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Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) or aerial drone, video, and image analysis is a developing application that has attracted substantial academic interest in several computer vision-related fields. Visual target tracking is one of the most popular and difficult study areas in computer vision. Recent deep learning (DL)-based approaches have resulted in many methods being developed and presented. This study reviewed and investigated the possibilities of using drones and computer vision techniques to undertake visual tracking in aerial video applications. Additionally, benchmark datasets, assessment criteria, and contemporary deep learning-based visual raking approaches are studied. This study aims to assess current accomplishments, emphasize the disadvantages and benefits of various existing approaches in each module, and deal with pressing research questions and difficult problems using visual tracking techniques. Finally, the direction for the next research is addressed, and the research foundation of this work is presented in aerial visual tracking. [ABSTRACT FROM AUTHOR]

One of the main objectives of modern-day surveying is to maximize the efficiency and accuracy of mapping a landscape for natural features and elevations prior to the start of a construction project. This paper focuses on a comparison between terrestrial and aerial photogrammetry and real-time kinematic global positioning systems (RTK-GPSs) in terms of elevation accuracy, data expenditure, and time for each survey to be completed. Two sites in San Diego County were chosen to be studied with a combined area of about 1.14 acres, and a total station system was used to establish 572 control points between both areas. Two of the three methods investigated produced similar results in elevation and were well within the established standard, as the terrestrial photogrammetry averaged 0.0583 feet of error, the aerial photogrammetry averaged 0.345 feet of error, and the RTK-GPS averaged 0.0432 feet of error when compared to the total station ground truth. If data consumption is not a concern, the terrestrial photogrammetric method should be preferred to the aerial photogrammetric and RTK-GPS methods in topographic mapping and land monitoring due to the increase in time efficiency and in surface model detail while keeping within the Caltrans specified tolerance of error of 0.2 feet. For general order land surveys, the photogrammetric approach utilized with a Looq scanner would provide the most efficient and cost-effective survey while staying within the 0.2 foot tolerance of error. This method also allows for the utmost clarity of the resulting point cloud when analyzing terrain, break lines, or other features in the survey area. [ABSTRACT FROM AUTHOR]

One of the main objectives of modern-day surveying is to maximize the efficiency and accuracy of mapping a landscape for natural features and elevations prior to the start of a construction project. This paper focuses on a comparison between terrestrial and aerial photogrammetry and real-time kinematic global positioning systems (RTK-GPSs) in terms of elevation accuracy, data expenditure, and time for each survey to be completed. Two sites in San Diego County were chosen to be studied with a combined area of about 1.14 acres, and a total station system was used to establish 572 control points between both areas. Two of the three methods investigated produced similar results in elevation and were well within the established standard, as the terrestrial photogrammetry averaged 0.0583 feet of error, the aerial photogrammetry averaged 0.345 feet of error, and the RTK-GPS averaged 0.0432 feet of error when compared to the total station ground truth. If data consumption is not a concern, the terrestrial photogrammetric method should be preferred to the aerial photogrammetric and RTK-GPS methods in topographic mapping and land monitoring due to the increase in time efficiency and in surface model detail while keeping within the Caltrans specified tolerance of error of 0.2 feet. For general order land surveys, the photogrammetric approach utilized with a Looq scanner would provide the most efficient and cost-effective survey while staying within the 0.2 foot tolerance of error. This method also allows for the utmost clarity of the resulting point cloud when analyzing terrain, break lines, or other features in the survey area.

We present gridded 8 km-resolution data products of the estimated stem density, basal area, and biomass of tree taxa at Euro-American settlement of the midwestern United States during the middle to late 19th century for the states of Minnesota, Wisconsin, Michigan, Illinois, and Indiana. The data come from settlement-era Public Land Survey (PLS) data (ca. 0.8-km resolution) of trees recorded by land surveyors. The surveyor notes have been transcribed, cleaned, and processed to estimate stem density, basal area, and biomass at individual points. The point-level data are aggregated within 8 km grid cells and smoothed using a generalized additive statistical model that accounts for zero-inflated continuous data and provides approximate Bayesian uncertainty estimates. The statistical modeling smooths out sharp spatial features (likely arising from statistical noise) within areas smaller than about 200 km2. Based on this modeling, presettlement Midwestern landscapes supported multiple dominant species, vegetation types, forest types, and ecological formations. The prairies, oak savannas, and forests each had distinctive structures and spatial distributions across the domain. Forest structure varied from savanna (averaging 27 Mg/ha biomass) to northern hardwood (104 Mg/ha) and mesic southern forests (211 Mg/ha). The presettlement forests were neither unbroken and massively-statured nor dominated by young forests constantly structured by broad-scale disturbances such as fire, drought, insect outbreaks, or hurricanes. Most forests were structurally between modern second growth and old growth. We expect the data product to be useful as a baseline for investigating how forest ecosystems have changed in response to the last several centuries of climate change and intensive Euro-American land use and as a calibration dataset for paleoecological proxy-based reconstructions of forest composition and structure for earlier time periods. The data products (including raw and smoothed estimates at the 8-km scale) are available at the LTER Network Data Portal as version 1.0.

