Use Of Unmanned Aerial Vehicles Research Articles

3D modeling of building generated by photogrammetry using Unmanned Aerial Vehicles (UAVs)

Resumo Projetos de construção são sistemas altamente complexos que apresentam recorrentemente discrepâncias entre o trabalho planejado e o trabalho realizado. Tecnologias digitais, como a fotogrametria com VANTs (drones), são ferramentas de suporte promissoras nesse contexto. Assim, este estudo tem como objetivo avaliar os procedimentos de desenvolvimento e qualidade de um modelo 3D gerado por fotogrametria através de imagens capturadas por drone, comparando-o ao modelo as-designed BIM. Para isso, realizou-se um estudo de caso em uma habitação de interesse social localizada em Camaçari-BA. Comparado ao modelo BIM projetado, o modelo fotogramétrico apresentou um desvio dimensional médio de -1,68%. O modelo também apresentou inconsistências como oclusões e deformações. O estudo mostra que tanto esse desvio como a qualidade do modelo fotogramétrico obtido podem ser consideravelmente influenciados pela forma de coleta dos dados (ex. baixa quantidade e resolução das fotos). A principal contribuição do estudo é apresentar o potencial do uso do VANT para captura de imagens para geração de um modelo de fotogrametria.

Post‐earthquake structural damage identification and safety evaluation using point clouds

AbstractEfficient and accurate post‐earthquake damage assessment of building structures is critical for ensuring the human safety and structural integrity of affected buildings. However, manual inspections and traditional visual damage identification methods are often constrained by the inaccessibility of hard‐to‐reach regions and the subjectivity of human inspectors. To overcome these issues, this paper proposes a novel approach for rapid and precise post‐earthquake damage identification and evaluation using unmanned aerial vehicle (UAV) and point cloud techniques, significantly reducing the time, labor, and errors associated with traditional methods. A comprehensive testing on full‐scale reinforced concrete shear walls was conducted to validate the precision and feasibility of this method. The point cloud models of the shear wall were generated leveraging UAV imagery and laser scanning technology with millimeter‐scale accuracy. The proposed algorithm effectively segmented each target plane of the shear wall, achieving a relatively satisfactory overall Intersection over Union of 99.25%. The relative errors of deformation between the algorithm's identification and gauges measurements were within 5%. This study successfully segmented and quantified structural surface damage, including cracks and spalling. Finally, the structural safety of the shear wall was evaluated according to ATC‐20 guidelines, using indicators such as inclination, story drift ratio, crack width, and damage area. Furthermore, proposed method was also verified in real cases.

A Lightweight Transmission Line Foreign Object Detection Algorithm Incorporating Adaptive Weight Pooling

Aerial photography using unmanned aerial vehicles (UAVs) to detect foreign objects is an important method to ensure the safety of transmission lines. However, existing detection algorithms often encounter challenges in complex environments, including limited recognition capability and high computational demands. To address these issues, this paper proposes YOLO-LAF, a lightweight foreign object detection algorithm that is based on YOLOv8n and incorporates an innovative adaptive weight pooling technique. The proposed method introduces a novel adaptive weight pooling module within the backbone network to enhance feature extraction for detecting foreign objects on transmission lines. Additionally, a multi-scale detection strategy is designed to integrate the FasterBlock and EMA modules. This combination enables the model to effectively capture both global and local image features through cross-channel interactions, thereby reducing misdetection and omission rates. Furthermore, a C2f-SCConv module is introduced in the neck network to streamline the model by eliminating redundant features, thus improving computational efficiency. Experimental results demonstrate that YOLO-LAF achieves average accuracies of 91.2% and 85.3% on the Southern Power Grid and RailFOD23 datasets, respectively, outperforming the original YOLOv8n algorithm by 2.6% and 1.8%. Moreover, YOLO-LAF reduces the number of parameters by 23.5% and 14.8% and the computational costs by 19.9% and 24.8%, respectively. These improvements demonstrate the superior detection performance of YOLO-LAF compared to other mainstream detection algorithms.

