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Recent advances in hydrocarbon exploration have been made in the Member Deng-2 marginal microbial mound-bank complex reservoirs of the Dengying Formation in the western Sichuan Basin, SW China, where the depositional process is regarded confusing. The microfacies, construction types, and depositional model of the Member Deng-2 marginal microbial mound-bank complex have been investigated using unmanned aerial vehicle photography, outcrop section investigation, thin section identification, and seismic reflections in the southwestern Sichuan Basin. The microbialite lithologic textures in this region include thrombolite, dendrolite, stromatolite, fenestral stromatolite, spongiostromata stone, oncolite, aggregated grainstone, and botryoidal grapestone. Based on the comprehensive analysis of "depositional fabricselithologyemicrofacies", an association between a fore mound, mound framework, and back mound subfacies has been proposed based on water depth, current direction, energy level and lithologic assemblages. The microfacies of the mound base, mound core, mound flank, mound cap, and mound flat could be recognized among the mound framework subfacies. Two construction types of marginal microbial mound-bank complex have been determined based on deposition location, mound scale, migration direction, and sedimentary facies association. Type Jinkouhe microbial mound constructions (TJMMCs) develop along the windward margin owing to their proximity to the seaward subfacies fore mound, with a northeastwardly migrated microbial mound on top of the mud mound, exhibiting the characteristics of large-sized mounds and small-sized banks in the surrounding area. Type E'bian microbial mound constructions (TEMMCs) primarily occur on the leeward margin, resulting from the presence of onshore back mound subfacies, with the smaller southwestward migrated microbial mounds existing on a thicker microbial flat. The platform margin microbial mound depositional model can be correlated with certain lateral comparison profile and seismic reflection structures in the 2D seismic section, which can provide references for future worldwide exploration. Microbial mounds with larger buildups and thicker vertical reservoirs are typically targeted on the windward margin, while small-sized microbial mounds and flats with better lateral connections are typically focused on the leeward margin. [ABSTRACT FROM AUTHOR]

On February 25th, 2022, an earthquake with a magnitude of 6.2 in Nagari Kajai, Talamau District, West Pasaman Regency, resulted in considerable structural damage because it originated from a shallow earthquake with a hypocenter depth of 12.6 km and is a manifestation of the strike-slip faulting system linked to the Sumatra Fault System (SFS), which is adjacent to the Angkola segment, Barumun Segment, Sumpur Segment, and Sianok Segment is a newly-found active fault in this area. All of Nagari Kajai (Kajai Village) Sub-district is covered in volcanic deposits, whether they are new (quaternary deposits) or a combination of old (tertiary deposits) and young (quaternary deposits) as a product from the Talamau Mountain and the Maninjau Mountain. These geological activities will provide a good impression of the topography and geomorphology of a newly found active fault, The Kajai Fault. The geomorphological analysis is used to get new characteristic features for this new fault line continuation using Landsat 8 imagery and supported by aerial photography using Unmanned Aerial Vehicles (UAV). A 2D subsurface image was obtained from the Wenner-Schlumberger geoelectric survey configuration to get an excellent subsurface interpretation of lithology in this area. From this interpretation, the Kajai Fault is present in a valley filled with volcanic products and a narrow diversion of the Batang Talu River. The geoelectrical survey images show a double juxtaposition from two interpreted faults, namely F1 and F2, which run through The Kajai village and are suspected to be one of the shallow earthquake sources. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV), specifically a quadcopter is publicly popular which it provides services in different applications such as aerial delivery, aerial photography, military, weather forecasting and more examples to date. A Proportional-Integral-Derivative (PID) controller is one of the control techniques that can provide stabilization and reliable trajectory tracking. However, proper PID gains are needed to ensure a stable flight and it should be hybridized or improved to increase the robustness, reliability, and stabilization during flight. In this paper, an intelligent PID controller using neural network is proposed based on Levenberg-Marquardt feedforward neural network training method. The PID gains are initialized using different ranges according to the optimal gains generated by Particle Swarm Optimization, and this contributes towards a good training performance using Mean Square Error (MSE) evaluation. The trained network takes desired output and references as input data to calculate the required combination of PID gains as the output. The including of the response characteristics as the input data for the network, together with reference, error, and control input is the significance of the work. The performance of this work is presented using MSE performances, attitudes and altitude stabilization, and trajectory tracking reliability through error index performances. The simulation results graphically prove that the proposed controller provides better stability with reduced overshoot and settling times. Disturbance rejection is also enhanced by 1.7% compared to manual tuned PID controller. The reliability of the proposed controller highlights avenues for further exploration in AI-driven control strategies for quadcopter systems. [ABSTRACT FROM AUTHOR]