Desert landscapes serve as a site for the dynamic interplay of competing historical and geopolitical narratives. In Kuwait, tribal, colonial, and global conflicts across the hinterland have mobilized state planning efforts, articulating a geospatial division between urban and desert life. Concurrently, visioning tools such as hand-drawn maps and satellite imagery have not only shaped the understanding of this desert, but have been shaped by it. Through an examination of the forces that have driven its transformation, this landscape is shown as both product and producer of cultural, social, and political complexity. The contemporary desert exists as a patchwork, the result of a disparate collection of essential narratives projected onto the territory. [ABSTRACT FROM AUTHOR]

In Indonesia, the COVID-19 pandemic has had an impact on a variety of sectors. Using all available technology for disaster mitigation is critical for pandemic prevention and control. Recent studies have uncovered the advantage of Unmanned Aerial Vehicle (UAV) or drones, particularly those with rotary wings, in dealing with the pandemic. Much effort has been devoted to developing a rotary-wing drone system as a flying platform for aerial monitoring. However, several factors must be considered when visually observing a specific region, i.e., the area's size, topographic contours, locations of special interest inside the area, approach points to the area, and the observation timeframe. Since fever is a common symptom of COVID-19, human body temperature monitoring is highlighted for fever screening, with the objective of minimizing people with high body temperatures going to the crowd. A major challenge is creating a system that can provide accurate body temperature data, which is critical for fighting the pandemic. The purpose of this paper is to present a rotary wing drone application for aerial human body temperature measurements. The paper also proposed an alternative solution based on using a portable, low-cost, Forward-looking Infrared (FLIR) thermal imaging camera. The FLIR thermal camera is incorporated into the drone's electronic system. Furthermore, thermal image data are transmitted into the ground station via a radio telemetry transceiver to allow flexible surveillance by the operator. Indoor and outdoor experiments reveal that the system has been effectively installed and provide data collection for further research. The results show that the system can be used for small-scale area aerial monitoring.

Various vision-based tasks suffer from inaccurate navigation and poor performance due to inevitable problems, such as adverse weather conditions like haze, fog, rain, snow, and clouds affecting ground and aerial navigation, as well as underwater images being degraded with blue-green tones and mud affecting marine navigation. Existing techniques in the literature typically focus on restoring specific degradations using separate models, leading to computational inefficiency. To address this, an all-in-one Multidomain Attention-based Conditional Generative Adversarial Network (MACGAN) is proposed to improve scene visibility for optimal ground, aerial, and marine navigation, using the same set of parameters across all domains. The MACGAN is a lightweight network with four encoder and decoder blocks and multiple attention blocks in between, which enhances the image restoration process by focusing on the most important features. To evaluate the effectiveness of MACGAN, extensive qualitative and quantitative comparisons are conducted with state-of-the-art image-to-image translation models, all-in-one adverse weather removal models, and single-effect removal models. The results highlight the superior performance of MACGAN in terms of scene visibility improvement and restoration quality. Additionally, MACGAN is tested on real-world unseen image domains, including smog, dust, fog, rain, snow, and lightning, further validating its generalizability and robustness. Furthermore, an ablation study is conducted to analyze the contributions of the discriminator and attention blocks within the MACGAN architecture. The results confirm that both components play significant roles in the effectiveness of MACGAN, with the discriminator ensuring adversarial training and the attention blocks effectively capturing and enhancing important image features.

