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Previous studies have shown that underground natural gas storage (UGS) in California has served a critical role in meeting energy demands in California, and there is no immediate alternative. Therefore, it is important to ensure the safety of UGS infrastructure, especially considering that many of the UGS sites are using a combination of new and old wells, some of which were installed decades ago and re-purposed for UGS. The purpose of this project is to develop an integrated risk management and decision support system (IRMDSS) to manage risks associated with this heterogeneous subsurface infrastructure. The approach of the IRMDSS is to take advantage of the predictive capability of mechanistic models, with support from data acquired from advanced monitoring technologies, for evaluation and analysis of various incident scenarios or potential threats. In this project, we have demonstrated data collection by four advanced monitoring technologies. These include two downhole monitoring technologies, distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and two surface monitoring technologies, Interferometric Synthetic Aperture Radar (InSAR), and unmanned aerial vehicle (UAV). DTS and DAS data are collected continuously, providing information related to individual wells. InSAR data are collected frequently (~every 24 days), and UAV data can be collected as frequently as is practical depending on need. Together, these subsurface and surface monitoring technologies provide near real-time information useful for risk management of UGS facilities.

Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV's ({i} ) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of ({i} ) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

The use of uncrewed aerial vehicles (UAVs) in cell-free networks is poised to unleash a number of new opportunities to further improve wireless networks. However, cell-free UAV networks present major challenges related to the wireless nature of access and fronthaul links. This manuscript studies the uplink of cell-free systems where users connect to UAVs, the latter devices forwarding the information to a processing point through imperfect wireless fronthaul links. Three multiple access alternatives are considered for the fronthaul, namely frequency division multiples access, spatial division multiple access, and combinations thereof. Deterministic equivalent expressions for the spectral efficiency under these fronthaul schemes and minimum mean-square error reception are derived. Then, the optimization subproblems of (a) the 3D deployment of the UAVs, (b) the user transmit powers, and (c) the UAV transmit powers, are investigated. The joint optimization of these subproblems yields superior performance, with the 3D deployment being the main source of improvement.

Effective waterfowl management relies on the collection of relevant demographic data to inform land-management decisions; however, some types of data are difficult to obtain. For waterfowl, brood surveys are difficult to conduct because wetland habitats often obscure ducklings from being visually assessed. Here, we used Unoccupied Aerial Systems (UAS) to assess what wetland habitat characteristics influenced brood abundance in Suisun Marsh, California, USA. Using a thermal-imaging camera, we surveyed 17 wetland units that encompassed 332ha of flooded area on the premises of seven waterfowl hunting clubs during the waterfowl breeding season. Additionally, using a combination of multi-spectral imagery collected from the UAS flights and LiDAR data from the previous year, we mapped habitat composition within each unit to relate to brood observation counts. From June 3–7, 2019, we identified 113 individual broods comprising 827 ducklings. We found a positive relationship between the number of broods observed and the proportion of the unit that was flooded. We also found a positive relationship between the number of broods observed and the area of effective habitat—a metric of flooded habitat within two times the 95th-percentile Euclidean distance that all broods were observed from any vegetated cover. Brood surveys using UAS could complement the traditional Breeding Population Survey and provide local managers with fine-scale and timely information about shifts in brood abundance in the region.

ABSTRACT This article presents an incremental nonlinear model following control (INMFC) strategy for a tilt‐wing vertical take‐off and landing (VTOL) unmanned aerial vehicle (UAV). To ensure a good and robust regulation performance, a two‐loop feedback controller, based on the incremental backstepping (IBS) methodology, is used to handle uncertainties and external disturbances robustly. In order to ensure a good mode following and to aid the tracking performance of the flight control system, the first and second‐time derivatives of the desired model response, computed by a model reference (MR) system, are either directly used as feedforward signals for the outer loop or are additionally transformed for the inner‐loop by using an incremental nonlinear dynamic inversion (INDI) grounded approach to transform the signals based on known systems kinematics. In order to uniformly handle the overactuated flight vehicle in both mission model and during the transition, an operation mode‐based weighted incremental linear control allocation approach is applied for safe operation. The performance of the suggested control approach is investigated by utilizing a high‐fidelity nonlinear flight dynamics model of the tilt‐wing system. The results presented in this paper demonstrate that the proposed control approach provides significant benefits for the robust control of the considered tilt‐wing UAV. [ABSTRACT FROM AUTHOR]