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network's focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to improve adaptability for small objects. We replace conventional downsampling with a Spatial-to-Depth Convolutional Combination (CSPDC) module to mitigate the loss of intricate feature details related to small objects. Furthermore, we propose a Spatial Context Pyramid with Multi-Scale Attention (SCPMA) module, which captures spatial and channel-dependent features of small targets acrossmultiple scales. This module enhances the perception of spatial contextual features and the utilization of multiscale feature information. On the Visdrone2023 and UAVDT datasets, MSC-YOLO achieves remarkable results, outperforming the baseline method YOLOv7 by 3.0% in terms of mean average precision (mAP). The MSCYOLO algorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets in UAV aerial photography, providing strong support for practical applications. [ABSTRACT FROM AUTHOR]

Monitoring biodiversity over time and space is essential for effective conservation of habitats, processes, and dependent organisms. Estimating large abundances of individuals can be challenging (e.g. birds and mammals), demanding efficient and effective methods. The Makgadikgadi Pans in north-eastern Botswana have high concentrations of breeding and feeding flamingos (Phoenicopterus roseus and Phoeniconaias minor), among the world’s most important breeding sites. No published estimates of flamingos exist since 2009, with historical estimates providing limited details on methods. We developed a semi-supervised machine learning method for counting a large feeding concentration of flamingos (2 June 2019) in aerial photographs from northern Sua Pan of the Makgadikgadi Pans. We also analysed rainfall and flooding frequency and extent, using satellite imagery, estimating likely frequency of these flamingo concentrations. Lastly, we reviewed the site’s global importance as flamingo breeding habitat. Our analysis successfully provided an estimate of 372,172 to 689,473 flamingos, with methods producing over 97 % test accuracy. Uncertainty related primarily to data coverage and collection rather than methodology. This estimate underlined the Makgadikgadi Pans’ significance for flamingos, supported by a high frequency of flooding (>5:10 years). Sua Pan ranked in the top ten breeding sites for the lesser and greater flamingos in the world. These methods can be applied to other large concentrations of flamingos and other animals. Our techniques provide considerable promise for tracking flamingo populations to ensure their protection. We provide code, for use in Google Earth Engine.

Seismogenic deformations of Cape Shartlay represent a very young fault system on the northwestern coast of Lake Baikal. Their study is providing an important opportunity to measure earthquake magnitudes, to identify areas where earthquakes are more likely to occur, and to estimate the probability of earthquake occurrence as applied to seismically active Baikal region. In this connection, the present work was aimed at characterizing in detail the structure, displacements, and reconstruction of the rupture propagation model. The study is based on photogrammetric processing and interpretation of the unmanned aerial survey data, as well as on morphostructural analysis of the displacement profiles and georadiolocation (GPR) data. It has been found that seismogenic ruptures of Cape Shartlay formed under prevailing extension conditions during no less than two earthquakes with magnitudes Mw≥7.0, Ms≥7.2. Seismic rupture propagation was primarily northward. The main rupture with displacement amplitude of more than 2 m contributed 39 to 93 % to the total surface displacement depending on the amount of dislocations on the transverse profile. It is shown that the length of a certain rupture increased almost instantaneously, then displacements along some of the ruptures stopped. A significant elongation of ruptures is primarily due to their merging. The present-day seismogenic zone is highly permeable. According to the tectonophysical model of formation of inner structure of the fault zone, the development of the seismogenic rupture system of Cape Shartlay corresponds to the late disjunctive stage. This means that the rupturing process in this segment of the North Baikal fault may not have stopped yet, and the lack of large earthquakes in the instrumental record implies the accumulation of stress in its southern part. The obtained results provide an opportunity to reconstruct the development of large fault zones by studying the displacement profiles and, therefore, to localize more precisely the places where future earthquakes may occur.