At the current stage, an investigation technique on the accident takes a longer time and this causes longer traffic congestion. The aim of this study is to reconstruct a 3D model of an accident scene using an unmanned aerial vehicle (UAV). The flight parameters that have been chosen are the circular method, the double grid method, and the single grid method. All these designs can produce a good 3D model to achieve the study's objective. The methodology in this study is divided into 4 phases which are preliminary work, data acquisition, data processing, and data analysis. The main results of this study are the 3D model of the accident scene, an orthophoto map layout, and an accuracy assessment of a 3D model of reconstructed accident scene. All these parameters will be tested on accuracy based on the root mean square error (RMSE) value, comparing the UAV data and site measurement data. This objective has been tested for 10 different types of processing and different types of flight parameters. The best result among all the methods is the circular method 5 meters with ground control point (GCP) since this method has the least RMSE value which is 0.047 m. UAVs can replace the site measurement to reconstruct the accident scene. [ABSTRACT FROM AUTHOR]

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires. [ABSTRACT FROM AUTHOR]

Beavers are ecosystem engineers that create and maintain riparian wetland ecosystems in a variety of ecologic, climatic, and physical settings. Despite the large‐scale implications of ongoing beaver conservation and range expansion, relatively few landscape‐scale studies have been conducted, due in part to the significant time required to manually locate beaver dams at scale. To address this need, we developed EEAGER—an image recognition machine learning model that detects beaver complexes in aerial and satellite imagery. We developed the model in the western United States using 13,344 known beaver dam locations and 56,728 nearby locations without beaver dams. Performance assessment was performed in twelve held out evaluation polygons of known beaver occupancy but previously unmapped dam locations. These polygons represented regions similar to the training data as well as more novel landscape settings. Our model performed well overall (accuracy = 98.5%, recall = 63.03%, precision = 25.83%) in these areas, with stronger performance in regions similar to where the model had been trained. We favored recall over precision, which results in a more complete catalog of beaver dams found but also a higher incidence of false positives to be manually removed during quality control. These results have far‐reaching implications for monitoring of beaver‐based river restoration, as well as potential applications detecting other complex landforms. Plain Language Summary: Beavers are ecosystem engineers that can dramatically change the shape of the landscape and how water moves through it. They create and maintain wetland environments across North America in a wide variety of places, including mountains, deserts, coasts, forests, grasslands, shrublands, etc. Despite their large influence on the landscape, there are very few programs that monitor them at the landscape scale. This is partially due to how much time it takes to find and identify beaver dams in satellite and aerial images. To make it easier for us to find and understand the influence of beavers at larger scales, we built a model that can automatically find beaver dams in satellite and aerial imagery. While our model is trained to find beaver dams, this type of model has promise for finding other landscape features too. Key Points: Tracking the distribution and range of ecosystem engineers like beavers is increasingly important under a changing climateA neural network machine learning model can be trained to find and identify beaver dams in aerial and satellite imagery automaticallyThis type of machine learning model may have applications for finding other landforms where geospatial context is an important input [ABSTRACT FROM AUTHOR]

While native populations are often adapted to historical biotic and abiotic conditions at their home site, populations from other locations in the range may be better adapted to current conditions due to changing climates or extreme conditions in a single year. We examine whether local populations of a widespread species maintain a relative advantage over distant populations that have evolved at sites better matching the current climate. Specifically, we grew lines derived from low- and high-elevation annual populations in California and Oregon of the common monkeyflower (Erythranthe guttata) and conducted phenotypic selection analyses in low- and high-elevation common gardens in Oregon to examine relative fitness and the traits mediating relative fitness. Californian low-elevation populations have the highest relative fitness at the low-elevation site, and Californian high-elevation populations have the highest relative fitness at the high-elevation site. Relative fitness differences are mediated by selection for properly timed transitions to flowering, with selection favoring more rapid growth rates at the low-elevation site and greater vegetative biomass prior to flowering at the high-elevation site. Fitness advantages for Californian plants occur despite incurring higher herbivory at both sites than the native Oregonian plants. Our findings suggest that a lag in adaptation causes maladaptation in extreme years that may be more prevalent in future climates, but local populations still have high growth rates and thus are not yet threatened.

