AERIAL TERRAIN MAPPING USING UNMANNED AERIAL VEHICLE APPROACH

Landslide is usually happening anywhere in Malaysia without any warning. Most of landslide occurs at manmade slope and natural slopes according to their slope gradient. Recently, Unmanned Aerial Vehicle (UAV) has been widely used for mapping purpose. The first objective of this study is to assess capabilities of UAV in the production of digital photogrammetric products based on landslide risk area along Jeli-Gerik highway. The study area covered is along Jeli-Gerik highway and its surrounding area. Primary data of ground control points (GCP) and check points (CP) were established using real time kinematic technique of Global Positioning System (GPS). All the aerial photographs were processed using digital photogrammetric software and the output in the form of digital elevation model (DEM) and orthophoto were produced. The second objective is to produce landslide risk area map using UAV technology. The risk area map separates the landslide area into three groups which are Low Risk, Medium Risk and High Risk. The parameters that were used to generate the landslide risk area map are slope, aspect and elevation. GIS based Landslide Hazard Zonation models helps not only to map and monitor landslides but also to predict future slope failures. In addition, data collected by UAV can be used to produce accurate ground surface surveys. As conclusion, UAV system has potential use for large scale mapping and could be used by public work department in order to monitoring of landslide area with low cost, less manpower and faster. The ability to detect landslide scarps will lead to a better understanding of landslide mechanisms for the propose area, thus leading to an enhanced identification of the most likely failure sites within a landslide-prone area.

In this study, the trend of unmanned aerial vehicle (UAV) technology and its applications in the mining industry were reviewed. To understand the trend of the UAV technology, the definition and classification of the UAVs, their major applications in each field, the characteristics of the sensors of the UAV were investigated. Some examples that used the UAV technology for planning, work management, facility management, safety management, and other works in the mining industry were reviewed. By introducing the UAV technology in the mining site, it is expected that the work efficiency related to mining operations would be increased. Therefore, the application of the UAV technology in the mining industry would be expanded further.

In recent years, as the cost of Unmanned Aerial Vehicle (UAV) technology has steadily decreased and advancements in data processing speed and image recognition have accelerated, UAVs have become increasingly favored in civil engineering surveying and mapping. The purpose of this paper is to study the development of UAV technology in the field of civil engineering surveying and mapping and its current application methods. By analysing the above contents, the problems still existing in the current practical use of UAV technology are studied. Through a review of relevant literature, newly developed technologies will be integrated with current UAV capabilities to propose recommendations for future improvements. This article employs literature analysis and case study methodologies to investigate the utilization of UAV technology. It delineates the current state of development and the challenges that UAV technology faces in the surveying field by examining extant academic research materials and legal regulations. It also provides a concise overview of the distinctive benefits of UAV technology. Simultaneously, the deficiencies of UAV technology in field environments are investigated, particularly focusing on data collection and accuracy in engineering surveying projects. UAV technology has obvious advantages compared with traditional methods in the field of civil engineering surveying and mapping. It can complete surveying and mapping tasks efficiently and quickly in many application scenarios. The high flexibility of UAVs and the high-precision scanning of their sensors are enough to meet the needs of current engineering surveying and mapping. The wide application of UAV technology still faces problems such as imperfect laws and regulations, insufficient surveying and mapping scope, and large surveying and mapping data. Addressing these issues requires the development of new laws and the creation of algorithms to enhance data processing capabilities.

Recent advances in structure from motion (SfM) and dense matching algorithms enable surface reconstruction from unmanned aerial vehicle (UAV) images with high spatial resolution, allowing for new insights into earth surface processes. However, accuracy issues are inherent in parallel‐axes UAV image configurations. In this study, the quality of digital elevation models (DEMs) is assessed using images from a simulated UAV flight. Five different SfM tools and three different cameras are compared. If ground control points (GCPs) are not integrated into the adjustment process with parallel‐axes image configurations, significant dome‐effect systematic errors are observed, which can be reduced based on calibration parameters retrieved from a testfield captured with convergent images immediately before or after the UAV flight. A comparison between DEMs of a soil surface generated from UAV images and terrestrial laser‐scanning data show that natural surfaces can be very accurately reconstructed from UAV images, even when GCPs are missing and simple geometric camera models are considered.