Three-dimensional spectrum coverage gap map construction in cellular networks: A non-linear estimation approach

Data collection techniques can be used to determine the coverage conditions of a cellular communication network within a given area. In such tasks, the data acquisition process faces significant challenges for larger or inaccessible locations. Such challenges can be alleviated through the use of unmanned aerial vehicles (UAVs). This way, data acquisition obstacles can be overcome to acquire and process the necessary data points with relative ease to estimate a full area coverage map for the concerned network. In this study, we formulate the problem of deploying a UAV to acquire the minimum possible measurement data points in a geographical region for the purpose of constructing a full communication coverage gap map for this region. We then devise an estimation model that utilizes the measured data samples and determines the noise/loss levels of the communication links at the other unvisited spots of the region accordingly. The proposed estimation model is based on a cascade-forward neural network to allow for both nonlinear and direct linear relationships between the input data and the output estimations. We further investigate the conventional method of using linear regression estimators to decide on the received power levels at the different locations of the examined area. Our simulation evaluations show that the proposed nonlinear estimator outperforms the conventional linear regression technique in terms of the communication coverage error level while using the minimum possible collected data points. These minimum data points are then used in constructing a complete coverage gap map visualization that demonstrates the overall network service conditions within the surveyed region.

Assessing two methods for agrochemical application (Tractor-Based and UAV) in rice fields

Introduction. Rice is one of the world’s most consumed grains in the world and is the most cultivated annual crop in Costa Rica. During rice’s growth, some kernel characteristics can be enhanced by using plant growth regulators (PGR), usually sprayed over the crop with a tractor-based spray boom (SB). Recently, the use of Unmanned aerial vehicle (UAV) sprayers, in PGR applications, has become more common. However, the lack of efficiency compared with conventional methods (as SB) urges research development. Objective. To compare spraying efficiency of SB and UAV using a PGR over rice (Oryza sativa). Materials and methods. Three treatments of different volumes with SB (SB100=100 L ha-1, SB150=150 L ha-1 and SB200=200 L ha-1), three treatments with UAV (D10=10 L ha-1, D20=20 L ha-1 and D30=30 L ha-1) and one control (no spray application) were conducted in a rice field located in Pavones, Guanacaste, Costa Rica. Five repetitions were carried out in a completely randomized design for all treatments with different PGR rates and considering spraying quality (coverage, uniformity and droplet density), rice yield, and rice milling quality as efficiency parameters. Results and discussion. SB and UAV treatments did not present statistical differences in rice milling quality and yield. Considering the spray quality for SB, SB100 complied with the required droplet density (more than 20 drops cm-2), while being the treatment with the lowest water use. As for spray quality in UAV treatments, neither had the minimum droplet density required (7.44 - 17.4 drops cm-2), in addition to a poor uniformity performance with high values of coefficient of variation (48.49 - 57.77%). Conclusion. After evaluating the efficiencies of SB and UAV treatments, SB100 is the most efficient method based only on the spray quality parameter, since rice milling quality and yield did not present statistical differences.

Balanced Multi-UAV path planning for persistent monitoring

Abstract In scenarios such as environmental data collection and traffic monitoring, timely responses to real-time situations are facilitated by persistently accessing nodes with revisiting constraints using unmanned aerial vehicles (UAVs). However, imbalanced task allocation may pose risks to the safety of UAVs and potentially lead to failures in monitoring tasks. For instance, continuous visits to nodes without replenishment may damage UAV batteries, while delays in recharging could result in missing task deadlines, ultimately causing task failures. Therefore, this study investigates the problem of achieving balanced multi-UAV path planning for persistent monitoring tasks, which has not been previously researched according to the authors’ knowledge. The main contribution of this study is the proposal of two novel indicators to assist in balancing task allocation regarding multi-UAV path planning for persistent monitoring. One of the indicators is namely the waiting factor, which reflects the urgency of a task node waiting to be accessed, and the other is the difficulty level which is introduced to measure the difficulty of tasks undertaken by a UAV. By minimizing differences in difficulty level among UAVs, we can ensure equilibrium in task allocation. For a single UAV, the ant colony initialized genetic algorithm (ACIGA) has been proposed to plan its path and obtain its difficulty level. For multiple UAVs, the K-means clustering algorithm has been improved based on difficulty levels to achieve balanced task allocation. Simulation experiments demonstrated that the difficulty level could effectively reflect the difficulty of tasks and that the proposed algorithms could enable UAVs to achieve balanced task allocation.