Accurate crop yield prediction is crucial for optimizing cultivation practices and informing breeding decisions. Integrating UAV-acquired multispectral datasets with advanced machine learning methodologies has markedly refined the accuracy of crop yield forecasting. This study aimed to construct a robust and versatile yield prediction model for multi-genotyped oat varieties by investigating 14 modeling scenarios that combine multispectral data from four key growth stages. An ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support vector machines (SVM), Cubist, and extreme gradient boosting (XGBoost)—to predict oat yield. The results show that, for single growth stages, base models achieved R2 values within the interval of 0.02 to 0.60 and RMSEs ranging from 391.50 to 620.49 kg/ha. By comparison, the StackReg improved performance, with R2 values extending from 0.25 to 0.61 and RMSEs narrowing to 385.33 and 542.02 kg/ha. In dual-stage and multi-stage settings, the StackReg consistently surpassed the base models, reaching R2 values of up to 0.65 and RMSE values as low as 371.77 kg/ha. These findings underscored the potential of combining UAV-derived multispectral imagery with ensemble learning for high-throughput phenotyping and yield forecasting, advancing precision agriculture in oat cultivation. [ABSTRACT FROM AUTHOR]

Olive tree orchards are suffering from wildfires in many Mediterranean countries. Following a wildfire event, identifying damaged olive trees is crucial for developing effective management and restoration strategies, while rapid damage assessment can support potential compensation for producers. Moreover, the implementation of real-time health monitoring in olive groves allows producers to carry out targeted interventions, reducing production losses and preserving crop health. This research examines the use of deep learning methodologies in true-color images from Unmanned Aerial Vehicles (UAV) to detect damaged trees, including withering and desiccation of branches and leaf scorching. More specifically, the object detection and image classification computer vision techniques area applied and compared. In the object detection approach, the algorithm aims to localize and identify burned/dry and unburned/healthy olive trees, while in the image classification approach, the classifier categorizes an image showing a tree as burned/dry or unburned/healthy. Training data included true color UAV images of olive trees damaged by fire obtained by multiple cameras and multiple flight heights, resulting in various resolutions. For object detection, the Residual Neural Network was used as a backbone in an object detection approach with a Single-Shot Detector. In the image classification application, two approaches were evaluated. In the first approach, a new shallow network was developed, while in the second approach, transfer learning from pre-trained networks was applied. According to the results, the object detection approach managed to identify healthy trees with an average accuracy of 74%, while for trees with drying, the average accuracy was 69%. However, the optimal network identified olive trees (healthy or unhealthy) that the user did not detect during data collection. In the image classification approach, the application of convolutional neural networks achieved significantly better results with an F1-score above 0.94, either in the new network training approach or by applying transfer learning. In conclusion, the use of computer vision techniques in UAV images identified damaged olive trees, while the image classification approach performed significantly better than object detection. [ABSTRACT FROM AUTHOR]

Cereal crops play a critical role in global food security, but their productivity is increasingly threatened by climate change. This study evaluates the feasibility of using PlanetScope satellite imagery and a UAV equipped with the MicaSense RedEdge multispectral imaging sensor in monitoring winter wheat under various fertilizer treatments in a Mediterranean climate. Eleven fertilizer treatments, including organic-mineral fertilizer (OMF) pellets, were tested. The results show that conventional inorganic fertilization provided the highest yield (8618 kg ha⁻1), while yields from OMF showed a comparable performance to traditional fertilizers, indicating their potential for sustainable agriculture. PlanetScope data demonstrated moderate accuracy in predicting canopy cover (R2 = 0.68), crop yield (R2 = 0.54), and grain quality parameters such as protein content (R2 = 0.49), starch (R2 = 0.56), and hectoliter weight (R2 = 0.51). However, its coarser resolution limited its ability to capture finer treatment-induced variability. MicaSense, despite its higher spatial resolution, performed poorly in predicting crop components, with R2 values below 0.35 for yield and protein content. This study highlights the complementary use of remote sensing technologies to optimize wheat management and support climate-resilient agriculture through the integration of sustainable fertilization strategies. [ABSTRACT FROM AUTHOR]

