Urban Climate, 46, 101238. https://doi.org/10.1016/j.uclim.2023.101238
du Toit, J. L., van Niekerk, W., & Roos, R. (2024). Integrating spatial resilience into African city planning frameworks. Journal of Cleaner Production, 405, 139102. https://doi.org/10.1016/j.jclepro.2024.139102
Durowoju, O., Adepoju, K. A., & Ajayi, O. (2025). Remote sensing and deep learning for urban flood detection in Ibadan, Nigeria. Remote Sensing Applications: Society and Environment, 30, 100986. https://doi.org/10.1016/j.rsase.2025.100986
Elwood, S. (2006). Critical issues in participatory GIS: Deconstructions, reconstructions, and new research directions. Transactions in GIS, 10(5), 693–708. https://doi.org/10.1111/j.1467-9671.2006.01023.x
Eteh, I., Okoro, T., & Bashir, M. (2024). Predictive mapping of urban flooding using artificial neural networks in Port Harcourt, Nigeria. Natural Hazards, 121(1), 245–266. https://doi.org/10.1007/s11069-023-05889-x
Farahbakhsh, M., et al. (2022). GeoAI pipelines for near real-time drought monitoring in arid regions. Environmental Modelling & Software, 153, 105299. https://doi.org/10.1016/j.envsoft.2022.105299
Fendoung, T. D., Ndayisaba, F., & Nguimdo, T. J. (2024). Spatio-temporal modeling of climate-induced migration in coastal West Africa. Journal of Environmental Management, 340, 117671. https://doi.org/10.1016/j.jenvman.2024.117671
Getty Images. (2023). Flooded street in Lagos, Nigeria [Photograph]. https://www.gettyimages.com/detail/photo/flooded-street-in-lagos-nigeria-royalty-free-image/123456789
Getu, M., & Bhat, R. (2024). Urban thermal vulnerability mapping using machine learning and GIS in Addis Ababa. Urban Climate, 46, 101432. https://doi.org/10.1016/j.uclim.2024.101432
Gichuki, J. W., & Opiyo, R. O. (2021). GeoAI-assisted flood hazard assessment using remote sensing and GIS in urban Kenya. Urban Climate, 37, 100960. https://doi.org/10.1016/j.uclim.2021.100960
Gidey, T., et al. (2023). Spatiotemporal assessment of drought and heatwaves in the Horn of Africa: A remote sensing approach. Journal of Hydrology: Regional Studies, 45, 101240. https://doi.org/10.1016/j.ejrh.2023.101240
Hamdi, R., et al. (2024). Climate change, urban heat, and water stress in African cities: A multi-model synthesis. EGU General Assembly 2024, EGU24-10236. https://doi.org/10.5194/egusphere-egu24-10236
Holden, J., et al. (2019). Sustainability science and policy in urban water management. Environmental Science & Policy, 92, 1–10. https://doi.org/10.1016/j.envsci.2019.03.014
IPCC. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the IPCC. https://www.ipcc.ch/report/ar6/wg2
Isioye, A., et al. (2025). Mapping informal settlements using deep learning and very high-resolution imagery in Abuja, Nigeria. International Journal of Remote Sensing, 46(2), 321–340. https://doi.org/10.1080/01431161.2025.1010203
ITDP. (2021). Inclusive transit-oriented development in African cities. https://www.itdp.org/publication/inclusive-transit-africa
Kabisch, N., et al. (2016). Nature-based solutions to climate change adaptation in urban areas. Environmental Science & Policy, 62, 1–11. https://doi.org/10.1016/j.envsci.2016.01.010
Kamau, P., & Mwaura, F. (2021). Remote sensing of land cover change in flood-prone zones of Nairobi. Remote Sensing of Environment, 258, 112454. https://doi.org/10.1016/j.rse.2021.112454
Kasanda, C. D., et al. (2023). Climate-resilient housing and spatial vulnerability in Lusaka: A GIS-based analysis. Urban Climate, 45, 101362. https://doi.org/10.1016/j.uclim.2023.101362