The paper aims to shed some light on the morphology of terrestrial and aerial life in Romanian folktales, by offering a list of creatures populating one realm or another, their description in accordance with a specific type of imaginary, the relationships established with humans. It will offer a comprehensive picture of the anthropomorphic elements that constitute the Romanian mythical fauna and express the fact that human mind has developed in relation with nature and magic. Therefore, I propose an insight into the folktale representation of the bulls (better known in Romanian folklore as aurochs), the horses and the birds which are found and depicted not only as mere animals or birds in the rich tradition of folktale, but as characters endowed with human speech, cognitive functions, their primary role in the folktale narrative being to aid the hero in his initiatory journey. Visual and symbolic features of the animals mentioned, as part of the Romanian mythical fauna, will be presented, with some aspects of their naming. Folktale names and the naming itself serve as a creative act helping us to understand the symbolic value imprinted by the community on these animal representations. Even though they have a zoomorphic appearance, they are not ruled only by the primary instincts of their species, they also have the ability to think and speak articulately. Moreover, all these types of animals belonging to the mythical fauna are relicts of the solar cult that developed in the area from which the folktales were collected. Thus, reference will be made to them as ‟fantastic animals”, in order to differentiate them from other types of characters, which only have the appearance of animals or can shape shift in animal bodies.

Introductions of transboundary animal diseases (TADs) into free-ranging wildlife can be difficult to control and devastating for domestic livestock trade. Combating a new TAD introduction in wildlife with an emergency response requires quickly limiting spread of the disease by intensely removing wild animals within a contiguous area. In the case of African swine fever virus (ASFv) in wild pigs (Sus scrofa), which has been spreading in many regions of the world, there is little information on the time- and cost-efficiency of methods for intensively and consistently culling wild pigs and recovering carcasses in an emergency response scenario. We compared the efficiencies of aerial operations, trapping, experimental toxic baiting, and ground shooting in northcentral Texas, USA during two months in 2023. Culling and recovering carcasses of wild pigs averaged a rate of 0.15 wild pigs/person hour and cost an average of $233.04/wild pig ($USD 2023) across all four methods. Aerial operations required the greatest initial investment but subsequently was the most time- and cost-efficient, costing an average of $7266 to reduce the population by a standard measure of 10 %, including recovering carcasses. Aerial operations required a ground crew of ∼7 people/helicopter to recover carcasses. Costs for reducing the population of wild pigs using trapping were similar, although took 13.5 times longer to accomplish. In cases where carcass recovery and disposal are needed (e.g., response to ASFv), a benefit of trapping was immediate carcass recovery. Toxic baiting was less efficient because both culling and carcass recovery required substantial time. We culled very few wild pigs with ground shooting in this landscape. Our results provide insight on the efficiencies of each removal method. Strategically combining removal methods may increase overall efficiency. Overall, our findings inform the preparation of resources, personnel needs, and deployment readiness for TAD responses involving wild pigs. [ABSTRACT FROM AUTHOR]

Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p < 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R2 > 0.8 (pixel-based classification) and R2 > 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops. [ABSTRACT FROM AUTHOR]

Aerial observation is usually affected by the Earth's atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing. We first developed a clear–hazy aerial image dataset addressing various types of ground; we then compared the dehazing results of some state-of-the-art methods, including the classic dark channel prior, color attenuation prior, non-local image dehazing, multi-scale convolutional neural networks, DehazeNet, and all-in-one dehazing network. We extended the most suitable method, DehazeNet, to a multi-scale form and added it into a multi-agent deep reinforcement learning network called DRL_Dehaze. DRL_Dehaze was tested on several ground types and in situations with multiple haze scales. The results show that each pixel agent can automatically select the most suitable method in multi-scale haze situations and can produce a good dehazing result. Different ground scenes may best be processed using different steps. [ABSTRACT FROM AUTHOR]

Aerial applications of pesticides for adult mosquito control have emerged as effective tools in integrated mosquito management due to wider reach compared with ground-based methods. A key determinant for efficacy is the swath or line of spray deposition. This study aimed to assess the potential of aerial buffer sprays, which specifically target mosquito habitats, in comparison to traditional full-area aerial and ground-based applications. The efficacy and cost implications of these methods were evaluated in hot spot areas of St. Johns County, FL. Mosquito populations in 4 areas, 2 for buffer aerial (St. Johns County Golf Course and World Golf Village), 1 for full aerial (Flagler Estate), and 1 for ground-based (Elkton) treatments, were assessed using the Centers for Disease Control and Prevention light traps baited with octenol. Following treatments, mosquito population reductions and service request changes were evaluated. For aerial applications, the organophosphate naled was used, while ground applications used the pyrethroid Aqualuer® 20-20. Buffer aerial applications averaged 668.5 ha, significantly smaller than full aerial treatments (3750.2 ha). Cost and volume for buffer applications were notably lower ($1,998.47 and 34.1 liters) than full treatments ($10,558.40 and 180.6 liters). Mosquito populations posttreatment showed a 63.6% reduction for buffer applications, 59% for full aerial applications, and a 52% increase for ground-based treatments. Service request reductions were varied, with a slight increase (0.13%) after buffer aerial treatments and substantial reductions after full aerial (29.4%) and ground applications (87.5%). Buffer aerial applications demonstrated potential as an effective and cost-efficient method for mosquito control in targeted habitats. Although further evaluations are essential, such strategies could reshape mosquito management practices in regions similar to the Anastasia Mosquito Control District of St. Johns County., (Copyright © 2025 by The American Mosquito Control Association, Inc.)