This study aims to propose a technology forecasting approach based on hierarchical S-curves. The proposed approach uses holistic forecasting by evaluating the S-curves of sub-technologies as well as the main technology under concern. A case study of unmanned aerial vehicle (UAV) technologies is conducted to demonstrate how the proposed approach works in practice. This is the first study that applies hierarchical S-curves to technology forecasting of unmanned aerial vehicle technologies in the literature. The future trend of the UAV technologies is analysed in detail through a hierarchical S-curve approach. Hierarchical S-curves are also utilised to investigate the sub-technologies of the UAV. In addition, the technology development life cycle of technology is assessed by using the three indexes namely, (1) the current technological maturity ratio (TMR), (2) estimating the number of potential patents that could be granted in the future (PPA), and (3) forecasting the expected remaining life (ERL). The results of this study indicate that the UAV technologies and their sub-technologies are at the growth stage in the technology life cycle, and most of the developments in UAV technology will have been completed by 2048. Hence, these technologies can be considered emerging technologies.

This paper underscores the significance of safety and reliability in the realm of unmanned aerial vehicle (UAV) technologies, and how regulations play a pivotal role in ensuring their responsible use. We have analyzed safety incidents and trends both in Canada and globally, noting a decline in incidents attributed to enhanced regulations. Our comparative analysis of different UAV technologies identified batteries as the most reliable power supply, Global Navigation Satellite System as the most effective navigation system, and light detection and ranging as the optimal optical sensor due to regulatory compliance and system redundancies. We also examined the regulatory framework in Canada, comparing it with the risk-based approach of the European Union Aviation Safety Agency and the efforts of Joint Authorities for Rule-making on Unmanned Systems towards global harmonization. Furthermore, we highlighted emerging trends in automation and flight control technologies, with a focus on European regulations shaping UAV automation trends. In conclusion, by adhering to best practices from other regulatory bodies, embracing emerging trends, and adopting a risk-based approach, Canada can promote the growth of the UAV industry while ensuring safety and reliability in UAV technologies.

Recent technological advance in UAV (Unmanned Aerial Vehicle) technology offers new opportunities for assessing crop situation using UAV imagery. The objective of this study was to assess if reflectance and NDVI derived from consumer-grade cameras mounted on UAVs are useful for crop condition monitoring. This study was conducted using a fixed-wing UAV(Ebee) with Cannon S110 camera from March 2015 to March 2016 in the experiment field of National Institute of Agricultural Sciences. Results were compared with ground-based recordings obtained from consumer-grade cameras and ground multi-spectral sensors. The relationship between raw digital numbers (DNs) of UAV images and measured calibration tarp reflectance was quadratic. Surface (lawn grass, stairs, and soybean cultivation area) reflectance obtained from UAV images was not similar to reflectance measured by ground-based sensors. But NDVI based on UAV imagery was similar to NDVI calculated by ground-based sensors. Change of NDVI in lawn grass and soybean cultivation area depending on method of reflectance calculation using UAV images.

The geo-localization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with non-shared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-stream network extracts UAV and satellite features independently. This approach significantly affects the efficiency of feature extraction and increases the computational load. To address these issues, we propose a novel coarse-to-fine one-stream network (OS-FPI). Our approach allows information exchange between UAV and satellite features during early image feature extraction. To improve the model's performance, the framework retains feature maps generated at different stages of the feature extraction process for the feature fusion network, and establishes additional connections between UAV and satellite feature maps in the feature fusion network. Additionally, the framework introduces offset prediction to further refine and optimize the model's prediction results based on the classification tasks. Our proposed model, boasts a similar inference speed to FPI while significantly reducing the number of parameters. It can achieve better performance with fewer parameters under the same conditions. As a one-stream network, our proposed model not only improves inference speed, but also reduces the number of parameters. Moreover, it achieves state-of-the-art performance on the UL14 dataset. Compared to previous models, our model achieved a significant 10.92-point improvement on the RDS metric, reaching 76.25. Furthermore, its performance in meter-level localization accuracy is impressive, with 82.62% improvement in 3-meter accuracy, 64.17% improvement in 5-meter accuracy, and 37.43% improvement in 10-meter accuracy. Code and models are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cjhing/OS-FPI.</uri>

Abstract—These technological advances make researchers around the world develop many new things to support the harmony of life. Humans think a lot, including in terms of technology for the advancement of the times and to maintain life, as well as for handling the unprecedented outbreak of the corona virus that spread in the end of 2019, referred to as COVID-19 by the World Health Organization (WHO), and has put the whole world in a precarious position. The impact of the COVID-19 outbreak, which was witnessed only by Chinese citizens, has become a problem of great concern for almost every country around the world makes the COVID-19 outbreak is spreading tremendous fear, overburdened healthcare system has forced most countries around the world are in a state of partial or complete lockdown. Researchers have begun to develop studies in various fields to help control the virus. Technology is also forced to adapt and develop faster to deal with the existing situation. In response to this action, with this review paper we use trusted sources to present a detailed review of specific aspects related to technological developments during the COVID-19 pandemic, especially UAV or drone technology. In this study we explore the use of technologies such as Unmanned Aerial Vehicles (UAVs) or drones that are modified for handling pandemic situations, and how these technologies are also combined with other trending technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and 5G, among others, to help reduce their impact from the COVID-19 outbreak and the challenges for the post COVID-19 period. Where UAV technology can be modified and developed according to the needs of the pandemic and development for the post-pandemic and endemic period

