THE SYNERGISTIC IMPACT OF ARTIFICIAL INTELLIGENCE, GPS/GNSS TECHNOLOGIES, AND ENGLISH LANGUAGE PROFICIENCY ON REMOTE SENSING APPLICATIONS
https://doi.org/10.5281/zenodo.15665972
Keywords:
Remote Sensing, Artificial Intelligence, GPS, GNSS, English Language Proficiency, Data IntegrationAbstract
This study investigates the synergistic impact of Artificial Intelligence (AI),
Global Positioning System/Global Navigation Satellite System (GPS/GNSS)
technologies, and English language proficiency on Remote Sensing (RS) applications,
particularly in non-English-speaking regions. Employing a convergent parallel
mixed-methods design, the research integrates a systematic literature review, in-depth
case studies, and survey and interview data from RS professionals. Findings reveal
that the integration of AI and GPS/GNSS enhances data processing, geolocation
accuracy, and real-time monitoring in RS. However, limited English proficiency
significantly impedes access to technical resources and international collaboration,
hindering the effective adoption of these technologies. The study underscores the
necessity of multilingual resources, targeted language training, and international
collaborations to foster more inclusive advancements in AI-integrated RS
applications globally.
References
Guo, H., et al. (2024). Remote Sensing ChatGPT: Solving Remote Sensing Tasks
with ChatGPT and Visual Models. arXiv:2401.09083. 2. Li, X., et al. (2023). Vision-Language Models in Remote Sensing: Current
Progress and Future Trends. arXiv:2305.05726. 3. Jeon, G. (2022). Artificial Intelligence-Based Learning Approaches for Remote
Sensing. Remote Sensing, 14(20), 5203. 4. Asenso-Gyambibi, D., et al. (2024). Enhancing Survey Field Data with Artificial
Intelligence: A Real-Time Kinematic GPS Study. Arabian Journal of Geosciences, 17, 184.
Xu, Y., et al. (2022). AI Security for Geoscience and Remote Sensing:
Challenges and Future Trends. arXiv:2212.09360. (Remote Sensing ChatGPT:
Solving Remote Sensing Tasks with ChatGPT and Visual Models, Vision- Language Models in Remote Sensing: Current Progress and Future Trends, Artificial Intelligence-Based Learning Approaches for Remote Sensing, Enhancing survey field data with artificial intelligence: a real-time kinematic GPS
study | Arabian Journal of Geosciences, AI Security for Geoscience and Remote
Sensing: Challenges and Future Trends
