Unmanned Aerial Vehicle Applications Research Articles

A secure and efficient heterogeneous ID‐based signcryption for unmanned aerial vehicular networking system

AbstractIn the last decade, Internet of Things opened the door to applications of unmanned aerial vehicles (UAVs). Since the data is transferred on a public channel, therefore security, privacy, and efficiency are the main concerns of UAVs communication. Signcryption is a technique to execute encryption and signature in one step. However, the usual signcryption is not applicable to UAVs with constrained nature of resources and ground station. Moreover, in particular, UAVs and ground station need a heterogeneous signcryption for UAVs to establish communication with the ground station. But, the bilinear bilinear mapping is a very costly operation, so we need pairless identity based heterogeneous signcryption. The proposed design is unforgeable and secure against chosen message attacks. The experiment shows the efficiency of the proposed method. It takes less communication and computation costs.

An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments

The use of unmanned aerial vehicles (UAVs) is increasing in transportation applications due to their high versatility and maneuverability in complex environments. Search and rescue is one of the most challenging applications of UAVs due to the non-homogeneous nature of the environmental and communication landscapes. In particular, mountainous areas pose difficulties due to the loss of connectivity caused by large valleys and the volumes of hazardous weather. In this paper, the connectivity issue in mountainous areas is addressed using a path planning algorithm for UAV relay. The approach is based on two main phases: (1) the detection of areas of interest where the connectivity signal is poor, and (2) an energy-aware and resilient path planning algorithm that maximizes the coverage links. The approach uses a viewshed analysis to identify areas of visibility between the areas of interest and the cell-towers. This allows the construction of a blockage map that prevents the UAV from passing through areas with no coverage, whilst maximizing the coverage area under energy constraints and hazardous weather. The proposed approach is validated under open-access datasets of mountainous zones, and the obtained results confirm the benefits of the proposed approach for communication networks in remote and challenging environments.

A Review of Electric UAV Visual Detection and Navigation Technologies for Emergency Rescue Missions

Sudden disasters often result in significant losses of human lives and property, and emergency rescue is a necessary response to disasters. In recent years, with the development of electric unmanned aerial vehicles (UAVs) and artificial intelligence technology, the combination of these technologies has been gradually applied to emergency rescue missions. However, in the face of the complex working conditions of emergency rescue missions, the application of electric UAV visual detection still faces great challenges, particularly in relation to a lack of GPS positioning signal in closed emergency rescue environments, as well as unforeseen obstacle avoidance and autonomous planning and searching flights. Although the combination of visual detection and visual navigation technology shows great potential and added value for use in the context of emergency rescue, at present it remains in the research and experimental stages. Consequently, this paper summarizes and discusses the current status and development of visual detection and navigation technologies for electric UAVs, as well as issues related to emergency rescue applications, with a view to accelerating the research and application of visual detection and navigation technologies for electric UAVs in emergency rescue missions. In this study, we first summarize the classification of typical disasters, analyze the application of sample UAV and configurations in typical disasters with a high frequency of occurrence, refine key electric UAV technologies in emergency rescue missions, and propose the value of exploring electric UAV visual detection and navigation technologies. Subsequently, current research on electric UAV visual detection and navigation technology is analyzed and its application in emergency rescue missions is discussed. Finally, this paper presents the problems faced in the application of electric UAV visual detection and navigation technology in urban emergency rescue environments and offers insights into future research directions.

A precise localization algorithm for unmanned aerial vehicles integrating visual-internal odometry and cartographer

Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios.

Experimental investigation of blade-shaped riblets for drag reduction on UAV applications

This study presents an experimental investigation of blade-shaped riblets for drag reduction in unmanned aerial vehicle (UAV) applications. UAVs have gained significant attention since they can perform various missions, including surveillance, reconnaissance, and package delivery. However, their aerodynamic performance, specifically the high drag associated with their exposed surfaces, remains a key challenge for enhancing their efficiency and extending their flight endurance. To address this issue, riblet geometries are proposed as a potential solution, which can reduce the turbulent skin friction drag by up to 8%. The experimental investigation involves wind tunnel testing of blade-shaped riblets, with various spacing-to-height (s/h) ratios and constant groove cross-sectional area (Ag). The riblets are designed for application on the wing, empennage, and fuselage surfaces of a UAV. The investigations are performed on a flat plate for various flow conditions, including different freestream velocities, to evaluate the drag reduction effectiveness of the riblet configuration. The drag force is measured using a force balance system and flow visualization techniques are employed to assess the position where the boundary layer has transitioned to fully turbulent. The results demonstrate the drag-reducing effect of blade-shaped and trapezoidal riblets and the different performances observed for the various s/h ratios. The cases with s/h=1 result in the smallest drag coefficients, while the cases with s/h=2 have significantly increased drag values, compared to the smooth flat plate, due to the increased wetted surface area. These findings highlight the potential of riblets as an effective drag-reduction technique for UAV applications, enabling increased endurance and/or enhanced payload capacity.

