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SDI-Enabled Smart Governance: A Review (2015–2025) of IoT, AI and Geospatial Technologies—Applications and Challenges

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  • Sofianos Sofianopoulos

    (Department of Geography, Harokopio University of Athens, El. Venizelou 70, 17676 Athens, Greece)

  • Antigoni Faka

    (Department of Geography, Harokopio University of Athens, El. Venizelou 70, 17676 Athens, Greece)

  • Christos Chalkias

    (Department of Geography, Harokopio University of Athens, El. Venizelou 70, 17676 Athens, Greece)

Abstract

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies.

Suggested Citation

  • Sofianos Sofianopoulos & Antigoni Faka & Christos Chalkias, 2025. "SDI-Enabled Smart Governance: A Review (2015–2025) of IoT, AI and Geospatial Technologies—Applications and Challenges," Land, MDPI, vol. 14(7), pages 1-28, July.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:7:p:1399-:d:1693989
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    References listed on IDEAS

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    1. Iban, Muzaffer Can & Aksu, Oktay, 2020. "A model for big spatial rural data infrastructure in Turkey: Sensor-driven and integrative approach," Land Use Policy, Elsevier, vol. 91(C).
    2. M Isabel Ramos & Juan J Cubillas & Ruth M Córdoba & Lidia M Ortega, 2025. "Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-25, January.
    3. Crismeire Isbaex & Francisco dos Reis Fernandes Costa & Teresa Batista, 2025. "Application of GIS in the Maritime-Port Sector: A Systematic Review," Sustainability, MDPI, vol. 17(8), pages 1-36, April.
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