Satellite System Technology Research Articles

Ground and Space (LEO) Based GNSS Ionospheric Detection Using Chapman Function Constraint

AbstractLimited by the number and location distribution of ground stations, the ground based ionospheric detection using Global Navigation Satellite Systems (GNSS) technology suffers from low accuracy and poor reliability in some regions. To improve the performance of the global ionospheric model, this study combines the Continuously Operating Reference Station and the Low Earth Orbit (LEO) Satellite observation to conduct the ground and space‐based (G/SBased) joint ionospheric detection technique. With reference to the ionospheric radar data of the global ionospheric radio observatory, the performance of the G/SBased GNSS joint ionospheric detection technique was tested in quiet and disturbed geomagnetic environments. Results show that in the ground and space based joint mode, the distribution of ionospheric puncture points (IPPs) is uniform, thus effectively avoiding the problem of IPPs blank in the absence of ground stations. In the quiet geomagnetic environment, the ionospheric detection of the G/SBased joint mode has a remarkable performance improvement compared with the ground GNSS mode, with an average improvement of 55.51%. In the geomagnetically disturbed environment, the G/SBased joint mode still has high consistency with the ionospheric radar results, and the detection performance is better than that of the ground GNSS mode. This research shows that combining with ground GNSS and LEO satellite observation data can substantially provide the performance of GNSS ionospheric total electron content detection and can provide good data support for the construction of a global ionospheric refinement model.

Evaluation of the regional ionosphere using final, ultra-rapid, and rapid ionosphere products

Abstract The ionosphere plays a critical role in radio wave propagation, impacting satellite-based communication and navigation systems. This study evaluates near-real-time ionosphere maps (NRTIMs) derived from dual-frequency Global Positioning System (GPS) observations and validates them against established ionosphere models. Using dual-frequency Global Navigation Satellite System (GNSS) technology, the research mitigates ionospheric errors by measuring phase delays at L1 and L2 frequencies. Global ionosphere maps (GIMs) generated by the International GNSS Service (IGS) provide essential ionospheric corrections. Our approach combines accurate GPS observations with regional modeling to enhance GNSS positioning accuracy. The results demonstrate the effectiveness of the developed MATLAB algorithm in estimating ionospheric delays, showing strong convergence with GIMs. The results show a significant convergence between the Regional Ionosphere Modeling of RIM, IGS (Final Ionosphere Product), IGU (Ultra Rapid Ionosphere Product), and IGR (Rapid Ionosphere Product), as the highest average values during the 77th DOY of winter 2020 at the CPVG station were 14.753 TECU for RIM and 14.736, 14.7373 and 14.731 TECU at the CPVG station for IGS, IGU, and IGR while the average was for RIM, IGS, IGU, and IGR are respectively lower, with the lowest average values during the 190th DOY of autumn 2020 at station IZMI with a value of 3.5472 TECU for RIM, 3.5541, 3.5421 and 3.5624 TECU at IZMI station for IGS, IGU, and IGR respectively. By achieving strong agreement with existing GIMs and providing high-frequency results, the algorithm improves the reliability of GPS systems by effectively monitoring envelope disturbances. Ionic and dilute.

Detecting GNSS spoofing and Re-localization on UAV based on imagery matching

Abstract Due to rapid advancements in global navigation satellite system (GNSS), computer, and microelectronics technologies, there is a growing popularity and widespread promotion of high-performance, cost-effective intelligent UAVs across various applications. Recently, GNSS spoofing attacks have emerged as a significant obstacle hindering the long-term development of UAVs. UAVs rely heavily on unprotected GNSS signals for navigation, making them highly vulnerable to spoofing. This paper presents an algorithm that employs image matching for detecting GNSS spoofing and re-localization on UAVs using a deep learning methodology. This method functions autonomously solely reliance on camera and does not require alterations to the antenna configuration. Utilizing a camera-equipped UAV, we evaluate the likeness between real-time aerial photographs and satellite imagery by leveraging the position information provided by UAV. By identifying disparities between images taken by a spoofing affected drone and authentic ones, the spoofing can be identified using deep neural network models. Upon detecting spoofing, this paper presents a vision-based re-localization method for UAVs. Experimental results demonstrate an approximately 88.4% accuracy of our model in detecting GNSS spoofing attacks within 100 ms and 88% success rate of re-localization with the accuracy of less than 15 m. Our algorithm exclusively depends on publicly accessible satellite imagery, offering an intelligent and efficient approach for detecting UAV GNSS spoofing and re-localization in GNSS denied environment.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Multi-round decentralized dataset distillation with federated learning for Low Earth Orbit satellite communication