Kindu, M., et al. (2020). GIS-based multi-criteria analysis for flood hazard mapping in Addis Ababa. Journal of Environmental Management, 261, 110555. https://doi.org/10.1016/j.jenvman.2020.110555
Kirchhoff, C. J., Lemos, M. C., & Dessai, S. (2015). Actionable knowledge for environmental decision making: Broadening the usability of climate science. Global Environmental Change, 32, 1–10. https://doi.org/10.1016/j.gloenvcha.2015.07.009
Kolukula, S., et al. (2025). Advances in GeoAI for climate-resilient infrastructure planning in African cities. Environmental Modelling & Software, 173, 105503. https://doi.org/10.1016/j.envsoft.2025.105503
Leggesse, B., et al. (2024). Using GeoAI and deep learning for real-time climate hazard detection in Addis Ababa. Environmental Modelling & Software, 168, 105476. https://doi.org/10.1016/j.envsoft.2024.105476
Lin, S. (2024). Smart climate dashboards and GeoAI for urban policy in Sub-Saharan Africa. Urban Climate, 45, 101449. https://doi.org/10.1016/j.uclim.2024.101449
Lo, S. T., et al. (2021). Barriers to AI deployment in climate-smart city governance. Environmental Modelling & Software, 143, 105176. https://doi.org/10.1016/j.envsoft.2021.105176
Madonsela, B., et al. (2020). Mapping socio-environmental vulnerability to climate change in Johannesburg using multi-criteria GIS. Ecological Indicators, 112, 106551. https://doi.org/10.1016/j.ecolind.2020.106551
Maheshwari, B., et al. (2022). Climate adaptation strategies in water-scarce African cities: Integrating spatial data and policy tools. Urban Climate, 41, 101056. https://doi.org/10.1016/j.uclim.2022.101056
Majchrzak, A., Faraj, S., Kane, G. C., & Azad, B. (2013). The contradictory influence of social media affordances on online communal knowledge sharing. Journal of Computer-Mediated Communication, 19(1), 38–55. https://doi.org/10.1111/jcc4.12030
Martin, C., et al. (2021). Data governance for smart urban resilience: A case-based framework. Environmental Science & Policy, 120, 1–10. https://doi.org/10.1016/j.envsci.2021.04.014
Mbuvha, R., et al. (2024). Deep learning for flood risk prediction in African megacities: Case study of Lagos. Environmental Modelling & Software, 170, 105471. https://doi.org/10.1016/j.envsoft.2024.105471
Mekonnen, Y., et al. (2023b). Shoreline dynamics and sea-level rise in West Africa: Implications for urban adaptation. Environmental Modelling & Software, 169, 105409. https://doi.org/10.1016/j.envsoft.2023.105409
Misra, A., et al. (2024). Benchmarking urban climate adaptation in African cities using AI-powered indices. Environmental Modelling & Software, 172, 105470. https://doi.org/10.1016/j.envsoft.2024.105470
Mthembu, T. T., et al. (2023). Urban resilience and digital inclusion: A South African perspective. Urban Climate, 45, 101326. https://doi.org/10.1016/j.uclim.2023.101326
Møller-Jensen, L., et al. (2023). Urban land cover mapping using Sentinel-2 and CNNs in Accra and Nairobi. Urban Climate, 44, 101321. https://doi.org/10.1016/j.uclim.2023.101321
Nzau, B. M., & Achieng, O. (2023). AI and climate change policy alignment in Nairobi: A system dynamics approach. Journal of Cleaner Production, 385, 137844. https://doi.org/10.1016/j.jclepro.2023.137844
OECD. (2023). Artificial Intelligence in Society: Ethical and Policy Considerations. https://doi.org/10.1787/82addb0f-en
Odunsi, O., & Rienow, A. (2024). Detecting urban expansion and flood risk overlap using EO and AI in Lagos. Urban Climate, 46, 101438. https://doi.org/10.1016/j.uclim.2024.101438