The safety of road construction is one of the most important concerns of construction managers for the following reasons: long-span construction operation, no fixed monitoring cameras, and huge impacts on existing traffic, while the managers still rely on manual inspection and a lack of image records. With the fast development of Unmanned Aerial Vehicle (UAV) and Artificial Intelligence (AI), monitoring safety concerns of road construction sites becomes easily accessible. This research aims to integrate UAVs and AI to establish a UAV-based road construction safety monitoring platform. In this study, road construction safety factors including constructors, construction vehicles, safety signs, and guardrails are defined and monitored to make up for the lack of image data at the road construction site. The main findings of this study include three aspects. First, the flight and photography schemes are proposed based on the UAV platform for information collection for road construction. Second, deep learning algorithms including YOLOv4 and DeepSORT are utilized to automatically detect and track safety factors. Third, a road construction dataset is established with 3594 images. The results show that the UAV-based monitoring platform can help managers with security inspection and recording images. [ABSTRACT FROM AUTHOR]

A total of 248 UAV RGB images were taken in the summer of 2021 over a representative pistachio orchard in Spain (X: 341450.3, Y: 4589731.8; ETRS89/UTM zone 30N). It is a 2.03 ha plot, planted in 2016 with Pistacia vera L. cv. Kerman grafted on UCB rootstock, with a NE–SW orientation and a 7 × 6 m triangular planting pattern. The ground was kept free of any weeds that could affect image processing. The photos (provided in JPG format) were taken using a UAV DJI Phantom Advance quadcopter in two flight missions: one planned to take nadir images (β = 0°), and another to take oblique images (β = 30°), both at 55 metres above the ground. The aerial platform incorporates a DJI FC6310 RGB camera with a 20 megapixel sensor, a horizontal field of view of 84° and a mechanical shutter. In addition, GCPs (ground control points) were collected. Finally, a high-quality 3D photogrammetric reconstruction process was carried out to generate a 3D point cloud (provided in LAS, LAZ, OBJ and PLY formats), a DEM (digital elevation model) and an orthomosaic (both in TIF format). The interest in using remote sensing in precision agriculture is growing, but the availability of reliable, ready-to-work, downloadable datasets is limited. Therefore, this dataset could be useful for precision agriculture researchers interested in photogrammetric reconstruction who want to evaluate models for orthomosaic and 3D point cloud generation from UAV missions with changing flight parameters, such as camera angle. Dataset: https://doi.org/10.5281/zenodo.7271542. Dataset License: CC-BY 4.0. [ABSTRACT FROM AUTHOR]

Germ-line specification is essential for sexual reproduction. In the ovules of most flowering plants, only a single hypodermal cell enlarges and differentiates into a megaspore mother cell (MMC), the founder cell of the female germ-line lineage. The molecular mechanisms restricting MMC specification to a single cell remain elusive. We show that the Arabidopsis transcription factor WRKY28 is exclusively expressed in hypodermal somatic cells surrounding the MMC and is required to repress these cells from acquiring MMC-like cell identity. In this process, the SWR1 chromatin remodeling complex mediates the incorporation of the histone variant H2A.Z at the WRKY28 locus. Moreover, the cytochrome P450 gene KLU, expressed in inner integument primordia, non-cell-autonomously promotes WRKY28 expression through H2A.Z deposition at WRKY28. Taken together, our findings show how somatic cells in ovule primordia cooperatively use chromatin remodeling to restrict germ-line cell specification to a single cell.

