Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches

Daniel G. Costa (), João Carlos N. Bittencourt, Franklin Oliveira, João Paulo Just Peixoto and Thiago C. Jesus
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Daniel G. Costa: SYSTEC-ARISE, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
João Carlos N. Bittencourt: CETEC, Federal University of Recôncavo da Bahia, Cruz das Almas 44380-000, Brazil
Franklin Oliveira: PPGM-UFBA, Federal University of Bahia, Salvador 40170-110, Brazil
João Paulo Just Peixoto: Federal Institute of Education, Science and Technology of Bahia, Valença 40301-015, Brazil
Thiago C. Jesus: DTEC-UEFS, State University of Feira de Santana, Feira de Santana 44036-900, Brazil

Sustainability, 2024, vol. 16, issue 2, 1-30

Abstract: In recent years, the concept of smart cities has become increasingly important in the pursuit of sustainable development goals. In general, common urban challenges have been addressed through smart-city services, and new perspectives for more sustainable cities have emerged. To realize the full potential of such smart urban environments, geospatial approaches have been used as a focal point, offering a plethora of applications that contribute to a better understanding of urban challenges and innovation potentials. Nevertheless, although significant progress has been made, different problems may arise when the available technologies and resources are not understood or even when their potentialities are not properly capitalized. This article reviews the state of the art in the field, highlighting success cases and remaining challenges in exploiting geospatial data-driven strategies, particularly when leveraging geographic information systems, satellites, and distributed sensors to produce and process geospatial data and datasets in urban scenarios. Moreover, a more organized perspective of the area is provided in this article, as well as future development trends, supporting new research efforts in this area when empowering smart cities for a more sustainable future.

Keywords: urban planning; data mining; OpenStreetMap; GIS; dataset; data science (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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