Deep learning, which has exhibited considerable potential and effectiveness in forest resource assessment, is vital for comprehending and managing forest resources and ecosystems. However, extensive assessment of forest resources is highly challenging due to the complex and varied nature of forest types sourced from diverse remote sensing platforms, which include images, point clouds, and fusion data. To facilitate further study, we systematically review the current status, applications and prospects of deep learning technologies for different types of forest remote sensing data. After considering more than two hundred forest-related papers published over the past decade, we introduce sensors and devices for forest data acquisition, classify deep learning methods based on their data processing methods and operational principles, and categorize diverse instances of these methods with various forest applications. Moreover, we summarize available datasets related primarily to forest data and examine the global geographic distribution of the relevant literature. Comprehensive insights into the advantages and limitations of each method are described, offering a forward-looking perspective on the trend of applying deep learning technology to forest research. In this paper, we aim to provide an overview of the current trends and challenges of deep learning techniques applied to forest research, creating a comprehensive picture for use as a reference by both academia and industry professionals.

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics' surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomplete 3D models with blurred geometric structures and textures. To address these challenges, an implicit–explicit coupling enhancement for a UAV large-scale scene modeling framework is proposed. Benefiting from the mutual promotion of implicit and explicit models, we initially address the issue of missing co-visibility clusters caused by environmental noise through large-scale implicit modeling with UAVs. This enhances the inter-frame photometric and geometric consistency. Subsequently, we enhance the multi-view point cloud reconstruction density via synthetic co-visibility clusters, effectively recovering missing spatial information and constructing a more complete dense point cloud. Finally, during the mesh modeling phase, high-quality 3D modeling of large-scale UAV scenes is achieved by inversely radiating and mapping additional texture details into 3D voxels. The experimental results demonstrate that our method achieves state-of-the-art modeling accuracy across various scenarios, outperforming existing commercial UAV aerial photography software (COLMAP 3.9, Context Capture 2023, PhotoScan 2023, Pix4D 4.5.6) and related algorithms. [ABSTRACT FROM AUTHOR]

With the continuous development of science and technology, UAV technology has been widely used in various fields. As an essential part, light and small UAV platforms have the advantages of being small, lightweight, and easy to operate and thus have been widely used in aerial photography, monitoring, rescue, and other fields. However, the traditional data transmission method can no longer meet the demand for the data transmission speed and stability of small and light UAV platforms. Wireless optical communication has the advantages of being high-speed and license-free, as well as having a large bandwidth strong anti-interference ability, and good confidentiality. Therefore, this paper aims to study high-speed data transmission technology for light and small UAV platforms to improve their transmission speed and stability. In this paper, the characteristics and advantages of UAV airborne laser communication are first elaborated to illustrate the advancements and importance of data transmission technology based on light and small UAV platforms. Then, the composition of a UAV airborne laser communication system is clarified to illustrate the feasibility of conducting airborne laser communication research. Then, the current domestic and international research status of people on light and small UAV airborne laser communication high-speed data transmission systems is reviewed, and its key technical features are analyzed. Finally, the prospects for its application and development trend are investigated. [ABSTRACT FROM AUTHOR]