Satellite communication and Low Earth Orbit (LEO) satellites are important components of the 6G network, widely used for Earth observation tasks due to their low cost and short return period, making them a key technology for 6G network connectivity. Due to limitations in satellite system technology and downlink bandwidth, it is not feasible to download all high-resolution image information to ground stations. Even in existing federated learning (FL) methods, sharing well-trained parts of the model can still bottleneck with increasing model size. To address these challenges, we propose a new federated learning framework (FL-M3D) for LEO satellite communication that employs multi-round decentralized dataset distillation techniques. It allows satellites to independently extract local datasets and transmit them to ground stations instead of exchanging model parameters. Communication costs depend only on the size of the synthesized dataset and do not increase with larger models. However, the heterogeneity of satellite datasets can lead to sample ambiguity and decreased model convergence speed. Therefore, we propose distilling the datasets to mitigate the negative effects of data heterogeneity. Through experiments using real-world image datasets, FL-M3D reduces communication volume in simulated satellite networks by approximately 49.84% and achieves improved model performance.

Comparative Legal Analysis of Maritime Technology Utilization in Indonesia and China

Managing maritime borders is a vital issue that needs special attention from coastal nations such as Indonesia and China. Both designed and deployed various systems to monitor marine activity as declared in UNCLOS 1982. The goal of this study is to give a comparison of marine surveillance technologies and to analyze the legality of their usage under international law. The research methods include literature review and analysis of material from official sources, scientific publications, and relevant reports. This study discovered that Indonesia has created an integrated maritime surveillance system based on satellite, radar, and Automatic Identification System (AIS) technologies. Besides that, China is deploying more advanced and extensive maritime surveillance and monitoring technologies, such as a modern fleet of patrol vessels, aircraft, and remote monitoring equipment. Indonesia and China have generally employed marine surveillance technologies in compliance with international law principles such as territorial sovereignty, freedom of navigation, and maritime law enforcement. However, there are indications of limits on access to information and potential privacy concerns that should primarily concern the Chinese. Stronger regional coordination and collaboration are required to guarantee that the deployment of marine surveillance technologies complies with international law and current maritime security concepts.

Regulatory challenges in modernizing toll road transaction systems in Indonesia

The Government of Indonesia has modernized the toll road transaction system by implementing the multi-lane free-flow (MLFF) project, set to operate commercially by the end of 2024. This project leverages Global Navigation Satellite System (GNSS) technology to identify vehicles using toll roads and establish a transaction mechanism that allows the MLFF Project Company to charge road users according to distance, vehicle category, and tariff levels. The project has result in a complex business arrangement between the Indonesia National Toll Road Authority (INTRA), Toll Road Companies (TRCs), and the MLFF Project Company. The aim of this paper is to review the regulatory and institutional framework of the MLFF project and analyze its challenges. The methodology employed is a qualitative framework for legal research, utilizing international literature reviews and current regulatory frameworks. The study assesses the proposed transaction architecture of the project and identifies commercial, political, and other risks associated with its implementation. Based on the analysis, the research identifies opportunities for regulatory improvements and better contracting arrangements. This research provides valuable insights into the regulatory landscape and offers policy recommendations for the Government to mitigate the identified risks. This contribution is significant to the academic field as it enhances understanding regulatory and institutional challenges in implementing advanced toll road systems.