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

Information acquisition and rescue operation management can be facilitated by the use of unmanned aerial vehicles (UAVs) equipped with cameras and sensors that are capable of aerial photography and acquire important information from disaster-stricken areas. UAV technology is a relatively low-cost, flexible, and safe solution in missions that are difficult for humans or other systems. UAV usage in pre-and post-disaster assessment, response, and management is an active area of research. There are two important technical aspects of UAV applications in disaster situations. The first is the limited communication range. Information acquired and photography produced by UAVs are only transmitted in real-time to the ground control station (GCS) within its communication range. Remote real-time monitoring of distant areas beyond the UAV's communication range is not possible using a single UAV. It is only possible through communication among several UAVs. The second is the relatively short flight time due to limited battery capacity. UAV replacement is required in order to carry out a persistent monitoring mission. In this paper, we propose a new remote real-time monitoring system of a distant disaster area using multiple UAVs. Wireless communications between multiple UAVs constitutes a UAV network in our proposed system. Moreover, the proposed system replaces the UAVs in-flight with standby UAVs according to the remaining energy levels. We compare the performance of the proposed system with an existing UAV system. Our simulation results show that the proposed system can achieve efficient remote real-time monitoring.

Destructive methods in the field of leaf area measurement are not effective for predicting plant growth. The development of Unmanned Aerial Vehicle (UAV) and digital camera sensors makes it easier to obtain Small Format Aerial Photography (SFAP) data. Nondestructive sugarcane (Saccharum officinarum) cutting using UAV technology is more effective than destructive methods. This study aims to determine the width of the tree canopy using Small-Format Aerial Photography Near-Infrared (NIR). The research stage includes a preliminary survey of sugarcane plantations and determination of Ground Control Point (GCP), data acquisition and SFAP using a modified Syma X8 Pro UAV drone and a Nikon Coolpix a 100 modified NIR camera, infrared aerial photo processing, measurement of cane area on sugarcane objects using Image Processing and Geographic Information Systems (GIS). The results of this research block sugarcane plantation SFAP in Balecatur Village, Sleman Regency, and a map of the area of sugarcane canopy in Balecatur Village, Sleman Regency. The cane canopy area of the study area is 29.3 m2.

The use of unmanned aerial vehicles (UAV) for computer vision analysis is a significant trend in the current scenario. UAV technology is highly utilized for various purposes, including object detection, tracking, traffic management, environment monitoring, and agriculture sector, mainly due to the ease of data collection compared to conventional remote sensing methods through satellites. This study focuses on enhancing the YOLOv5 architecture to effectively detect small targets. The modifications made to the YOLOv5 framework specifically target the architecture, resulting in improved performance in object identification. The addition of a new feature fusion layer within the feature pyramid section of YOLOv5 plays a crucial role in achieving these improvements. To maintain resolution and prevent the loss of valuable feature information in the deeper sections of the network, a lateral connection is introduced, connecting this layer to an earlier part of the network. This addition ensures that crucial details and feature data are preserved throughout the network architecture. Additionally, data augmentation techniques such as image saturation and cropping are employed.

Unmanned Aerial Vehicle (UAV) technology is being used increasingly for military and civilian purposes. The primary reason for this increase is that UAVs eliminate the risk to human life in difficult and dangerous missions, are cost effective, and easily are deployed. Developments in UAV technology and decreasing costs have increased UAV usage. However, when multiple UAVs are deployed, inter UAV communication becomes complicated. For this reason, communication in multi-UAV systems is the most important problem that needs to be solved. To enable communication among UAVs without infrastructure support, a Flying Ad Hoc Network (FANET) is used. A FANET provides UAVs to fly in tandem without colliding. To ensure coordinated flight, UAVs require the location information of other UAVs. In this study, we developed a common channel multi-token circulation protocol to share location information in multi-UAV systems that communicate using a FANET. The proposed method ensures that UAVs in multi-UAV systems know each other's coordinate information with minimum error.DOI: 10.5755/j01.eie.25.1.22738

NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement