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With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the laws and privacy regarding the use of drones in the urban planning context play an essential role in future smart cities. This study provides a comprehensive survey of the regulation of drone application and drone management systems, including a comparison of existing rules, management methods, and guidelines in drone operation to guarantee the safety and security of people, property, and environment. Evaluating existing regulations and laws practiced worldwide will assist in designing drone management and regulation. In Vietnam, the current rules can manage and regulate general guidelines of drone operations based on prohibited, restricted, and controlled areas within the urban context. The legislation, however, is unclear as to how it regulates smaller civilian unmanned aircraft used in the country. In the legal aspect, the potential consequences consist of the inefficiency of compensation responsibility, the violation of drone regulations, and information insecurity.

Premise of the study: Low-elevation surveys with small aerial drones (micro–unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Methods: Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. Results: We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. Discussion: The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.

Big data have arrived in archaeology, in the form of both large-scale datasets themselves and in the analytics and approaches of data science. Aerial data collected from satellite-, airborne- and UAV-mounted sensors have been particularly transformational, allowing us to capture more sites and features, over larger areas, at greater resolution, and in formerly inaccessible landscapes. However, these new means of collecting, processing, and visualizing datasets also present fresh challenges for archaeologists. What kinds of questions are these methods suited to answer, and where do they fall short? How do they articulate with the work of collecting smaller scale and lower resolution data? How are our relationships with \"local\" communities impacted by working at the scales of entire provinces, nation-states, and continents? This themed issue seeks to foster a conversation about how the unprecedented expansion of archaeological site detection, the globalization of archaeological data structures and databases, and the use of high-resolution aerial datasets are changing both the way archaeologists envision the past and the way we work in the present. In our introduction to the issue, presented here, we outline a series of conceptual and ethical issues posed by big data approaches in archaeology and provide an overview of how the nine essays that comprise this volume each address them.

Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk-Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman's ρ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2%-63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10-30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman's ρ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.

The development of unmanned aerial vehicles (UAVs) has become a revolution in the fields of data collection, surveying, monitoring, and tracking objects in the field. Many control and navigation algorithms are experimented and deployed for UAVs, especially quadrotors. Recent numerous approaches are geared towards reducing the influence of external disturbances to enhance the performance of UAVs. Nevertheless, designing cutting-edge controllers following the requirements of the applications is still a huge challenge. Based on the operating characteristics and movement principle of a quadrotor, this work reviews potential control algorithms of the current researches in the field of the quadrotor flight controller. Besides, a comparison has been made to provide an overview of the advantages and disadvantages of the mentioned methods. At last, the challenges and future directions of the quadrotor flight controller are suggested.

This study examines the perspective of 130 Czech experts regarding the adoption and impacts of four AI-driven security technologies – ground robots, drones, AI-equipped cameras and sensors, and integrated systems – for safeguarding critical infrastructure and advancing national security. The experts, drawn from academia, government, and private industry, completed a structured questionnaire capturing demographic factors such as gender, educational background, and employment sector. Analyses revealed that male respondents consistently expressed stronger approval of all technologies, whereas female experts conveyed more reserved or critical evaluations. A binary logistic regression further indicated that male respondents were nearly five times more likely to foresee improvements in resilience resulting from these automated solutions. Moreover, educational background proved influential: those with technical or engineering credentials were over eight times more inclined than their natural-science counterparts to anticipate substantial infrastructure benefits. Sectoral and contextual patterns emerged as well. The private sector displayed the highest enthusiasm for AI-enhanced security, whereas public-sector participants adopted a more measured approach. Across the board, experts expressed reservations about pervasive surveillance in ordinary public environments – particularly concerning facial recognition and drone overflight – however, they demonstrated markedly stronger support for their use in strategic or high-risk contexts, including border patrol, energy facilities, and particularly military premises or conflict zones. Female respondents rated critical infrastructure protection devices such as cameras, sensors, and drones, as well as ground robots and other surveillance equipment, as more appropriate than male respondents. Gender and education field were statistically significant variables for assessing whether introducing advanced automated security systems leads to an increase or a decrease in the resilience of critical infrastructure. Male respondents here see an increase, while respondents with a secondary educational level keep odds approximately twice higher than those with tertiary educational level that it will lead to an increase. These findings suggest that successful integration of advanced security automation hinges on aligning the optimism of technologically adept stakeholders with the caution evident among certain demographic groups, while also accounting for heightened public expectations of effective threat mitigation in critical and military settings.

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R2 = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R2 = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.

In the last decade, the attention on unmanned aerial vehicles has rapidly grown, due to their ability to help in many human activities. Among their widespread benefits, one of the most important uses regards the possibility of distributing wireless connectivity to many users in a specific coverage area. In this study, we focus our attention on these new kinds of networks, called flying ad-hoc networks. As stated in the literature, they are suitable for all emergency situations where the traditional networking paradigm may have many issues or difficulties to be implemented. The use of a software simulator can give important help to the scientific community in the choice of the right UAV/drone parameters in many different situations. In particular, in this work, we focus our main attention on the new ways of area covering and human mobility behaviors with the introduction of a UAV/drone behavior model to take into account also drones energetic issues. A deep campaign of simulations was carried out to evaluate the goodness of the proposed simulator illustrating how it works.

In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors.

In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.