Populations of many North American bird species have declined throughout the past half century. The majority of studies quantifying these declines have analyzed counts of birds taken during spring and summer, not during the season of scarcity, winter. We re-surveyed seven large study plots in Douglas fir (Pseudotsuga menziesii) forests initially surveyed in winter during the late 1960s in the Oregon Coast Range, USA. We compared density estimates, observed species richness and vegetation structure within plots, and inspected aerial photography to describe changes in landscape-level landcover. To assess potential drivers of change, we reviewed historical climate data and explored patterns of abundance associated with forest age. Observed species richness was greater in the modern surveys. Abundances of four of the six most common bird species appeared to be similar to or slightly lower than 50 years ago as the historical point estimates usually fell within or above 95% confidence intervals generated from our analyses. We found no clear associations with changes in bird communities and forest structure, climate, or forest age. The historical data are unique in providing point estimates of abundance for the entire bird community on each study plot. Yet, some uncertainties in accuracy of the historic counting methods could still influence interpretation of multi-decadal changes in apparent abundance. We conclude that this Pacific Northwest bird community was relatively stable in its abundances of the most common forestdwelling species and that largely the communities are still intact. Additional production of precisely repeatable surveys of winter bird communities are needed in all habitats to adequately characterize long-term population dynamics during the season of scarcity. [ABSTRACT FROM AUTHOR]

Definition: Aerial images captured with the camera optical axis deliberately inclined with respect to the vertical are defined as oblique aerial images. Throughout the evolution of aerial photography, oblique aerial images have held a prominent place since its inception. While vertical airborne images dominated in photogrammetric applications for over a century, the advancements in photogrammetry and computer vision algorithms, coupled with the growing accessibility of oblique images in the market, have propelled the rise of oblique images in recent times. Their emergence is attributed to inherent advantages they offer over vertical images. In this entry, basic definitions, geometric principles and relationships for oblique aerial images, necessary for understanding their underlying geometry, are presented. [ABSTRACT FROM AUTHOR]

Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches. [ABSTRACT FROM AUTHOR]

Toronto's Chinatown was born out of a form of resistance which paired infidelity to official definitions of Canadian citizenship (who was allowed to belong) with fidelity to its community members (who belonged). Historical representations have often been unfaithful to the Chinatown community, and architectural imagery has often tended to erase it from view entirely. In this essay, the authors explore Linda Zhang's appropriation of architectural technologies (such as photogrammetry and pointcloud scanning) as a form of antidisplacement resistance to the ongoing and centuries-old erasure(s) of Toronto's Chinatown. Her project, Chinatown 2050, uses speculative futurist 3D reconstructions and community storytelling to reimagine what Toronto's Chinatowns might be like in the year 2050. Unfaithful to the present and past "official" demarcations of the neighborhood, it is a form of social organizing and imagination towards a more generative future. In countering technological acts of erasure, Zhang's work illuminates the broader sociopolitical implications of technological choices and critiques the ways in which history often silences marginalized communities. [ABSTRACT FROM AUTHOR]

The Pohang Accelerator Laboratory (PAL) in South Korea is a leading scientific facility that attracts thousands of researchers each year. PAL operates the Pohang Light Source (PLS) and PAL-XFEL, which are synchrotron accelerators used for various scientific experiments. In 2013, the Industrial Science Support Center (ISSC) was established at PAL to provide a platform for industries to access synchrotron science. The ISSC offers expedited beamtime access and comprehensive support for industries conducting experiments. The center has supported various industries, including secondary batteries, semiconductors, and biotechnology. PAL aims to expand its industrial science support and enhance beamline efficiency in the future. [Extracted from the article]

The article explores the use of remote sensing techniques to study the Boynton Mounds Complex in Palm Beach County, Florida. The site, which contains earthworks and mounds, has been investigated since the 1970s and is believed to have a long history of habitation. The research aims to analyze the site's design and its relationship with other sites in the region. The article emphasizes the significance of remote sensing in archaeology, as it provides valuable information about ancient earthworks that may have been lost due to development or agriculture. The findings from the analysis of the Boynton Mounds Complex contribute to a better understanding of the site's layout and reveal similarities to other earthwork sites in the area. The article also highlights the importance of using new survey technologies to re-examine previously investigated sites. [Extracted from the article]