The 2023 Mw 6.8 Morocco earthquake induced atmospheric and ionospheric anomalies

Earth observations through Global Navigation Satellite System (GNSS) and Remote Sensing (RS) technologies play a significant role in natural hazard surveillance, particularly in the context of earthquake prediction and detection. This study introduces a distinctive Deep Learning (DL) based approach to identify ionospheric and atmospheric precursors, utilizing data from multiple satellite sources and provides a comprehensive analysis of spatiotemporally varying precursors, contributing to the understanding and monitoring of seismic activity in earthquake-prone regions. In our investigation of the Morocco earthquake on September 08, 2023 (Mw 6.8), we analyzed various precursors including Total Electron Content (TEC), Air Pressure (AP), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Air Temperature (AT). Our study aims to identify a synchronized anomalous window of potential earthquake precursors using Standard Deviation (STDEV), Continuous Wavelet Transform (CWT), and Long Short-Term Memory Inputs (LSTM) network. Both statistical and deep learning methods revealed abnormal fluctuations as precursors occurring within 8–9 days before the earthquake near the epicenter. Additionally, we detected geomagnetic anomalies in the ionosphere 6 days prior to and 4 days after the earthquake, coinciding with active geomagnetic storm days. This research underlined the importance of combining multiple earthquake precursors using statistical and deep learning approaches to support the understanding of the Lithosphere-Atmosphere-Ionosphere-Coupling (LAIC) phenomena.

GIS and GNSS Integration Techniques for Earth Observations

Abstract: The term "GNSS," or global navigation satellite system, refers to any satellite navigation system with global coverage. To determine your current location, GNSS devices and receivers employ geolocation information from satellite navigation systems. Trilateration is the basis for how navigation satellites operate. An object's position on the spheroid is defined by its latitude, longitude, and altitude above mean sea level. Today, the utilisation of Global Navigation Satellite Systems (GNSS) technology is required for a wide range of uses, including engineering, cities, and defence. Geographic Information Systems (GIS) can be integrated with GPS to create powerful tools for mapping and spatial analysis. This integration allows for precise display of geographic characteristics on maps, land use planning, response to emergencies, and management of natural resources. Precision agriculture is one area where GNSS and GIS are integrated, enabling farmers to gather precise position data while operating machinery. GNSS and GIS integration can be done through position-focused and technology-focused integration. Position-focused integration uses GNSS technology to locate items or features on the earth's topography, while technology-focused integration enhances the utility and accuracy of GNSS data using GIS applications and technology. Both forms of integration provide potent solutions for various industries.

Establishing a Geo-Database for Drinking Water and Its Delivery and Storage Components with an Object-Based Approach

Infrastructure facilities that serve the city as a whole and should be considered as a whole should be built in an orderly and planned manner, just as cities are. Infrastructure facilities become obsolete over time. Aging infrastructure facilities may become unserviceable over time. When the need for maintenance and repair arises, it is mandatory to renew or replace infrastructure facilities. In this case, necessary maintenance/repair and renovation works should be completed as soon as possible. These infrastructure facilities may not be transferred to maps in the digital environment and may often be managed with person-oriented information, not institutional. There is a problem for decision makers, namely, that the construction, maintenance, repair and governance of infrastructure facilities cannot be carried out systematically, on time and effectively. The only way to provide such a service is through the combined use of today’s informatics, Geographical Information System (GIS) and Global Navigation Satellite System (GNSS) technologies, unlike the classical methods of the past. The aim of the study is to effectively manage the scarce resource of drinking water and its facilities, which are an important component of infrastructure facilities, with a method that uses current mapping technologies and informatics facilities. Especially after Infrastructure for Spatial Information (INSPIRE) and the transformation of Land Administration Domain Model (LADM) to the International Organization for Standardization (ISO) standard, Turkish National Geographic Information System (TNGIS) studies and many academic studies carried out in Türkiye have been modelled with Unified Modelling Language (UML) diagrams in accordance with LADM. Similarly, within the scope of this study, UML diagrams were prepared, and then a GIS database was established. Thanks to field workers, chiefs, engineers and others working on water pipelines, all necessary data, classic, as-built and digital, were gathered. These were collected in different ways in order to conduct spatial and non-spatial analysis in the study area of Trabzon. The most important result from the study is that the entire drinking water infrastructure of Trabzon has been transferred to the system in a structure that allows spatial queries, ensuring that damage detection on water components, maintenance and repair processes are carried out in the shortest time and at the lowest cost. The investigation and application of a sensor-integrated GIS-aided system, making it possible to control and monitor the use of lost and illegal water to be controlled as well as inform consumers who will be affected by possible maintenance and repair, is recommended.

