Geodesy is dependent on proper positioning and spatial information, and this is usually determined by GNSS and remote sensing techniques. Nevertheless, the conventional techniques are usually limited in terms of accuracy, particularly where the environment is complex. This paper explores the use of GNSS data in conjunction with remote sensing in order to enhance geodetic measurements. This paper is aimed at assessing the success of a combination of GNSS and remote sensing data (LiDAR and satellite images) in terms of high-precision geodetic data, in terms of accuracy, reliability, and processing efficiency. The research works combine the GNSS data with the remote sensing data through Kalman filtering and least squares adjustment methods. Information from different GNSS systems and remote sensing platforms had undergone pre-processing and fusion to develop high-accuracy geospatial models. The most significant results are a decrease in RMSE to 0.5 meters, MAE to 0.3 meters, SD to 0.4 meters, an average error of 0.02 degrees, and a processing rate of 30 seconds. The statistical analysis confirmed the high level of precision that was improved in comparison with the old GNSS methods (p-value < 0.05). The combination of GNSS and remote sensing data will greatly improve the accuracy and efficiency of the geodetic measurements, and it can be easily applied in urban planning, environmental monitoring, and disaster management. Research in the future ought to address real-time observation and application of other remote sensing technologies.
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