An indigenous maize landrace from the Sierra Mixe region of Oaxaca, Mexico exhibits extensive formation of aerial roots which exude large volumes of a polysaccharide-rich gel matrix or "mucilage" that harbors diazotrophic microbiota. We hypothesize that the mucilage associated microbial community carries out multiple functions, including disassembly of the mucilage polysaccharide. In situ, hydrolytic assay of the mucilage revealed endogenous arabinofuranosidase, galactosidase, fucosidase, mannosidase and xylanase activities. Screening the mucilage against plant cell wall glycan-specific monoclonal antibodies recognized the presence of carbohydrate epitopes of hemicellulosic polysaccharides like xyloglucan (both non-fucosylated and fucosylated), xylan (both substituted and unsubstituted xylan domains) and pectic-arabinogalactans, all of which are potential carbon sources for mucilage microbial residents. Mucilage metagenome annotation using MG-RAST identified the members forming the microbial community, and gene fragments with predicted functions associated with carbohydrate disassembly. Data from the in situ hydrolytic activity and monoclonal antibody screening assays were used to guide the selection of five full length genes with predicted glycosyl hydrolase function from the GenBank database that were similar to gene fragments of high relative abundance in the mucilage metagenomes. These five genes were then synthesized for recombinant production in Escherichia coli. Here we report the characterization of an α-N-arabinofuranosidase (GH51) and an oligosaccharide reducing-end xylanase (GH8) from Flavobacterium johnsoniae; an α-L-fucosidase (GH29) and a xylan β-1,4 xylosidase (GH39) from Spirosoma linguale, and a β-mannosidase (GH2) from Agrobacterium fabrum. Biochemical characterization of these enzymes revealed a β-Mannosidase that also exhibits a secondary activity towards the cleavage of galactosyl residues. We also describe two xylanases (GH8 and GH39) from underexplored glycosyl hydrolase families, one thermostable α-L-Fucosidase (GH29) and a thermostable α-N-Arabinofuranosidase (GH51).

In the modern realities of the development of the Armed Forces of Ukraine, the current issue is the search for ways to create new aerial and ground integrated reconnaissance-strike complexes that are capable of quick reconnaissance and attack of an enemy. However, a reasonable choice of such a system should not create an excessive burden for the state economy in terms of its development, maintenance, and application, and must also have the prospect of timely bringing it to combat readiness. Therefore, this study considers a methodology for substantiating the choice of an optimal option of the reconnaissance-strike complex or system for its use in operations when developing programmes and development plans of the AF of Ukraine. The proposed methodology has an integral criterion for choosing a rational option of a reconnaissance-strike complex, which combines indicators for assessing the efficiency of the combat use of these systems under various conditions, the sufficiency of the state economic capabilities for its creation, maintenance, and use, and assessing the timeliness of its creation and bringing it to combat readiness. The method is based on a mathematical model of game theory. The decision to choose a particular option of the reconnaissance-strike complex occurs in conditions of uncertainty of the combat situation and the volume of tasks that would rely on these systems when performing combat tasks.

On 15 December 2016, the European Commission (EC) declared the provision of the Galileo Initial Services (IS). This marked a historical milestone in the Galileo program, towards the reaching of its Full Operational Capability. This allows users to navigate with performance-accuracy levels either matching or exceeding those obtained with other GNSS. Under the delegation of the EC, the European Union Agency for the Space Programme (EUSPA) has assumed the role of the Galileo Service Provider. As part of this service provision, the primary mission of the Galileo Reference Centre (GRC) is to provide the EUSPA and the EC with independent means for monitoring and evaluating the performance of the Galileo services, the quality of the signals in space, and the performance of other GNSS. This mission includes significant contributions from cooperating entities in the European Union (EU) Member States (MS), Norway and Switzerland. In particular, for a detailed assessment of the Galileo performance, these contributions include (but are not limited to) periodic dynamic campaigns in three different environments (aerial, terrestrial, and maritime). These campaigns were executed in the frame of the GRC-MS Project and use multi-constellation receivers to compare the navigation performance obtained with different GNSS. The objective of this paper is to present the numerical results obtained from these campaigns, together with several considerations about the experimental setup, the methodology for the estimation of the reference («actual») trajectory, and the reasons for possible performance degradations. [ABSTRACT FROM AUTHOR]

Occlusions are one of the leading causes of data degradation in lidar. The presence of occlusions reduces the overall aesthetic quality of a point cloud, creating a signature that is specific to that viewpoint and sensor modality. Typically, datasets consist of a series of point clouds with one type of sensor and a limited range of viewpoints. Therefore, when training a dataset with a particular signature, it is challenging to infer scenes outside of the original range of the viewpoints from the training dataset. This work develops a generative network that can predict the area in which an occlusion occurs and furnish the missing points. The output is a complete point cloud that is a more general representation and agnostic to the original viewpoint. We can then use the resulting point cloud as an input for a secondary method such as semantic or instance segmentation. We propose a learned sampling technique that uses the features to inform the point sampling instead of relying strictly on spatial information. We also introduce a new network structure that considers multiple point locations and augmentations to generate parallel features. The network is tested against other methods using our aerial occlusion dataset, DALES Viewpoints Version 2, and also against other point cloud completion networks on the Point Cloud Network (PCN) dataset. We show that it reduces occlusions visually and outperforms state-of-the-art point cloud completion networks in both Chamfers and Earth Mover's Distance (EMD) metrics. We also show that using our occlusion reduction method as a pre-processing step improves semantic segmentation results compared to the same scenes processed without using our method. [ABSTRACT FROM AUTHOR]