Purpose. To study the modern methodology for forming a list of mineragenic factors and prospecting signs of ore fields and deposits of copper-porphyry type based on reference objects and model constructions on the example of deposits of JungarBalkhash folded region. Methodology. The methodology for identifying the desired factors and criteria is based on the collection and analysis of published and historical data, formation of digital databases using raster and vector geological maps. It also includes the use of modern, advanced specialized technologies and software packages for geophysical surveys and remote sensing data. According to the adopted technology, the results of aerospace methods are used, which include aerial photography, space radar imaging, infrared and spectrozonal surveys and others. The research involves interpretation of WorldView-3 satellite images. Findings. A list of metallogenic characteristics describing potential copper-porphyry mineralization within the study area has been compiled. Using the Spectral Evolution PSM-3500 infrared spectrometer, characteristic areas of secondary changes in copper-porphyry systems have been identified. ASTER spectral satellite radiometer data have identified mineral spectra, iron hydroxides, propylitic associations, areas of quartzite formation and silicification. By interpreting WorldView-3 satellite images, digital files have been obtained identifying iron ore minerals (hematite, goethite), secondary quartzites (advanced argillizites), varieties of sericite and other clay minerals, silicon, carbonates and propylites (chlorite, epidote). Based on interpretation, “parquet” fracturing in granite bodies and in the area of volcanogenic and sedimentary strata development has been identified, as well as structural lines indicating the strike of rock layers. Originality. Using the example of an exploration area based on the analysis of a priori and up-to-date data, the nature of a set of relevant criteria and signs for predicting and prospecting has been determined, aimed at increasing the reliability and accuracy of formation of geological-geophysical models. Practical value. The collected and systematized geophysical data can serve as a basis for developing plans for further geological exploration, identifying promising areas, determining the required volumes and methods of geophysical surveys. They can also contribute to the increase in estimate of reserves and production at mining sites. The prepared models are intended for preliminary assessment of known and newly discovered ore deposits of various types, as well as for determining the directions of geological exploration in the areas of prospecting the Northern Pribalkhash. [ABSTRACT FROM AUTHOR]

Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction module (SDCN). Moreover, the model undergoes pruning to eliminate redundant channels. Finally, the non-maximum suppression algorithm of intersection-union ratio based on minimum point distance (MPDIOU_NMS) algorithm has been integrated to eliminate redundant detection boxes, and a comprehensive validation has been conducted using the infrared aerial dataset and the Visdrone2019 dataset. The comprehensive experimental results demonstrate that when the number of parameters and floating-point operations is reduced by 30% and 20%, respectively, there is a 1.2% increase in mean average precision at a threshold of 0.5 (mAP(0.5)) and a 4.8% increase in mAP (0.5:0.95) on the infrared dataset. Finally, the mAP on the Visdrone2019 dataset has experienced an average increase of 12.4%. The accuracy and recall rates have seen respective increases of 9.2% and 3.6%. [ABSTRACT FROM AUTHOR]

Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their applications for classification challenges in diverse agricultural areas, including plants, grains, fruits, and vegetables. By meticulously examining primary studies, we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use. Additionally, our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context. The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture. Nevertheless, we also shed light on the various issues and limitations of working with spectral images. This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm and description of its software implementation for detection of lineaments (ground erosions or cracks) in aerial images of open pits. The proposed approach is based on the apparatus of convolutional neural networks for semantic classification of binarized images of lineament objects, as well as graph theory for determining the geometric location of linearized lineament objects with subsequent calculation of their lengths and areas. As source data, three-channel RGB images of high-resolution aerial photography (10×10 cm) are used. The software module of the model is logically divided into three levels: preprocessing, detection, and post-processing. The first level implements the preprocessing of input data to form a training sample based on successive transformations of RGB images into binary images by using the OpenCV library. A neural network of the U-Net type, which includes convolutional (Encoder) and scanning (Decoder) blocks, represents the second level of the information model. At this level, automatic detection of objects is implemented. The third level of the model is responsible for calculating their areas and lengths. The result provided by the convolutional neural network is passed to it as input data. The lineament area is calculated by summing the total number of points and multiplying by the pixel size. The lineament length is calculated by linearizing the areal object into a segmented object with node pixels and, then, calculating the lengths between them while taking into account the resolution of the source image. The software module can work with fragments of the source image by combining them. The module is implemented in Python and its source code is available at https://gitlab.ict.sbras.ru/popov/lineaments/-/tree/master/lineaments-cnn. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasingly powered by proton-exchange membrane fuel cells (PEMFCs), providing longer endurance and shorter charging/fueling times. They are used for various tasks, from aerial photography to military missions. The present study explores the best scenarios for driving fixed-wing UAVs using PEMFCs, considering different weights up to 25 kg. By optimizing the UAVs' main design and performance parameters, both efficiency and endurance are aimed to be enhanced. The considered input design variables are the rate of climb, hydrogen energy density, the amount of hydrogen fuel, and cruise altitude for different aspect ratios and cruise speeds. The results show that PEMFCs-UAVs could fly continuously for a long time (> 5 h), which is impossible with other clean methods like batteries. Also, the optimization results show 3.76 times endurance enhancement compared to similar reported vehicles in the literature. The hydrogen storage system is between 1.6 and 2.9 kg for the investigated takeoff weights. For a 6.5 kg UAV with a fuel cells power density of 580 Wh/kg, the UAV can fly for 17.15 h endurance with a total power consumption of < 1 kWh and a hydrogen system of 2.9 kg. [ABSTRACT FROM AUTHOR]

Urban color, primarily emanating from building façades and roofs, plays a pivotal role in shaping a city's image and influencing people's overall impression. Understanding the nuances of color patterns contributes significantly to unraveling the uniqueness and identity of a city. This study introduces a statistical method for the systematic analysis of urban color and macroscopic urban structure. Specifically, we employ drones to collect and extract building roof and façade colors in the main urban area of Hangzhou, mapping these colors to the HSV color space. Subsequently, we establish a random walk model and an origin–destination trip model within the urban transportation network to simulate the movement of people. Our experiments reveal robust correlations between façade and roof values and passing frequency (with the Pearson correlations reaching 0.70). Through a rigorous statistical analysis, we gain insights into the distribution of urban color and the impact of architectural structures on color variations, identifying potential patterns or trends. By integrating color data with architectural structure data, our systematic research method deepens the understanding of the visual features that define cities. Beyond theoretical exploration, this approach offers practical insights for building planning and design. This study not only sheds light on the relationship between architectural structures and urban color but also provides valuable guidance for future urban development initiatives. [ABSTRACT FROM AUTHOR]

Landslides represent significant risks to human activity, leading to infrastructure damage and loss of life. This study focuses on assessing landslide hazards in Utmah Region, Yemen. The evaluation involves comparing the effectiveness of the relative frequency ratio model with five machine learning algorithms (MLAs) for hazard mapping. Field surveys, high-resolution satellite imagery, and aerial photography were utilized in the study. The inventory map was generated after identifying and mapping 100 landslides. The inventory was then divided randomly into a training dataset (70 landslides) and a validation dataset (30 landslides), with an equal number of non-landslide pixels. Eleven additional landslide conditioning factors were collected from various sources, and the frequency ratio (FR) approach was employed to identify the most crucial variables for modeling. The models were rigorously tested and assessed using statistical metrics, including the Friedman and Wilcoxon signed-rank tests, as well as the area under the receiver operating characteristics (AUROC) curve. The findings based on the training and validation datasets revealed that the RF algorithm (AUROC, 0.992) outperformed the other models in generating hazard maps. The XGBoost model (AUROC, 0.991), NB model (AUROC, 0.970), ANN model (AUROC, 0.922), KNN model (AUROC, 0.877), and FR (AUROC, 0.674) were found to be less effective. Consequently, the study highlights that the Random forest (RF) model shows promise as an effective approach for predicting landslides spatially on a global scale. [ABSTRACT FROM AUTHOR]