Precision Agriculture: UAV-Based Soil Mapping and Remote Sensing Applications

Using Unmanned Aerial Vehicle (UAV) equipped with remote sensing cameras that measure the electromagnetic spectrum of light reflected back from the ground below, this technology does soil mapping and field analysis. The way that different elements reflect different light wavelengths can be used to identify them. Reams of data on those wavelengths are gathered by multispectral image sensors, which power sophisticated AI software that can identify even the smallest variations in the elemental composition of soil. There are numerous positives to soil mapping, including helping farmers choose the best crops, applying the right fertilizers, and scheduling irrigation. Soil mapping also makes it easier to create plans for soil conservation and restoration that work. UAVs facilitate rapid deployment, enabling data collection near end-users, while delivering a more comprehensive and precise dataset compared to traditional camera-based methods. This study will provide effective technical support and decision-making assistance for future agricultural land planning.

Facets of security and safety problems and paradigms for smart aerial mobility and intelligent logistics

AbstractThe use of unmanned aerial vehicles (UAVs) for smart and speedy logistics is still relatively nascent compared to traditional delivery methods. However, it is witnessing sporadic and steady growth due to booming demands, technological advancement, and regulatory support. The intelligence and integrity of UAV systems depend largely on the underlying cognitive and cybersecurity models, which serve as both eyes and brains to perceive and respond to the myriad of scenarios around them. Smart mobility and intelligent logistic ecosystems (SMiLE) are complex and advanced technological networks which are exposed to several issues. The incorporation of UAVs for priority logistics, thereby extending the coverage and capacity of SMiLE, further heightens these vulnerabilities and questions its security, safety, and sustainability. This review scrutinizes the significant security disruptions, smartness dynamics, and sundry developments for the sustainable deployment of UAVs as an aerial logistics‐based vehicle. Using the PRISMA‐SPIDER methodology, 157 articles were selected for quantitative analysis and 20 review articles for qualitative evaluation. Security and safety issues in UAVs cut across all the layers of logistics operations: components, communication, network architecture, navigation, supply chain etc. Expanding the capacity of SMiLE using UAV demands an intentional and incremental convergence‐based integration of an agile explainable artificial framework for reliable and safety‐conscious smart mobility, a scalable and tamperproof blockchain for multi‐factor authentication, and a zero trust cybersecurity paradigm for inclusive enterprise‐based authorization.

Impact of urban environments on FANET communication: A comparative study of propagation models

The steady rise in the use of unmanned aerial vehicles (UAVs) is leading to the development of an ever-growing number of applications. In urban settings, efforts like the U-Space initiative in Europe are striving to standardize and regulate the operations of UAVs. To support these applications and further UAV research, it is essential to thoroughly understand UAV communication, both among and between UAVs. Nonetheless, we have identified a lack of studies on communication models, especially in urban areas where obstacles like tall buildings can disrupt communication. This study offers a comprehensive review of current measurement campaigns on channel models for aerial communication. In addition, we conducted experiments on (i) the separation distance between two UAVs, (ii) Multi-UAV communication and (iii) Multi-UAV to ground communication using three different city profiles in Spain (Valencia, Barcelona, and Madrid). To accomplish this, we utilized an advanced co-simulation framework that accurately models both UAV mobility (Ardusim) and communication (OMNeT++). Our results regarding UAV-to-UAV communication in a city environment indicate that: (i) the communication range, in our specific experiments, is limited to around 400 meters. Afterward, the Packet Delivery Ratio (PDR) declines significantly. (ii) Different communication models yield similar results. (iii) UAV-to-UAV communication becomes feasible at higher altitudes (e.g., 120 m), particularly in the presence of tall buildings. With respect to the Multi-UAV to ground communications, we can conclude that again, the altitude of the UAVs is paramount. Furthermore, increasing the number of UAVs providing service to the ground does increase the PDR, but only ever so slightly.

Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

This work aims to develop a surveillance mission system using unmanned aerial vehicles (UAVs) or drones when Denial-of-Service (DoS) attacks are present to disrupt normal operations for mission systems. In particular, we introduce the concept of cyber deception using honey drones (HDs) to protect the mission system from DoS attacks. HDs exhibit fake vulnerabilities and employ stronger signal strengths to lure DoS attacks, unlike the legitimate drones called mission drones (MDs) deployed for mission execution. This research formulates an optimization problem to identify an optimal set of signal strengths of HDs and MDs to best prevent the system from DoS attacks while maximizing mission performance under the resource constraints of UAVs. To solve this optimization problem, we leverage deep reinforcement learning (DRL) to achieve these multiple objectives of the mission system concerning system security and performance. Particularly, for efficient and effective parallel processing in DRL, we utilize a DRL algorithm called the Asynchronous Advantage Actor-Critic (A3C) algorithm to model attack-defense interactions. We employ a physical engine-based simulation testbed to consider realistic scenarios and demonstrate valid findings from the realistic testbed. The extensive experiments proved that our HD-based approach could achieve up to a 32% increase in mission completion, a 20% reduction in energy consumption, and a 62% decrease in attack success rates compared to existing defense strategies.

Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model

Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring methods are inefficient, expensive, and constrained by geographic conditions. Satellite remote sensing spectral analysis methods can only identify pollutant plumes and are affected by discharge timing and cloud/fog interference. Therefore, we propose a smart monitoring method for land-based sources of marine outfalls based on an improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This method can accurately identify and classify marine outfalls, offering high practical application value. Inspired by the sparse sampling method in compressed sensing, we incorporated a multi-scale dilated attention mechanism into the model and integrated dynamic snake convolutions into the C2f module. This approach enhanced the model’s detection capability for occluded and complex-feature targets while constraining the increase in computational load. Additionally, we proposed a new loss calculation method by combining Inner-IoU (Intersection over Union) and MPDIoU (IoU with Minimum Points Distance), which further improved the model’s regression speed and its ability to predict multi-scale targets. The final experimental results show that the improved model achieved an mAP50 (mean Average Precision at 50) of 87.0%, representing a 3.4% increase from the original model, effectively enabling the smart monitoring of land-based marine discharge outlets.

Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.

Mission Design and Validation of a Fixed-Wing Unmanned Aerial Vehicle for Environmental Monitoring

Climate change is becoming a worldwide emergency. In order to prevent catastrophic levels of climate change, three broad categories of action are ongoing: cutting emissions, adapting to climate impacts, and financing required adjustments. Cutting emissions requires stopping the use of fossil fuels in favor of renewable energy sources. Adapting to climate change and financing required adjustments need instruments for the understanding of the source causes and how effective the potential measures are. In this context, the use of Unmanned Aerial Vehicles for environmental monitoring is continuously increasing thanks to their ability to collect a wide range of environmental data, from the quality of air to the health status of vegetation, waters, and lands. This paper describes the research activities that are being performed for the design and development of a 100 kg Max Take Off Mass prototype zero-emission Unmanned Aerial Vehicle, named Daphne, destined for environmental monitoring, surveillance, and inspection missions. The developed prototype will drive the next industrialization of the vehicle. A particular focus is given to the design of the power system, based on the use of Proton Exchange Membrane fuel cells fueled with green hydrogen, the integration of the sensors allowing for multipurpose observations and measurements, and the design and validation of the relative multi-purpose missions via an innovative approach based on Model-Based System Engineering.

PII-GCNet: Lightweight Multi-Modal CNN Network for Efficient Crowd Counting and Localization in UAV RGB-T Images

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study’s significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment.

New Trends in the Procurement and Use of Unmanned Aerial Vehicles

Based on the presented classification of unmanned aerial vehicles (UAVs), the specifics of their use in modern conflicts are examined. The main trends in the procurement of UAVs are analyzed. It is indicated that the use of UAVs in combat is not limited to the functions of reconnaissance, target designation and strikes, as well as the detection and destruction of terrorist groups, as was the case in Afghanistan and Iraq conflicts. Thе UAVs are increasingly used within the framework of reconnaissance and strike complexes on the battlefield. The RD and production of small and mini-drones, as well as of unmanned aerial vehicles capable of remaining invulnerable to air defense systems, are actively developing. Other modern technologies of unmanned systems are being advanced. Due to the fact that in recent years the trend of massive use of small and mini-UAVs in conflicts has prevailed, supplies to the armed forces of various countries of heavy and a number of medium (tactical) high-altitude and medium-altitude drones are decreasing, while the purchases of small drones, as well as light civilian UAVs with reconfigured software, are increasing.