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R2 = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R2 = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. [ABSTRACT FROM AUTHOR]

UAV-assisted communication facilitates efficient data collection from IoT nodes by exploiting UAVs' flexible deployment and wide coverage capabilities. In this paper, we consider a scenario in which UAVs equipped with high-precision sensors collect sensing data from ground terminals (GTs) in real-time over a wide geographic area and transmit the collected data to a ground base station (BS). Our research aims to jointly optimize the trajectory scheduling and the allocation of collection time slots for multiple UAVs, to maximize the system's data collection rates and fairness while minimizing energy consumption within the task deadline. Due to UAVs' limited sensing distance and battery energy, ensuring timely data processing in target areas presents a challenge. To address this issue, we propose a novel constraint optimization-based deep reinforcement learning–Lagrangian UAV real-time data collection management (CDRLL—RDCM) framework utilizing centralized training and distributed execution. In this framework, a CNN–GRU network units extract spatial and temporal features of the environmental information. We then introduce the PPO–Lagrangian algorithm to iteratively update the policy network and Lagrange multipliers at different time scales, enabling the learning of more effective collaborative policies for real-time UAV decision-making. Extensive simulations show that our proposed framework significantly improves the efficiency of multi-UAV collaboration and substantially reduces data staleness. [ABSTRACT FROM AUTHOR]

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases. [ABSTRACT FROM AUTHOR]

Several studies have used aerial images to predict physiological maturity (R8 stage) in soybeans (Glycine max (L.) Merr.). However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. These include the type of camera used, the number and time between flights, and whether models fitted with data obtained in one or more environments can be used to make accurate predictions in an independent environment. Applying principal component analysis (PCA), it was found that compared to the full set of 8–10 flights (R2 = 0.91–0.94; RMSE = 1.8–1.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). Similar prediction accuracy was achieved using either a multispectral or an affordable RGB camera, and the excess green index (ExG) was found to be the important feature in making predictions. Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season. [ABSTRACT FROM AUTHOR]

Blockchain technology has revolutionized the management of Unmanned Aerial Vehicle (UAV) networks by enhancing security, enabling decentralized control, and improving operational efficiency. This study assesses the efficiency of private blockchain architectures in UAV networks, specifically examining important performance metrics such as throughput, latency, scalability, and packet size. Furthermore, we evaluate the effectiveness of UAV networks when integrating private blockchain technologies, focusing particularly on key performance indicators such as area, altitude, and data rate. The scope of our work includes extensive simulations that employ a private blockchain to assess its impact on UAV operations. In the blockchain network, throughput decreased as the number of UAVs and transactions increased, while delay remained constant up to a certain point. In contrast, the UAV network saw improved throughput but increased delay with more UAVs and transactions. Changes in area and altitude had little impact on the blockchain network but increased delays in the UAV network. Higher data rates enhanced the UAV network by reducing latency and improving throughput, though this effect was less pronounced in the blockchain network. The aforementioned results highlight the potential and limitations of private blockchains in enhancing the durability and efficiency of UAV networks. [ABSTRACT FROM AUTHOR]

Forests play a critical role in the global carbon cycle, with carbon storage being an important carbon pool in the terrestrial ecosystem with tree crown size serving as a versatile ecological indicator influencing factors such as tree growth, wind resistance, shading, and carbon sequestration. They help with habitat function, herbicide application, temperature regulation, etc. Understanding the relationship between tree crown area and stock volume is crucial, as it provides a key metric for assessing the impact of land-use changes on ecological processes. Traditional ground-based stock volume estimation using DBH (Diameter at Breast Height) is labor-intensive and often impractical. However, high-resolution UAV (unmanned aerial vehicle) imagery has revolutionized remote sensing and computer-based tree analysis, making forest studies more efficient and interpretable. Previous studies have established correlations between DBH, stock volume and above-ground biomass, as well as between tree crown area and DBH. This research aims to explore the correlation between tree crown area and stock volume and automate stock volume and above-ground biomass estimation by developing an empirical model using UAV-RGB data, making forest assessments more convenient and time-efficient. The study site included a significant number of training and testing sites to ensure the performance level of the developed model. The findings underscore a significant association, demonstrating the potential of integrating drone technology with traditional forestry techniques for efficient stock volume estimation. The results highlight a strong exponential correlation between crown area and stem stock volume, with a coefficient of determination of 0.67 and mean squared error (MSE) of 0.0015. The developed model, when applied to estimate cumulative stock volume using drone imagery, demonstrated a strong correlation with an R2 of 0.75. These results emphasize the effectiveness of combining drone technology with traditional forestry methods to achieve more precise and efficient stock volume estimation and, hence, automate the process. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions. To address these challenges, this study proposes an enhanced Q-learning algorithm designed to optimize UAV path planning by combining static and dynamic obstacle avoidance features. A shortest distance priority (SDP) strategy is integrated into the learning process to minimize the distance the UAV must travel to reach the target. In addition, the root mean square propagation (RMSP) method is used to dynamically adjust the learning rate according to gradient changes, which accelerates the learning process and improves path planning efficiency. In this study, firstly, the proposed method was compared with state-of-the-art path planning techniques (including A-star, Dijkstra, and traditional Q-learning) in terms of learning time and path length through a grid-based 2D simulation environment. The results showed that the proposed method significantly improved performance compared to existing methods. In addition, 3D simulation experiments were conducted in the AirSim virtual environment. Due to the complexity of the 3D state, a deep neural network was used to calculate the Q-value based on the proposed algorithm. The results indicate that the proposed method can achieve the shortest path planning and obstacle avoidance operations in an orchard 3D simulation environment. Therefore, drones equipped with this algorithm are expected to make outstanding contributions to the development of precision agriculture through intelligent navigation and obstacle avoidance. [ABSTRACT FROM AUTHOR]

The lack of understanding of topics in uncharted research areas can often be mitigated through a careful review of the existing literature. However, when a field is not well-studied, relying on assumptions before starting a project should be avoided. This article highlights the dangers of such presumptions as demonstrated by the case of brown planthopper (Nilaparvata lugens) detection in rice field. Although unpiloted aerial vehicles (UAVs) have shown promise in various agricultural applications, their effectiveness in the early detection of brown planthopper damage was initially assumed based on the expectation of visible symptoms. The image analysis in the current study indicated that images obtained from a camera mounted on a UAV could not detect the symptoms of the very early stages of damage from brown planthoppers. An overlooked factor was whether the pest damage was uniformly distributed across an entire rice plant. If symptoms appear consistently, early detection using a top-down view from a UAV is possible; otherwise, detection may be delayed. Our findings emphasize the need for thorough preliminary research to avoid failure. By investigating the biological characteristics of the target pest and the potential limitations of detection methods, researchers can greatly improve their chances of success. We hope that readers will recognize the importance of thoroughly examining unexplored areas before embarking on new research. [ABSTRACT FROM AUTHOR]

In natural environments, open slopes commonly exhibit complicated rock outcrops, which present significant challenges for the measurement of rock discontinuities. This research employs UAV laser scanning to gather data on rock outcrops within complex topographies and proposes a multilevel classification strategy (MLCS) to identify the discontinuities using slope point clouds that encompass multiple geographic elements. The MLCS applies Random Forest (RF) to extract rock discontinuity point clouds from complex slopes (Level 1) and then conducts a preliminary segmentation of the main direction sets of the discontinuity point clouds using an improved kernel density estimation (IKDE) algorithm (Level 2). Finally, the density-based spatial clustering of applications with noise (DBSCAN) method is used to refine the segmentation of individual discontinuities (Level 3). In this strategy, RF focused on the training features of planar structures is selected to preprocess the slope point cloud. This approach accurately distinguishes discontinuities from other geographic elements, reducing the impact of complex environments. Additionally, two cases were used to validate the IKDE, which entailed consistency with the previous research results (Case 1) and the preservation of the integrity of rough and curved discontinuities (Case 2). Both of these cases demonstrate the strong adaptability of automated IKDE. These findings indicate that the proposed MLCS effectively measures discontinuities in complex rock surfaces in natural environments. Highlights: A multilevel classification strategy (MLCS) is proposed to identify discontinuities on complicated rock surfaces. An improved kernel density estimation (IKDE) automatically and quickly determines the predominant direction. The point cloud features are selected by considering feature redundancies. The proposed method is validated through a classic case and a field case. [ABSTRACT FROM AUTHOR]

The Conservation Letter in the Journal of Raptor Research examines the use of drones in raptor research, highlighting their benefits and risks. Drones can aid in monitoring nesting activities, reduce disturbance, and offer cost-effective data collection. The letter stresses the importance of guidelines to minimize impacts on raptors during drone operations, based on limited research. The Raptor Research Foundation aims to provide evidence-based examples to inform researchers and promote responsible drone use in raptor conservation. [Extracted from the article]

With the rise of deep learning networks, object detection technologies for unmanned aerial vehicle (UAV) have demonstrated outstanding performance in many application scenarios. However, current small object detection approaches overwhelmingly disregard sparse feature interactions and global context modeling, resulting in incomplete utilization and even loss of semantic information of small objects. Therefore, this study provides an advanced Assemble-and-Fusion mechanism used in DEtection TRansformer (AF-DETR), in which the aggregated global semantics are allocated across layers to augment fine-grained feature learning for small instances. Meanwhile, an adaptive context broadcasting module is designed to effectively integrate contextual information in the decoder, thus ensuring accurate detection of small objects. First, the last four stage features selected from the backbone are sent into the intra-scale feature interaction module, which performs self-attention operation on feature map of the last scale. Second, a fixed fusion module aligns and aggregates multi-scale representations prior to dissemination across layers. Features of adjoining levels then undergo transformation and consolidation within convolutional module. Finally, an enhanced adaptive context broadcasting module is introduced within the decoding MLP to incorporate aggregated semantics into individual tokens for broadcasting contextual information. Our AF-DETR achieves 49.5 % mAP50 and 29.5 % mAP50-95 on VisDrone2021 dataset, and impressive mAP50 results of 67.7% and 70.7% are achieved under RGB and Infrared modalities on the DroneVehicle dataset respectively. Extensive evaluations manifest consistent performance gains attained by our approach over state-of-the-art methods under various metrics, validated across multiple UAV perception benchmarks containing small objects under practical complex conditions. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a timely and efficient strategy for estimating biomass. However, the universality of remote sensing retrieval models with multi-feature fusion under different management practices and cultivars are unknown. The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. Six advanced modeling algorithms, including random forest (RF), partial least squares regression (PLSR), multiple linear regression (MLR), simple linear regression (SLR), ridge regression (RR), and lasso regression (LR) models, were adopted to evaluate the ability of estimating AGB by single feature and multi-feature information fusion. The results indicate the following: (1) The newly proposed variety-dependent indicator growth process ratio (GPR) can improve the model accuracy by over 20%. (2) The fusion of vegetation indices, canopy cover, growing degree days, and GPR achieved higher accuracy to estimate AGB at all growth stages compared with single feature model. (3) RF model performed best for the estimation of AGB during the whole growth period with R2 0.79 and rRMSE 0.24 ton/ha. The study demonstrated that the fusion of multi-feature coupled with the machine learning algorithm achieved the best performance for estimating potato AGB under different management practices and cultivars, which can be a potential and useful phenotyping strategy for estimating AGB at refined plot scale during the whole growth period. [ABSTRACT FROM AUTHOR]

Discontinuities exist widely in high rock slopes and threaten their stability at all times. To accurately characterize the information of rock mass discontinuities in high slopes, the layered rock slope on the north side of the Fushun West Open-pit Mine was taken as a typical sample, the high-definition images were collected using unmanned aerial vehicle (UAV) photogrammetry technology, and the Agisoft PhotoScan software was then used to generate the 3D rock slope model (i.e., 3D point cloud data) in this study. After the noise reduction processing of the generated rock slope model, the orientation and dominant grouping of the rock mass discontinuities were calculated by the KNN (K-nearest neighbor) search and the PCA (principal component analysis) algorithms, and the calculated results are calibrated with manual measurements in the local slope area. Moreover, the high-definition images containing different rock layers were selected to identify the detailed information of the rock discontinuities using the MATLAB program. This study shows that the orientation information of the rock discontinuities extracted from point cloud data are close to the corresponding result of manual measurements. There are great differences in the orientation of the discontinuities at different rock layers. The small-angle discontinuities are densely distributed in brown shale, which has a serious influence on the stability of the cracked rock slope. This research can provide a reference for the stability analysis of layered rock slopes and the prevention of landslide disasters in the Fushun West Open-Pit Mine. [ABSTRACT FROM AUTHOR]

