Machine Learning-Enhanced Protocols for High-Resolution Aerial Imagery and Geodetic-Grade GPS Calibration in Diverse Environmental Conditions
Machine Learning-Enhanced Protocols for High-Resolution Aerial Imagery and Geodetic-Grade GPS Calibration in Diverse Environmental Conditions |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Thigulla Sampath Reddy, G. Nagarajan | ||
| DOI : 10.14445/22315381/IJETT-V73I7P116 | ||
How to Cite?
Thigulla Sampath Reddy, G. Nagarajan, "Machine Learning-Enhanced Protocols for High-Resolution Aerial Imagery and Geodetic-Grade GPS Calibration in Diverse Environmental Conditions," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.189-216, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P116
Abstract
Recent advancements in drone capabilities have driven the utilization of high-resolution aerial images across numerous applications, including environmental monitoring and urban planning. Nonetheless, such imagery quality and precision are heavily dependent on drone camera settings, GPS calibration and environmental factors. We present a thorough investigation on the optimization of these variables through machine learning augmented protocols aimed at achieving standardization for high-resolution image capture and geodetic-grade GPS precision in various physical settings. We use machine learning algorithms to dynamically tune camera settings like resolution, frame rate, and lens focus according to environmental feedback, with research on optimal configurations. In addition, we propose a novel advanced calibration protocol for geodetic-grade GPS systems, using machine learning models to provide accurate position information used in applications demanding high spatial accuracy. We also consider how such environmental conditions can impact drone performance and the quality of images, providing robust operational guidelines that mitigate risks from difficult terrains and weather variability. We validated the proposed protocols through large-scale field tests in multiple environments, yielding improvements in terms of image consistency, GPS accuracy and system reliability. Additionally, we present a set of standard data processing and storage protocols that consolidate post-flight processes to retain data integrity and enable easy access for end users. Through machine learning along each layer, we improve the quality of aerial data and create a sustainable framework for drone-based imaging in changing conditions. Our generalizable protocol for drone-based imaging and the robust potential of machine learning to improve accuracy and insight from aerial data will help to standardize these approaches for scientific and industrial applications. Highlights 5Highlight and promote the use of machine learning to generate flexible, robust protocols on drone high-res images and Geo-grade GPS calibrations.
Keywords
High-Resolution Aerial Imagery, Machine Learning Protocols, Geodetic-Grade GPS Calibration, Environmental Adaptation, Drone-Based Data Standardization.
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