A user-friendly software package for modelling gravimetric geoid by the classical Stokes-Helmert method

With the progress in Global Navigation Satellite Systems (GNSS) technology, accurate geoid modelling has started to play an essential role in geodetic applications such as establishing height datum as a continuous surface model and related vertical control for infrastructure projects. Thus, numerous geoid modelling methods have been offered since 1990’s, each of them has its own algorithm and approximation theories. Classical Stokes-Helmert is one of the most well-known methods all over the world by geodetic communities. However, a user-friendly software package of the method is not publicly accessible on the Internet. Therefore, a compact and user-friendly software package “CSHSOFT” is developed and presented for scholars in this field. A fractionated programming strategy has been treated to build individual components striving high accuracy and computational efficiency for geoid heights. Subsequently, the CSHSOFT is simply tested to construct a geoid model in the mountainous area in Auvergne test-bed where several geoid modelling techniques are implemented. Afterward, the new geoid model of the region is externally evaluated by GNSS-levelling data in terms of rigorous orthometric heights. The fitting statistics of 2.75 cm and 0.36 ppm in absolute and relative height differences fairly indicate that the CSHSOFT is a vigorous tool for gravimetric geoid modelling, and can be comfortably employed for geoscientific and technical studies.

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

Optimising Precision Agriculture Choices for Arable Farmers in Germany and the UK: the LINKDAPA Approach

SummaryFarmer adoption of so‐called Precision Agriculture (PA) or ‘smart’ technologies in the arable sector has grown in the last few decades with a focus on Global Navigation Satellite Systems (GNSS) and variable rate technologies (VRT). This has led to increased generation of large volumes of data about fields and their crop yields which could be used to increase the environmental and financial performance for farmers. However, survey results show that cost and adaptability have been issues for many farmers in the UK and Germany that have held back such adoption. The LINKDAPA (LINKing multi‐source Data for Adoption of Precision Agriculture) project's approach sought to minimise both concerns by creating a customisable web platform that incorporates both GNSS and VRT into one, easy to use, affordable option for farmers. The project developed an online cloud‐based decision support tool which takes into account different fertiliser strategies based on novel algorithms using soil, historic yield and satellite data. Co‐created by researchers, farmers and agricultural technology firms, the LINKDAPA approach offers both economical and easy to implement solutions for farm management to mitigate resource loss‐ratios such as in fertiliser use, provide financial performance analyses, and multi‐year graphical imagery for soil mapping.

GNSS Ground deformation observation network optimization assisted using prior InSAR-derived ground surface deformation and multiscale iteration estimation

ABSTRACT Ground surface deformation must be monitored to understand and prevent geological hazards. Global navigation satellite system (GNSS) technology has been widely used in deformation monitoring; however, current GNSS station layout strategies are often subjective and lack theoretical guidance. Therefore, in this study, a GNSS deformation observation network optimization method was developed based on interferometric synthetic aperture radar (InSAR) prior to deformation assistance. This method considers the deformation distribution, cost, accuracy, topography, and actual monitoring requirements and uses kriging interpolation and multiscale iterative optimization to obtain the reference number, spatial distribution, and deformation capture accuracy of GNSS stations in areas experiencing deformation. Simulation experiments showed that the proposed method strengthened the spatial deformation monitoring ability of GNSS by 46%–68% compared with the traditional station layout methods, which ignore the distribution of the deformation. Then, based on the small baseline subset (SBAS) InSAR technology and applying the new method, we evaluated the number and location of the most suitable GNSS stations for a mining area and found that 89.6% of the error was less than 1.5 cm, which further illustrates the practicability and reliability of the proposed method. This method provides an effective supplement to station layouts for monitoring GNSS deformation.