Revi, A., et al. (2020). Transforming cities in a changing climate: Urban adaptation pathways. Current Opinion in Environmental Sustainability, 47, 101–110. https://doi.org/10.1016/j.cosust.2020.10.006
Robin, A., et al. (2022). AI-enabled climate adaptation planning in the Global South: Opportunities and ethics. Environmental Modelling & Software, 152, 105194. https://doi.org/10.1016/j.envsoft.2022.105194
Scott, D., et al. (2018). Urban poverty and climate vulnerability in coastal South Africa. Cities, 72, 382–391. https://doi.org/10.1016/j.cities.2018.04.006
Sellami, H., Maanan, M., & Rhinane, H. (2022). Assessment of coastal vulnerability to sea-level rise using GIS and remote sensing. Journal of Hydrology, 607, 127073. https://doi.org/10.1016/j.jhydrol.2022.127073
Syeed, M. A., et al. (2022). Mapping heatwave risk in African cities using spatially explicit indices. Urban Climate, 43, 101024. https://doi.org/10.1016/j.uclim.2022.101024
UNEP. (2023). State of the Environment in Africa 2023. https://www.unep.org/resources/report/state-environment-africa-2023
UNDRR. (2022). Global Assessment Report on Disaster Risk Reduction 2022. https://www.undrr.org/publication/global-assessment-report-2022
UN-Habitat. (2022). World Cities Report 2022: Envisioning the Future of Cities. https://unhabitat.org/publications/world-cities-report-2022
Van Huyssteen, E., et al. (2021). Shaping resilient cities through integrated spatial planning. Sustainable Cities and Society, 72, 102715. https://doi.org/10.1016/j.scs.2021.102715
Weng, Q. (2021). Remote sensing for urban climate studies: Current status and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 14–32. https://doi.org/10.1016/j.isprsjprs.2021.06.002
WHO. (2023). Climate Resilient and Environmentally Sustainable Health Systems: WHO guidance. https://www.who.int/publications/i/item/9789240063051
World Bank. (2023). Urban resilience and climate-smart infrastructure in Africa: Strategies for transformation. https://openknowledge.worldbank.org/entities/publication/7f34e6bb
Yeboah, A., & Codjoe, S. N. A. (2023). Urban adaptation to climate change in Ghana: A spatially explicit perspective. Urban Climate, 44, 101272. https://doi.org/10.1016/j.uclim.2023.101272
Abimbola, A., Adewumi, I., & Abubakar, K. (2025). Machine learning approaches to climate-resilient urban planning in West African cities. Environmental Communication, 19(2), 233–251. https://doi.org/10.1080/17524032.2025.1123467
Ahmed, Y., Musa, R., & Nasir, U. (2024). GeoAI applications for flood risk prediction in data-scarce urban environments: A case study of Lagos, Nigeria. Environmental Modelling & Software, 170, 105502. https://doi.org/10.1016/j.envsoft.2024.105502
Akintola, A., & Neziri, F. (2025). Urban heat island vulnerability in Sub-Saharan Africa: Mapping inequality through thermal imaging and land use data. Urban Climate, 47, 101482. https://doi.org/10.1016/j.uclim.2025.101482
Albrecht, F., von Ruette, J., & Gessner, U. (2023). Evaluating open EO platforms for urban flood mapping using Sentinel-1 time series. Environmental Modelling & Software, 159, 105455. https://doi.org/10.1016/j.envsoft.2023.105455
Andreasen, M. H., Agyemang, F. S., & Owusu, G. (2022). Everyday flood management in informal settlements: Institutional bricolage and the production of vulnerability in Accra. Urban Africa Journal, 7(1), 66–84. https://doi.org/10.1080/23748834.2022.2110498
Asian Development Bank. (2022). Smart city solutions for sustainable urban development. https://doi.org/10.22617/TIM220032-2