Optimisation of Multi-Type Logistics UAV Scheduling under High Demand

At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.

ASSESSMENT OF SPATIAL DATA OBTAINED BY MEANS OF THE USE OF UNMANNED AERIAL VEHICLE (UAV)

At present, in industry or research, engineering projects demand continuous spatial data from the Earth's surface. Depending on the required level of detail, collecting such data through traditional methods like topographic surveying can be prohibitively expensive. Moreover, these methods often involve extended timeframes and challenges in accessing difficult terrains, such as steep slopes. The use of an unmanned aerial vehicle (UAV), commonly referred to as a drone, equipped with sensors like photographic cameras, offers a means of consistently acquiring very high spatial resolution data from the Earth's surface. The incorporation of Ground Control Points (GCPs) ensures the data 's spatial accuracy. Photogrammetric process like structure from motion (SfM), generate various spatial products, including orthophotos and point clouds. This research centers on the application of a fixed-wing UAV to survey a 120 ha area to generate spatial data. The study demonstrates the high precision of the derived spatial data, validated against ground true data, using a flight height of 100 m over the above ground with an 80% overlap. The results include the generation of an orthophoto, a Digital Terrain Model (DTM), and a Digital Surface Model (DSM), with both terrain models derived from the point cloud.

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view.

CHALLENGES AND BARRIERS FOR UNMANNED AERIAL VEHICLE (UAV) IMPLEMENTATION IN MALAYSIAN INFRASTRUCTURE PROJECTS

The usage of Unmanned Aerial Vehicles (UAV) in infrastructure projects has inclined drastic recently due to its benefits. However, there is a slowness in transition from conventional construction approach to new innovation. Besides, there are lots of issues in current practices such as cost overrun, dangerous, delay of time completion and lack of an overall comprehensive scientific system. Furthermore, although there was high level of awareness on the advantages of using UAVs, the Malaysian construction industry are still reluctant for the changes. In response, to overcome issues that the construction industry faces, such as infrastructure projects thus, this study aims to develop a conceptual framework for promoting the applications of UAV in Malaysia infrastructure projects. The methods adopted include explanatory sequential research methods. For instance, questionnaire surveys and interviews are used to collect quantitative and qualitative data, respectively. Besides that, a refined conceptual framework of the challenges and barriers of UAV adoption in Malaysia infrastructure projects were mainly contributed by the restrictive regulatory environment and industry point of view. For example, the privacy issues and guidelines on autonomous UAV and the industry lack of confidence in the adoption of UAV and education on the adoption of the UAV. In addition, it offers some solutions for the issues that have been revealed in the preliminary conceptual framework for the challenges on UAV adoption in Malaysia infrastructure projects. For instance, amaze the contractor with the final data from the UAV. Lastly, the identify solution served as a reference for other Malaysian construction stakeholders that have not used UAVs before. The recommendations for future scholarly research include the roles of UAV technology with other technologies such as Artificial Intelligent (AI) or integration of data with other software.

A fuzzy adaptive complementary filter for attitude estimation based on norm judgment

Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.

Advancements of Unmanned Aerial Vehicle Technology in the Realm of Applied Sciences and Engineering: A Review

Due to their versatility, which is a result of technological advancements, and their capacity to carry out various civilian tasks safely and effectively, unmanned aerial vehicles (UAVs) have revolutionized applied sciences and engineering. However, UAV technological advancements in applied sciences and engineering have not been fully leveraged. This paper highlights UAV technology in the realm of applied sciences and engineering. The review covers the historical advancement, classification, benefits, and drawbacks of UAV technology applied to science and engineering. The paper begins with a summary of the advancements, then moves on to a recent historical development and an explanation of the UAV categorization criteria. The review then discusses UAV technology's advantages and drawbacks in applied science and engineering. After WWII, UAVs became increasingly versatile and advanced. Their categories follow numerous criteria. UAVs have numerous applications in applied science and engineering. Regulations stimulate UAV innovation and applications while altering them. The paper concludes by offering a comprehensive overview of UAV technology and its uses in applied sciences and engineering. Future UAV applications are likely to be much more creative and significant as the technology develops and advances.