Accurate Height Determination in Uneven Terrains with Integration of Global Navigation Satellite System Technology and Geometric Levelling: A Case Study in Lebanon

The technology for determining a point’s coordinates on the earth’s surface using the global navigation satellite system (GNSS) is becoming the norm along with ground-based methods. In this case, determining coordinates does not cause any particular difficulties. However, to identify normal heights using this technology with a given accuracy, special research is required. The fact is that satellite determinations of geodetic heights (h) over an ellipsoid surface differ from ground-based measurements of normal height (HN) over a quasi-geoid surface by a certain value called quasi-geoid height or height anomaly (ζ). In relation to determining heights of a certain territory, the concept of geoid height (N) is usually operated when dealing with a geoid model. In this work, geodetic and normal heights are determined for five control points in three different regions in Lebanon, where measurements are carried out using GNSS technology and geometric levelling. The obtained quasi-geoid heights are compared with geoid heights derived from the global Earth model EGM2008. The results obtained showed that, in the absence of gravimetric data, the combination of global Earth model data, geometric levelling for selected areas, and satellite determinations allows for the creation of a highly accurate altitude network for mountainous areas.

High Precision Positioning and Rotation Angle Estimation of a Flatbed Truck Based on BDS and Vision.

Centimeter-level localization and precise rotation angle estimation for flatbed trucks pose significant challenges in unmanned forklift automated loading scenarios. To address this issue, the study proposed a method for high-precision positioning and rotation angle estimation of flatbed trucks using the BeiDou Navigation Satellite System (BDS) and vision technology. First, an unmanned forklift equipped with a Time-of-Flight (ToF) camera and a dual-antenna mobile receiver for BDS positioning collected depth images and localization data near the front and rear endpoints of the flatbed. The Deep Dual-Resolution Network-23-slim (DDRNet-23-slim) model was used to segment the flatbed from the depth image and extract the straight lines at the edges of the flatbed using the Hough transform. The algorithm then computed the set of intersection points of the lines. A neighborhood feature vector was designed to identify the endpoint of a flatbed from a set of intersection points using feature screening. Finally, the relative coordinates of the endpoints were converted to a customized forklift navigation coordinate system by BDS positioning. A rotation angle estimation was then performed using the endpoints at the front and rear. Experiments showed that the endpoint positioning error was less than 3 cm, and the rotation angle estimation error was less than 0.3°, which verified the validity and reliability of the method.

Research of Methods to Improve the Accuracy of the Star Sensor for Global Navigation Satellite System Technology

Research of Methods to Improve the Accuracy of the Star Sensor for Global Navigation Satellite System Technology

Testing Galileo High-Accuracy Service (HAS) in Marine Operations

Global Navigation Satellite System (GNSS) technology supports all phases of maritime navigation and serves as an integral component of the Automatic Identification System (AIS) and, by extension, Vessel Traffic Service (VTS) systems. However, the accuracy of standalone GNSS is often insufficient for specific operations. To address this limitation, various regional and local-area solutions have been developed, such as Differential GNSS (DGNSS), Satellite Based Augmentation Service (SBAS) and Real Time Kinematic (RTK) techniques. A notable development in this field is the recent introduction of the Galileo High-Accuracy Service (HAS), which saw its initial service declared operational by the European Commission (EC) on 24 January 2023. Galileo HAS provides high-accuracy Precise Point Positioning (PPP) corrections (orbits, clocks and signal biases) for Galileo and GPS, enhancing real-time positioning performance at no additional cost to users. This article presents the results of the first Galileo HAS testing campaign conducted at sea using a buoy-laying vessel temporarily equipped with a Galileo HAS User Terminal. The results presented in this Article include accuracy and position availability performance achieved using the Galileo HAS User Terminal. The article also highlights challenges posed by high-power radio-frequency interference, which likely originated from the Long-Range Identification and Tracking (LRIT) system antenna on board the vessel. Furthermore, the article provides additional assessments for different phases of navigation, demonstrating better performance in slow-motion scenarios, particularly relevant to mooring and pilotage applications. In these scenarios, values for horizontal accuracy reached 0.22 m 95% and 0.13 m 68% after removing interference periods. These results are in line with the expectations outlined in the Galileo HAS Service Definition Document (SDD).

