The article elaborates on Spatial Digital Twins (SDTs), which integrate various spatial data for real-world applications. Key challenges include ensuring seamless data acquisition and integration, which are crucial for the reliability of SDTs. It highlights that current research lacks comprehensive longitudinal studies on data capturing devices and their integration. Furthermore, advanced technologies such as AI, machine learning, and blockchain are necessary to enhance SDT capabilities. Future directions indicate a need for improved data quality and addressing security and privacy concerns as SDTs evolve.
The successful implementation of Spatial Digital Twins relies heavily on seamless data acquisition and integration across various devices, ensuring high data quality and precision.
Challenges in Spatial Digital Twins include managing multi-resolution data and leveraging advanced technologies such as AI and ML to automate data insights.
#spatial-digital-twins #data-integration #data-acquisition #artificial-intelligence #future-challenges
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