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Using a conventional approach via visual observation on the ground, farmers encounter difficulties monitoring the entire paddy field area, and it is time-consuming to do manually. The application of unmanned aerial vehicles (UAVs) could help farmers optimise inputs such as water and fertiliser to increase yield, productivity, and quality, allowing them to manage their operations at lower costs and with minimum environmental impact. Therefore, this article aims to provide an overview of the integration of UAV and multispectral sensors in monitoring paddy growth applications based on vegetation indices and soil plant analysis development (SPAD) data. The article briefly describes current rice production in Malaysia and a general concept of precision agriculture technologies. The application of multispectral sensors integrated with UAVs in monitoring paddy growth is highlighted. Previous research on aerial imagery derived from the multispectral sensor using the normalised difference vegetation index (NDVI) is explored to provide information regarding the health condition of the paddy. Validation of the paddy growth map using SPAD data in determining the leaf’s relative chlorophyll and nitrogen content is also being discussed. Implementation of precision agriculture among low-income farmers could provide valuable insights into the practical implications of this review. With ongoing education, training and experience, farmers can eventually manage the UAV independently in the field. This article concludes with a future research direction regarding the production of growth maps for other crops using a variety of vegetation indices and map validation using the SPAD metre values.

Аналіз наявних методів та підходів до пошуку пошкодженої автобронетанкової техніки під час технічної розвідки в сучасних арміях світу

The article proposes the definition of directions and opportunities for the use of technical means of information in the performance of the tasks of rear support of troops in armed conflicts that are taking place today.
 Possible areas of application of unmanned aerial vehicles in the technical intelligence system of the National Guard of Ukraine and the Armed Forces of Ukraine are considered. The analysis of various types of unmanned aerial vehicles revealed the peculiarities of their use. The foreign experience of the use and development of unmanned technologies in the field of search and evacuation of damaged armored vehicles was analyzed. Conclusions were obtained regarding the combination of unmanned aerial vehicles with traditional means of technical intelligence, which showed their significant advantages. Prospective areas of application of aircraft in combination with technical intelligence systems of individual units of the National Guard of Ukraine and military units of the Armed Forces of Ukraine during operations and hostilities have been identified. The possibility of combining aviation technologies and means of technical intelligence in armed conflicts of modern armies of the world is also considered.
 Technical intelligence was analyzed from the point of view of the type of intelligence by tasks, forces, means and methods; and also as an organization of intelligence activities based on the use of technical means. The dependence on the nature of signals detected by technical means is shown, one of the types of technical intelligence is photo and infrared intelligence based on the interception of light waves (species intelligence).
 Species intelligence is considered as the collection of intelligence information based on the analysis of a large number of images obtained with the help of photographic, optical-electronic and radar equipment. According to its specificity, intelligence belongs to technical intelligence and includes air and space intelligence. Species reconnaissance uses photographic images taken in the visible range of the electromagnetic spectrum, infrared photography and multispectral photography. Radar images for species reconnaissance are created by equipment of various electromagnetic ranges synthesized in radar equipment. Spectral reconnaissance should be distinguished from electronic reconnaissance using optical-electronic or radar equipment that does not form an image.
 Military experts of developed countries believe that in the modern combat environment, unmanned aerial vehicles (UAVs) for conducting reconnaissance as a means of technical intelligence can more effectively and quickly solve the tasks of technical intelligence. At the same time, the time required to deliver the received intelligence information to the relevant management bodies is reduced.