A Review on Deep Learning for UAV Absolute Visual Localization

In the past few years, the use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search and rescue, and even insurance. This surge in adoption can be attributed to the UAV ecosystem’s maturation, which has not only made these devices more accessible and cost effective but has also significantly enhanced their operational capabilities in terms of flight duration and embedded computing power. In conjunction with these developments, the research on Absolute Visual Localization (AVL) has seen a resurgence driven by the introduction of deep learning to the field. These new approaches have significantly improved localization solutions in comparison to the previous generation of approaches based on traditional computer vision feature extractors. This paper conducts an extensive review of the literature on deep learning-based methods for UAV AVL, covering significant advancements since 2019. It retraces key developments that have led to the rise in learning-based approaches and provides an in-depth analysis of related localization sources such as Inertial Measurement Units (IMUs) and Global Navigation Satellite Systems (GNSSs), highlighting their limitations and advantages for more effective integration with AVL. The paper concludes with an analysis of current challenges and proposes future research directions to guide further work in the field.

Estimation of Tree Diameter at Breast Height from Aerial Photographs Using a Mask R-CNN and Bayesian Regression

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions.

Thermographic inspections of solar photovoltaic plants in India using Unmanned Aerial Vehicles: Analysing the gap between theory and practice

Aerial inspection of solar PV plants using Unmanned Aerial Vehicles (UAVs) is gaining traction due to benefits such as no downtime and cost-effectiveness. This technology is proven to be the low-cost alternative to conventional approaches involving visual inspection and I-V curve tracing to identify physical damages and underperforming strings, respectively. Though the use of UAVs for thermographic solar PV inspection is a popular alternative in developed countries, its use in developing economies experience various challenges. Studies emphasizing these challenges especially in the context of rapid evolution of drones are limited. To overcome this limitation, literature scoping, a one-on-one survey, focus group discussion, and a flight campaign using a UAV with a thermal payload is conducted in India to identify the limitations. These are further categorized into Technical, Behavioural, Implementation, Pre-deployment, Deployment, and Post-deployment categories. The relevance and significance of each challenge are analysed using a hybrid multi-criteria framework developed in this study. Findings of this study highlight the importance of drone regulations, technology readiness, and workshops for drone pilots, industry professionals, and solar developers in India. This study aid developing economies in devising strategies that can promote the use of UAVs for solar PV plant commissioning activities.

The VII International Field Symposium was held in Yugra “West Siberian peatlands and the carbon cycle: past and present”

The article covers the results of the VII International Field Symposium "West Siberian Peatlands and the Carbon Cycle: Past and Present", which was held in the Khanty-Mansiysk autonomous okrug – Yugra (Khanty-Mansiysk, Beloyarsky) in August 16–26, 2024. The Symposium brought together over 180 scientists specializing in bog science, ecology and carbon balance. The main topics of the plenary and sectional sessions were: the role of bogs in the global carbon cycle, environmental modeling, biodiversity and ecology of bogs, biogeochemical cycles of natural andanthropogenically disturbed bog ecosystems, biogeochemistry of peat and bog waters, paleoecology and history of bog ecosystems development, the impact of modern climate change on forest-bog ecosystems. At the roundtable meetings, the participants discussed the possibilities of using unmanned aerial vehicles (UAVs) in the study and mapping of bog ecosystems, and shared their experience in implementation of forest-climate projects.

The use of drones for studying the behaviour of mammals

Since the advent and wide use of unmanned aerial vehicles (UAVs), they have been increasingly useful in monitoring the abundance, distribution and behaviour of terrestrial and aquatic animals. At present, this technique is actively applied to mammal research. The diversity and relative availability of drones allows for a variety of research tasks to be achieved. The use of UAVs has its advantages and disadvantages, these being discussed in the present review. The study examines the advantages of using UAVs in comparison to other methods, identifies new research opportunities opened up by drones, and emphasizes the advantages of modern analytical tools. The technical limitations of UAVs and the problem of the negative impact of this technique on mammals are discussed. The need to minimize the disturbance of animals during such research is emphasized. In addition, the work summarizes the experience of using UAVs in the studies on Russia’s theriofauna.