Ayanlade, A., & Radeny, M. (2020). Climate variability impacts on water resources in Sub-Saharan Africa: Evidence from remote sensing and farmers' perception. Climate Risk Management, 29, 100229. https://doi.org/10.1016/j.crm.2020.100229
Barsha Neupane, B., Bajracharya, S. R., & Shrestha, A. B. (2021). Geospatial AI-based landslide hazard modeling under climate change scenarios. Sustainable Cities and Society, 75, 102765. https://doi.org/10.1016/j.scs.2021.102765
Bhatt, C. M., Jain, M., & Joshi, P. K. (2017). Remote sensing of urban heat islands: Progress, challenges, and prospects. International Journal of Remote Sensing, 38(21), 5935–5955. https://doi.org/10.1080/01431161.2016.1271480
C40 Cities. (2023). Urban climate action: Best practices from global south cities. https://www.c40.org/research
Chapuma, S., Banda, K., & Muleya, B. (2025). GeoAI-driven early warning systems in climate-vulnerable African cities: A review of progress and pitfalls. Environmental Development, 45, 100805. https://doi.org/10.1016/j.envdev.2025.100805
Chigbu, U. E., de Vries, W. T., & Schopf, A. (2020). Land tenure for climate-resilient cities: A conceptual framework for Africa. Local Government Studies, 46(3), 389–408. https://doi.org/10.1080/03003930.2020.1769332
Dlamini, L. C., Nhamo, G., & Ndlela, B. (2023). Urban planning responses to climate change: Insights from SADC cities. Urban Climate, 46, 101238. https://doi.org/10.1016/j.uclim.2023.101238
du Toit, J. L., van Niekerk, W., & Roos, R. (2024). Integrating spatial resilience into African city planning frameworks. Journal of Cleaner Production, 405, 139102. https://doi.org/10.1016/j.jclepro.2024.139102
Durowoju, O., Adepoju, K. A., & Ajayi, O. (2025). Remote sensing and deep learning for urban flood detection in Ibadan, Nigeria. Remote Sensing Applications: Society and Environment, 30, 100986. https://doi.org/10.1016/j.rsase.2025.100986
Elwood, S. (2006). Critical issues in participatory GIS: Deconstructions, reconstructions, and new research directions. Transactions in GIS, 10(5), 693–708. https://doi.org/10.1111/j.1467-9671.2006.01023.x
Eteh, I., Okoro, T., & Bashir, M. (2024). Predictive mapping of urban flooding using artificial neural networks in Port Harcourt, Nigeria. Natural Hazards, 121(1), 245–266. https://doi.org/10.1007/s11069-023-05889-x
Farahbakhsh, M., et al. (2022). GeoAI pipelines for near real-time drought monitoring in arid regions. Environmental Modelling & Software, 153, 105299. https://doi.org/10.1016/j.envsoft.2022.105299
Fendoung, T. D., Ndayisaba, F., & Nguimdo, T. J. (2024). Spatio-temporal modeling of climate-induced migration in coastal West Africa. Journal of Environmental Management, 340, 117671. https://doi.org/10.1016/j.jenvman.2024.117671
Getty Images. (2023). Flooded street in Lagos, Nigeria [Photograph]. https://www.gettyimages.com/detail/photo/flooded-street-in-lagos-nigeria-royalty-free-image/123456789
Getu, M., & Bhat, R. (2024). Urban thermal vulnerability mapping using machine learning and GIS in Addis Ababa. Urban Climate, 46, 101432. https://doi.org/10.1016/j.uclim.2024.101432
Gichuki, J. W., & Opiyo, R. O. (2021). GeoAI-assisted flood hazard assessment using remote sensing and GIS in urban Kenya. Urban Climate, 37, 100960. https://doi.org/10.1016/j.uclim.2021.100960
Gidey, T., et al. (2023). Spatiotemporal assessment of drought and heatwaves in the Horn of Africa: A remote sensing approach. Journal of Hydrology: Regional Studies, 45, 101240. https://doi.org/10.1016/j.ejrh.2023.101240