Cyber attacks on UAV networks: A comprehensive survey

New technologies are constantly emerging in the modern world and changing the way we live our everyday lives. Although technology has many useful applications, there are various ways it can potentially be abused. Unmanned aerial vehicles (UAVs) are one of the most rapidly developing technologies, with potentially far-reaching consequences. A new focus on UAV applications has fueled rising concerns with regards to security, specifically around networked UAVs. UAVs may be managed from a remote place with relative ease. Essential operations involving the use of military tactics and weapons involve employing them in a variety of situations, such as reconnaissance, surveillance, and offensive, defensive, and civilian capacities. Message insertion, message manipulation, jamming, and GPS spoofing are the most commonly used cyber-attacks against these systems. Ensuring the security of electronics and communications in systems that employ several UAVs is of utmost importance to guarantee their safety and dependability in military and civilian activities. Many technological methods have been developed over the past decade for securing UAVs from cyber-attacks. This paper attempts to summaries the problems that can arise with unmanned aerial vehicles (UAVs), cyber-attacks, and the countermeasures used to protect against them. This is the first paper that details all of the past cyber-attacks on unmanned aerial vehicles (UAVs).

Autonomous predictive maintenance of quadrotor UAV with multi-actuator degradation

Abstract With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain.

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery

Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone’s specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model’s advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone.

Relative navigation with Bayesian range estimation

The applications of multiple unmanned aerial vehicles (UAVs) require high-precision and reliable relative navigation for flight formation, collision avoidance, and mission performance. Providing high-precision relative navigation is possible by fusing the observations of inertial measurement units, global navigation satellite system receivers, and radio-ranging modules. However, radio-ranging is vulnerable to various types of interference, including additive noise, nonlinear propagation, and multi-user interference. At the same time, UAVs strict energy constraints force us to look for ways to increase interference immunity without increasing the transmitting power of radio-ranging modules. This work is about improving the noise immunity of relative navigation by using a scheme tightly coupled with radio ranging and Bayesian range estimation. We describe the proposed scheme and also carry out a comparative simulation of tightly coupled and conventional, loosely coupled schemes. According to the simulation, a tightly coupled scheme has a quarter smaller RMS error compared to a loosely coupled one at an SNR below -7 dB. The obtained results demonstrate the prospects for further development of the proposed scheme.

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

The application of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in geological, geomorphological, and geotechnical studies has gained significant attention due to their versatility and capability to capture high-resolution data from challenging terrains. This research uses drone-based high-resolution photogrammetry to assess the geomechanical properties and rockfall potential of several rock scarps within a wide area of 50 ha. Traditional methods for evaluating geomechanical parameters on rock scarps involve time-consuming field surveys and measurements, which can be hazardous in steep and rugged environments. By contrast, drone photogrammetry offers a safer and more efficient approach, allowing for the creation of detailed 3D models of a cliff area. These models provide valuable insights into the topography, geological structures, and potential failure mechanisms. This research processed the acquired drone imagery using advanced geospatial software to generate accurate orthophotos and digital elevation models. These outputs analysed the key factors contributing to rockfall triggering, including identifying discontinuities, joint orientations, kinematic analysis of failures, and fracturing frequency. More than 8.9 × 107 facets, representing discontinuity planes, were recognised and analysed for the kinematic failure modes, showing that direct toppling is the most abundant rockfall type, followed by planar sliding and flexural toppling. Three different fracturation grades were also identified based on the number of planar facets recognised on rock surfaces. The approach used in this research contributes to the ongoing development of fast, practical, low-cost, and non-invasive techniques for geomechanical assessment on vertical rock scarps. In particular, the results show the effectiveness of drone-based photogrammetry for rapidly collecting comprehensive geomechanical data valid to recognise the prone areas to rockfalls in vast regions.

Unmanned Aerial Vehicles (UAVs) in Landslide Investigation and Monitoring: A Review

Over the past decade, Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for landslide studies, particularly in on-site investigations. This paper reviews UAV applications in landslide studies, with a focus on static geological characteristics, monitoring temporal and spatial dynamics, and responses post-events. We discuss the functions and limitations of various types of UAVs and sensors (RGB cameras, multi-spectral cameras, thermal IR cameras, SAR, LiDAR), outlining their roles and data processing methods in landslide applications. This review focuses on the UAVs’ roles in landslide geology surveys, emphasizing landslide mapping, modeling and characterization. For change monitoring, it provides an overview of the temporal and spatial evolution through UAV-based monitoring, shedding light on dynamic landslide processes. Moreover, this paper underscores UAVs’ crucial role in emergent response scenarios, detailing strategies and automated detection using machine learning algorithms. The discussion on challenges and opportunities highlights the need for ongoing UAV technology advancements, addressing regulatory hurdles, hover time limitations, 3D reconstruction accuracy and potential integration with technologies like UAV swarms.