The Vanniyar basin is located in Dharmapuri district, South India, and is severely affected by climate change. Due to changing climatic conditions and resulting poor precipitation characteristics, this district has received less rainfall over the last three decades (Dharmapuri District Statistical Hand book 2020). The objective of this investigative study is to utilize morphometric techniques and analyze their properties using GIS and image processing techniques. The linear, aerial, and relief morphometric aspects of the Vanniyar basin are analyzed. From the preliminary investigation, the study area occupies the Archean group of rocks. The drainage pattern of the Vanniyar basin is sub-dendritic to dendritic and classified into fifth-order drainage basins. The mean drainage density of the Vanniyar basin is 1.95 km/km2, which indicates that the basin has very permeable soil with intermediate drainage and low to moderate relief. The circularity ratio value reveals that the basin is circular with high to moderate relief and a structurally controlled drainage system. The elongation ratio value reveals that the basin is characterized by high susceptibility to erosion and sediment load. The stream length ratio between the streams of different orders of the Vanniyar basin shows a change in each sub-basin, indicating late youth stage of geomorphic development. From these research studies, farmers in the Vanniyar basin, river Cauvery may now comprehend their drainage basin’s geohydrological characteristics.

ABSTRACT Drones equipped with thermal sensors have shown ability to overcome some of the limitations often associated with traditional human‐occupied aerial surveys (e.g., low detection, high operational cost, human safety risk). However, their accuracy and reliability as a valid population technique have not been adequately tested. We tested the effectiveness of using a miniaturized thermal sensor equipped to a drone (thermal drone) for surveying white‐tailed deer (Odocoileus virginianus) populations using a captive deer population with a highly constrained (hereafter, known) abundance (151–163 deer, midpoint 157 [87–94 deer/km2, midpoint 90 deer/km2]) at Auburn University's deer research facility, Alabama, USA, 16–17 March 2017. We flew 3 flights beginning 30 minutes prior to sunrise and sunset (1 morning and 2 evening) consisting of 15 nonoverlapping parallel transects (18.8 km) using a small fixed‐wing aircraft equipped with a nonradiometric thermal infrared imager. Deer were identified by 2 separate observers by their contrast against background thermal radiation and body shape. Our average thermal drone density estimate (69.8 deer/km2, 95% CI = 52.2–87.6), was 78% of the mean known value of 90.2 deer/km2, exceeding most sighting probabilities observed with thermal surveys conducted using human‐occupied aircraft. Thermal contrast between animals and background was improved during evening flights and our drone‐based density estimate (82.7 deer/km2) was 92% of the mean known value. This indicates that time of flight, in conjunction with local vegetation types, determines thermal contrast and influences ability to distinguish deer. The method provides the ability to perform accurate and reliable population surveys in a safe and cost‐effective manner compared with traditional aerial surveys and is only expected to continue to improve as sensor technology and machine learning analytics continue to advance. Furthermore, the precise replicability of autonomous flights at future dates results in methodology with superior spatial precision that increases statistical power to detect population trends across surveys. © 2020 The Wildlife Society.

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This paper discusses the spatial configuration of the Roman easternmost borderland in Mesopotamia through several case studies and, particularly, with the aid of declassified aerial and satellite imagery. Satellite pictures from the 1960s and 1970s have proved to be of incredible value for the archaeological research in the Near East, contributing to a solid advancement in the understanding of large-scale phenomena on long-term periods. This is particularly true for the so-called late periods of Mesopotamian history which - traditionally - suffer from an inexplicable lack of terrain data. Although the research has consistently improved in the las decade or so, the support of remote-sensing techniques has open new and fruitful research trajectories on the matter. I will employ legacy aerial data, CORONA and HEXAGON declassified satellite imagery and U2 aerial data to assess some case studies in the region and to provide new insights on such a contested space, now roughly comprised between northeastern Syria and northwestern Iraq. [ABSTRACT FROM AUTHOR]