Crop monitoring focuses on detecting and identifying numerous crops within a limited region. A major challenge arises from the fact that the target crops are typically smaller in size compared to the image resolution, as seen in the case of rice plants. For instance, a rice plant may only span a few dozen pixels in an aerial image that comprises thousands to millions of pixels. This size discrepancy hinders the performance of standard detection methods. To overcome this challenge, our proposed solution includes a testing time grid cropping method to reduce the scale gap between rice plants and aerial images, a multi-scale prediction method for improved detection using cropped images based on varying scales, and a mean-NMS to prevent the potential exclusion of promising detected objects during the NMS stage. Furthermore, we introduce an efficient object detector, the Enhanced CSL-YOLO, to expedite the detection process. In a comparative analysis with two advanced models based on the public test set of the AI CUP 2021, our method demonstrated superior performance, achieving notable 4.6% and 2.2% increases in F1 score, showcasing impressive results. [ABSTRACT FROM AUTHOR]

Medicinal and Aromatic Plants (MAPs) play a critical role in providing ecosystem services through their provision of herbal remedies, food and natural skin care products, their integration into local economies, and maintaining pollinators' diversity and populations and ecosystem functioning. Mountainous regions, such as Chelmos-Vouraikos National Park (CVNP), represent unique reservoirs of endemic MAP diversity that require conservation prioritisation. This study aims to provide insights into the sustainable management of MAPs, contributing to efforts to protect Mediterranean biodiversity amid the dual challenges of climate and land-use change, using a suite of macroecological modelling techniques. Following a Species Distribution Modelling framework, we investigated the vulnerability of endemic and non-endemic MAPs to climate and land-use changes. We examined the potential shifts in MAP diversity, distribution, and conservation hotspots within the CVNP. Our results revealed species-specific responses, with endemic taxa facing severe range contractions and non-endemic taxa initially expanding but eventually declining, particularly under land-use change scenarios. Local biodiversity hotspots are projected to shift altitudinally, with considerable area losses in the coming decades and elevated species turnover predicted throughout the CVNP, leading to biotic homogenization. Climate and land-use changes jointly threaten MAP diversity, calling for adaptive conservation strategies, thus highlighting the importance of proactive measures, such as awareness raising, establishing plant micro-reserves, assisted translocation, and promoting sustainable harvesting to protect these species within the CVNP. Our study offers vital insights for managing biodiversity hotspots amid global change pressures, stressing the need to integrate ecological and socioeconomic factors. [ABSTRACT FROM AUTHOR]