This study innovatively employs drones equipped with air quality sensors to collect three-dimensional air quality data in a campus microenvironment. Data are accurately corrected using a BP neural network, and a cubic model is constructed using three-dimensional interpolation. Combining photogrammetry technology, this study analyzes air quality patterns, finding significant differences from macro trends. Construction activities and large electronic experimental equipment significantly increase PM2.5 levels in the air. In rainy weather, the respiration of vegetation is enhanced, leading to higher CO2 concentrations, while water bodies exhibit higher temperatures in rainy weather due to their high specific heat capacity. This research not only provides a new perspective for microenvironment air quality monitoring but also offers a scientific basis for future air quality monitoring and management. [ABSTRACT FROM AUTHOR]

Local feature matching between images is a challenging task, particularly when there are significant appearance variations, such as extreme viewpoint changes. In this work, we present LoFTRS, a deep learning-based image matching framework that integrates semantic constraints into the matching process. Our key insight is that a local feature matcher with deep layers can capture more human-intuitive and simpler-to-match features. In addition to image segmentation module, we also propose a detector-free Transformer module. It uses vector-based attention to model relevance among all features and achieves efficient and effective long-range context aggregation. Transformer module applies a relative position encoding to explicitly disclose relative distance information, further improving the representation of features. We evaluate the performance of LoFTRS comparing to various popular handcrafted and deep learning-based methods. We investigate the relationship between matching quality and the performance of subsequent processing steps, such as the accuracy and completeness of the model generated by SfM. The experimental results show that the proposed LoFTRS achieves equal or better image matching performance in terms of matching score, average track length, RMSE, and the number of 3D points. [ABSTRACT FROM AUTHOR]

This paper presents a real-time thrust fault diagnosis module for hexacopter UAVs, utilizing supervised learning and disturbance observers. The primary aim is to enhance the real-time diagnostic capabilities crucial for UAV safety and reliability. By employing disturbance observer technology, the proposed method effectively identifies and classifies thrust faults only using moment of Inertia data. The system was tested using GAZEBO simulations and real flight scenarios, demonstrating its effectiveness in accurately diagnosing faults. The research offers valuable insights into thrust fault diagnosis methodologies, contributing to improved fault-tolerant control systems for UAVs. [ABSTRACT FROM AUTHOR]

As the world continues to urbanize at an unprecedented rate, the energy demand in cities is rising. Buildings account for over 75% of all the energy consumed in cities and are responsible for over two-thirds of the emissions. Assessment of energy demand in buildings is a highly integrative endeavour, bringing together the interdisciplinary fields of energy and urban studies, along with a host of technical domains namely, geography, engineering, economics, sociology, and planning. In the last decade, several urban building energy modelling tools (UBEMs) have been developed for estimation as well as prediction of energy demand in cities. These models are useful in policymaking as they can evaluate future urban energy scenarios. However, data acquisition for generating the input database for UBEM has been a major challenge. In this review, a comprehensive assessment of the potential of remote sensing and GIS techniques for UBEM has been presented. Firstly, the most common input variables of UBEM have been identified by reviewing recent publications on UBEM and then studies related to the acquisition of data corresponding to these variables have been explored. More than 140 research papers and review articles relevant to remote sensing and GIS applications for building level data extraction in urban areas and UBEM applications have been investigated for this purpose. After going through level of details required for each of the input components of UBEM and studying the possibility of acquiring some of those data using remote sensing, it has been inferred that satellite remote sensing and Unmanned Aerial Vehicles (UAVs) have a strong potential in enhancing the input data space for UBEM but their applicability has been limited. Further, the challenges of the usage of these technologies and the possible solutions have also been presented in this study. It is recommended to utilise the existing methodologies of extracting information from remote sensing and GIS for UBEM, along with newer techniques such as machine learning and artificial intelligence. [ABSTRACT FROM AUTHOR]

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