Geospatial Technology as Integrated Decision Infrastructure: Applications, Persistent Challenges and Future Directions
TL;DR
It is argued that future progress depends less on incremental accuracy gains than on reproducible multi-source workflows, explicit uncertainty, trustworthy and explainable geospatial artificial intelligence, privacy-preserving governance, interoperable standards and evaluation in the institutions that ultimately use the evidence.
Abstract
Geospatial technology has evolved from a set of specialised mapping tools into an integrated decision infrastructure that links Earth observation, geographic information systems, satellite positioning, uncrewed aerial systems, volunteered geographic information, cloud computing and geospatial artificial intelligence. This critical narrative review examines how that integration is changing evidence production and decision support across agriculture, environmental and biodiversity monitoring, disaster risk management, urban and transport planning, and public health. Literature published primarily from 2000 to 9 June 2026 was selected through live web-based scholarly discovery, DOI and bibliographic verification, citation chaining and targeted searches of accessible scholarly records. The evidence indicates that geospatial systems are most valuable when they combine complementary observations across scales rather than relying on a single sensor, platform or algorithm. Their strongest contributions are spatially explicit monitoring, prioritisation, scenario analysis and repeated observation, while the weakest parts of many workflows remain ground-reference quality, uncertainty propagation, transferability, interoperability and evaluation against operational outcomes. Cloud platforms and deep learning have expanded computational reach, but they can also conceal provenance, amplify geographic bias and encourage benchmark-driven optimisation that does not translate reliably across places. Volunteered data and urban digital twins extend participation and real-time representation, yet introduce uneven coverage, privacy, governance and accountability concerns. The review argues that future progress depends less on incremental accuracy gains than on reproducible multi-source workflows, explicit uncertainty, trustworthy and explainable geospatial artificial intelligence, privacy-preserving governance, interoperable standards and evaluation in the institutions that ultimately use the evidence. Geospatial technology should therefore be judged not only by spatial resolution or predictive performance, but by whether it produces defensible, equitable and actionable knowledge across heterogeneous real-world settings.