Five new chalcones, 4,2',4'-trihydroxy-3'-[(2E,5E)-7-methoxy-3,7-dimethyl-2,5-octadienyl]chalcone (1), (±)-4,2',4'-trihydroxy-3'-[(2E)-6-hydroxy-7-methoxy-3,7-dimethyl-2-octenyl]chalcone (2), 4,2',4'-trihydroxy-3'-[(2E)-3-methyl-5-(1,3-dioxolan-2-yl)-2-pentenyl]chalcone (3), 2',3'-furano-4-hydroxy-4'-methoxychalcone (4), and (±)-4-hydroxy-2',3'-(2,3-dihydro-2-methoxyfurano)-4'-methoxychalcone (5), were isolated from the aerial parts of Angelica keiskei Koidzumi together with eight known chalcones, 6-13, which were identified as (±)-4,2',4'-trihydroxy-3'-[(6E)-2-hydroxy-7-methyl-3-methylene-6-octenyl]chalcone (6), xanthoangelol (7), xanthoangelol F (8), xanthoangelol G (9), 4-hydroxyderricin (10), xanthoangelol D (11), xanthoangelol E (12), and xanthoangelol H (13), respectively. Chalcones 1-13 were evaluated for their promoter activity on heat shock protein 25 (hsp25, murine form of human hsp27). Compounds 1 and 6 activated the hsp25 promoter by 21.9- and 29.2-fold of untreated control at 10 μM, respectively. Further protein expression patterns of heat shock factor 1 (HSF1), HSP70, and HSP27 by 1 and 6 were examined. Compound 6 increased the expression of HSF1, HSP70, and HSP27 by 4.3-, 1.5-, and 4.6-fold of untreated control, respectively, without any significant cellular cytotoxicities, whereas 1 did not induce any expression of these proteins. As a result, 6 seems to be a prospective HSP inducer.

A total of 248 UAV RGB images were taken in the summer of 2021 over a representative pistachio orchard in Spain (X: 341450.3, Y: 4589731.8; ETRS89/UTM zone 30N). It is a 2.03 ha plot, planted in 2016 with Pistacia vera L. cv. Kerman grafted on UCB rootstock, with a NE–SW orientation and a 7 × 6 m triangular planting pattern. The ground was kept free of any weeds that could affect image processing. The photos (provided in JPG format) were taken using a UAV DJI Phantom Advance quadcopter in two flight missions: one planned to take nadir images (β = 0°), and another to take oblique images (β = 30°), both at 55 metres above the ground. The aerial platform incorporates a DJI FC6310 RGB camera with a 20 megapixel sensor, a horizontal field of view of 84° and a mechanical shutter. In addition, GCPs (ground control points) were collected. Finally, a high-quality 3D photogrammetric reconstruction process was carried out to generate a 3D point cloud (provided in LAS, LAZ, OBJ and PLY formats), a DEM (digital elevation model) and an orthomosaic (both in TIF format). The interest in using remote sensing in precision agriculture is growing, but the availability of reliable, ready-to-work, downloadable datasets is limited. Therefore, this dataset could be useful for precision agriculture researchers interested in photogrammetric reconstruction who want to evaluate models for orthomosaic and 3D point cloud generation from UAV missions with changing flight parameters, such as camera angle.

In the modern realities of the development of the Armed Forces of Ukraine, the current issue is the search for ways to create new aerial and ground integrated reconnaissance-strike complexes that are capable of quick reconnaissance and attack of an enemy. However, a reasonable choice of such a system should not create an excessive burden for the state economy in terms of its development, maintenance, and application, and must also have the prospect of timely bringing it to combat readiness. Therefore, this study considers a methodology for substantiating the choice of an optimal option of the reconnaissance-strike complex or system for its use in operations when developing programmes and development plans of the AF of Ukraine. The proposed methodology has an integral criterion for choosing a rational option of a reconnaissance-strike complex, which combines indicators for assessing the efficiency of the combat use of these systems under various conditions, the sufficiency of the state economic capabilities for its creation, maintenance, and use, and assessing the timeliness of its creation and bringing it to combat readiness. The method is based on a mathematical model of game theory. The decision to choose a particular option of the reconnaissance-strike complex occurs in conditions of uncertainty of the combat situation and the volume of tasks that would rely on these systems when performing combat tasks. [ABSTRACT FROM AUTHOR]