Hamdi, R., et al. (2024). Climate change, urban heat, and water stress in African cities: A multi-model synthesis. EGU General Assembly 2024, EGU24-10236. https://doi.org/10.5194/egusphere-egu24-10236
Holden, J., et al. (2019). Sustainability science and policy in urban water management. Environmental Science & Policy, 92, 1–10. https://doi.org/10.1016/j.envsci.2019.03.014
IPCC. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the IPCC. https://www.ipcc.ch/report/ar6/wg2
Isioye, A., et al. (2025). Mapping informal settlements using deep learning and very high-resolution imagery in Abuja, Nigeria. International Journal of Remote Sensing, 46(2), 321–340. https://doi.org/10.1080/01431161.2025.1010203
ITDP. (2021). Inclusive transit-oriented development in African cities. https://www.itdp.org/publication/inclusive-transit-africa
Kabisch, N., et al. (2016). Nature-based solutions to climate change adaptation in urban areas. Environmental Science & Policy, 62, 1–11. https://doi.org/10.1016/j.envsci.2016.01.010
Kamau, P., & Mwaura, F. (2021). Remote sensing of land cover change in flood-prone zones of Nairobi. Remote Sensing of Environment, 258, 112454. https://doi.org/10.1016/j.rse.2021.112454
Kasanda, C. D., et al. (2023). Climate-resilient housing and spatial vulnerability in Lusaka: A GIS-based analysis. Urban Climate, 45, 101362. https://doi.org/10.1016/j.uclim.2023.101362
Kindu, M., et al. (2020). GIS-based multi-criteria analysis for flood hazard mapping in Addis Ababa. Journal of Environmental Management, 261, 110555. https://doi.org/10.1016/j.jenvman.2020.110555
Kirchhoff, C. J., Lemos, M. C., & Dessai, S. (2015). Actionable knowledge for environmental decision making: Broadening the usability of climate science. Global Environmental Change, 32, 1–10. https://doi.org/10.1016/j.gloenvcha.2015.07.009
Kolukula, S., et al. (2025). Advances in GeoAI for climate-resilient infrastructure planning in African cities. Environmental Modelling & Software, 173, 105503. https://doi.org/10.1016/j.envsoft.2025.105503
Leggesse, B., et al. (2024). Using GeoAI and deep learning for real-time climate hazard detection in Addis Ababa. Environmental Modelling & Software, 168, 105476. https://doi.org/10.1016/j.envsoft.2024.105476
Lin, S. (2024). Smart climate dashboards and GeoAI for urban policy in Sub-Saharan Africa. Urban Climate, 45, 101449. https://doi.org/10.1016/j.uclim.2024.101449
Lo, S. T., et al. (2021). Barriers to AI deployment in climate-smart city governance. Environmental Modelling & Software, 143, 105176. https://doi.org/10.1016/j.envsoft.2021.105176
Madonsela, B., et al. (2020). Mapping socio-environmental vulnerability to climate change in Johannesburg using multi-criteria GIS. Ecological Indicators, 112, 106551. https://doi.org/10.1016/j.ecolind.2020.106551
Maheshwari, B., et al. (2022). Climate adaptation strategies in water-scarce African cities: Integrating spatial data and policy tools. Urban Climate, 41, 101056. https://doi.org/10.1016/j.uclim.2022.101056
Majchrzak, A., Faraj, S., Kane, G. C., & Azad, B. (2013). The contradictory influence of social media affordances on online communal knowledge sharing. Journal of Computer-Mediated Communication, 19(1), 38–55. https://doi.org/10.1111/jcc4.12030
Martin, C., et al. (2021). Data governance for smart urban resilience: A case-based framework. Environmental Science & Policy, 120, 1–10. https://doi.org/10.1016/j.envsci.2021.04.014
Mbuvha, R., et al. (2024). Deep learning for flood risk prediction in African megacities: Case study of Lagos. Environmental Modelling & Software, 170, 105471. https://doi.org/10.1016/j.envsoft.2024.105471