Developing an Algorithm to Improve Positioning Accuracy of Low-Cost Global Navigation Satellite System Modules

Global Navigation Satellite System (GNSS) technology is the most widely used technique for obtaining positioning and navigation information for various applications. However, GNSS is not an error free technology. Differential GNSS techniques are used to mitigate these errors and different commercial brands of GNSS receivers have been developed to avoid some errors where quality and performance depends heavily on the price tag of such advance GNSS receivers due to the fact that technological capabilities adopted and embedded in each single GNSS receiver. There are user cases where the few or more those capabilities has to keep leave behind thereby compromise the cost to benefit ratio. As an alternative to above problem, some low-cost GNSS modules are now available in the market which has quite low position accuracy but can be developed to address unique requirements of some user cases. In practical situations, Real-Time-Kinematic (RTK) positioning systems cannot be used everywhere due to its technical limitations and GNSS receivers use different levels of techniques such as moving baseline system or Satellite Based Augmentation System (SBAS). Further, heading information is also a very important parameter in marine industry for obtaining the vessel’s orientation. This research attempts to assess the capability of u-blox NEO M8N GNSS module for hydrographic surveys by developing a moving baseline GNSS configuration and simple Kalman filter based algorithm. The developed prototype was tested in both static and kinematic observations. The prototype achieved 0.5-2.5 meters of position accuracy at the 95% confidence level in static observations, while it archived around 3 meters of positioning accuracy in kinematic observations. This is a sufficient accuracy for Order 1a, Order 1b and Order 2 standards of hydrographic surveys according to the IHO S-44 guidelines.

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING POTENTIAL AS TOOLS FOR GEOREFERENCING AND FEATURES DETECTION USING UAV IMAGERY

Abstract. While Satellite imagery holds the advantage of encompassing expansive geographical regions. spanning square kilometers, its Spatial Resolution (SR) might prove inadequate for specific tasks. Conversely, Unmanned Aerial Vehicles (UAVs) excel in capturing high-resolution images with spatial resolutions in the range of a few centimeters or even millimeters. However, the accuracy of sensor locations during UAV flights is non-accurate enough by the Global Navigation Satellite Systems (GNSS) technology onboard. One of the key objectives of this research is to evaluate a technique aimed at generating precise sensor locations. This technique employs raw data from the drone's GNSS receiver and minimum Ground Control Points (GCPs) placed within a 2-meter diameter circle in the study area. The goal is to achieve accurate Digital Elevation Models (DEM) and orthomosaic images. Another focus of this research is on addressing challenges related to road lane detection. This is achieved through the enhancement of the You Only Look Once (YOLO) v3 algorithm. The proposed approach optimizes grid division, detection scales, and network architecture to enhance accuracy and real-time performance. The experimental results showcase an impressive 92.03% accuracy with a processing speed of 48 frames per second (fps), surpassing the performance of the original YOLOv3. In the rapidly evolving landscape of Artificial Intelligence (AI) and drone technology, this investigation underscores both the potential and complexities inherent in utilizing advanced AI models, such as YOLOv8, for building detection using UAV and satellite imagery. Furthermore, the research delves into robustness and real-time capabilities within building detection algorithms. The outlined strategy encompasses precise pre-processing, Field-Programmable Gate Array (FPGA) validation, and algorithm refinement. This comprehensive framework aims to elevate feature detection in intricate scenarios, ensuring accuracy, real-time efficiency, and adaptability.