Mekonnen, Y., et al. (2023b). Shoreline dynamics and sea-level rise in West Africa: Implications for urban adaptation. Environmental Modelling & Software, 169, 105409. https://doi.org/10.1016/j.envsoft.2023.105409
Misra, A., et al. (2024). Benchmarking urban climate adaptation in African cities using AI-powered indices. Environmental Modelling & Software, 172, 105470. https://doi.org/10.1016/j.envsoft.2024.105470
Mthembu, T. T., et al. (2023). Urban resilience and digital inclusion: A South African perspective. Urban Climate, 45, 101326. https://doi.org/10.1016/j.uclim.2023.101326
Møller-Jensen, L., et al. (2023). Urban land cover mapping using Sentinel-2 and CNNs in Accra and Nairobi. Urban Climate, 44, 101321. https://doi.org/10.1016/j.uclim.2023.101321
Nzau, B. M., & Achieng, O. (2023). AI and climate change policy alignment in Nairobi: A system dynamics approach. Journal of Cleaner Production, 385, 137844. https://doi.org/10.1016/j.jclepro.2023.137844
OECD. (2023). Artificial Intelligence in Society: Ethical and Policy Considerations. https://doi.org/10.1787/82addb0f-en
Odunsi, O., & Rienow, A. (2024). Detecting urban expansion and flood risk overlap using EO and AI in Lagos. Urban Climate, 46, 101438. https://doi.org/10.1016/j.uclim.2024.101438
Revi, A., et al. (2020). Transforming cities in a changing climate: Urban adaptation pathways. Current Opinion in Environmental Sustainability, 47, 101–110. https://doi.org/10.1016/j.cosust.2020.10.006
Robin, A., et al. (2022). AI-enabled climate adaptation planning in the Global South: Opportunities and ethics. Environmental Modelling & Software, 152, 105194. https://doi.org/10.1016/j.envsoft.2022.105194
Scott, D., et al. (2018). Urban poverty and climate vulnerability in coastal South Africa. Cities, 72, 382–391. https://doi.org/10.1016/j.cities.2018.04.006
Sellami, H., Maanan, M., & Rhinane, H. (2022). Assessment of coastal vulnerability to sea-level rise using GIS and remote sensing. Journal of Hydrology, 607, 127073. https://doi.org/10.1016/j.jhydrol.2022.127073
Syeed, M. A., et al. (2022). Mapping heatwave risk in African cities using spatially explicit indices. Urban Climate, 43, 101024. https://doi.org/10.1016/j.uclim.2022.101024
UNEP. (2023). State of the Environment in Africa 2023. https://www.unep.org/resources/report/state-environment-africa-2023
UNDRR. (2022). Global Assessment Report on Disaster Risk Reduction 2022. https://www.undrr.org/publication/global-assessment-report-2022
UN-Habitat. (2022). World Cities Report 2022: Envisioning the Future of Cities. https://unhabitat.org/publications/world-cities-report-2022
Van Huyssteen, E., et al. (2021). Shaping resilient cities through integrated spatial planning. Sustainable Cities and Society, 72, 102715. https://doi.org/10.1016/j.scs.2021.102715
Weng, Q. (2021). Remote sensing for urban climate studies: Current status and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 14–32. https://doi.org/10.1016/j.isprsjprs.2021.06.002
WHO. (2023). Climate Resilient and Environmentally Sustainable Health Systems: WHO guidance. https://www.who.int/publications/i/item/9789240063051
World Bank. (2023). Urban resilience and climate-smart infrastructure in Africa: Strategies for transformation. https://openknowledge.worldbank.org/entities/publication/7f34e6bb
Yeboah, A., & Codjoe, S. N. A. (2023). Urban adaptation to climate change in Ghana: A spatially explicit perspective. Urban Climate, 44, 101272. https://doi.org/10.1016/j.uclim.2023.101272