The land-use, land-use change and forestry (LULUCF) sector is receiving increasing attention in climate change mitigation and greenhouse gas (GHG) emission offsetting. The sector itself and measures applied to mobilize this sector in order to tackle climate change are dominant in nationally determined contributions under the Paris Agreement as well as in national strategies, as in the case of Lithuania. Lithuania has set the goal of becoming a carbon-neutral country in 2050, reducing GHGs by 80% compared to 1990 and offsetting the remaining 20% through the LULUCF sector. Therefore, this paper aims at analyzing historical land-use changes in 1990–2021, as reported for the United Nations Framework Convention on Climate Change (UNFCCC) secretariat, and LULUCF's potential to achieve climate change mitigation goals, taking into account different land-use change scenarios (business as usual, forest development, forest development + additional measures and forest land 40% + additional measures) for 2030 and 2050 in Lithuania. The scenarios are based on historical and potential future policy-based land-use changes. Projections of GHG emissions/removals for different scenarios are prepared according to the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2006) by the Intergovernmental Panel on Climate Change (IPCC). The results indicate that land-use changes over the period 1990–2021 remained rather stable, with some increases in forest area and grassland at the expense of cropland. The whole LULUCF sector acted as a carbon sink in most cases, forests being a key category for removal. However, reaching climate neutrality in 2050 might be challenging, as the goal to offset 20% of remaining GHG emission compared to 1990 through LULUCF would not be met in any of the scenarios analyzed, even the scenario of maximal forest-area development and additional measures. Considering the high historical GHG-removal fluctuations and the uncertainties of the sector itself, caution should be taken when relying on LULUCF's potential to reach the set goals. [ABSTRACT FROM AUTHOR]

Studying the abandonment status of mountain terraces and its determinants, as well as exploring opportunities for the sustainable use of mountain terraces, are of great significance for the protection of regional cultivated land resources. The present study analysed eight typical villages in Jianhe County, Guizhou Province, China, as examples. A combination of high‐resolution remote sensing images and farm household survey data were used to explore the abandonment of terraces in mountainous areas. A multi‐level logistic model was constructed to analyse the determinants of terrace abandonment based on three levels: plot, household and village. The results show that serious terrace abandonment occurred in the study area, with 33.78% of all terraces having been abandoned. The plot and household levels were important in influencing terrace abandonment by farmers. At the plot level, terrace abandonment is more likely to occur on small plots found at a long distance from residences with poor irrigation conditions and collapsed ridges. At the household level, households where the head had a relatively high level of education and households with relatively small agricultural labour forces and lower degrees of mechanisation had a higher probability of their terraces being abandoned. At the village level, a relatively high risk of terrace abandonment was found in villages relatively close to an administrative centre, with a low land transfer rate and a low rate of forest coverage. In conclusion, carrying out land consolidation projects and improving agricultural infrastructure in mountainous areas are important means to achieving the sustainable use of terraces. [ABSTRACT FROM AUTHOR]

Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method's effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification (VI-ReID) aims at matching pedestrian images with the same identity between different modalities. Existing methods ignore the problems of detailed information loss and the difficulty in capturing global features during the feature extraction process. To solve these issues, we propose a Transformer-based Feature Compensation Network (TFCNet). Firstly, we design a Hierarchical Feature Aggregation (HFA) module, which recursively aggregates the hierarchical features to help the model preserve detailed information. Secondly, we design the Global Feature Compensation (GFC) module, which exploits Transformer's ability to capture long-range dependencies in sequences to extract global features. Extensive results show that the rank-1/mAP of our method on the SYSU-MM01 and RegDB datasets reaches 60.87%/58.87% and 91.02%/75.06%, respectively, which is better than most existing excellent methods. Meanwhile, to demonstrate our method's transferability, we also conduct related experiments on two aerial photography datasets. [ABSTRACT FROM AUTHOR]

With the development of UAV automatic cruising along power transmission lines, intelligent defect detection in aerial images has become increasingly important. In the process of target detection for aerial photography of transmission lines, insulator defects often pose challenges due to complex backgrounds, resulting in noisy images and issues such as slow detection speed, leakage, and the misidentification of small-sized targets. To address these challenges, this paper proposes an insulator defect detection algorithm called DFCG_YOLOv5, which focuses on improving both the accuracy and speed by enhancing the network structure and optimizing the loss function. Firstly, the input part is optimized, and a High-Speed Adaptive Median Filtering (HSMF) algorithm is introduced to preprocess the images captured by the UAV system, effectively reducing the noise interference in target detection. Secondly, the original Ghost backbone structure is further optimized, and the DFC attention mechanism is incorporated to strike a balance between the target detection accuracy and speed. Additionally, the original CIOU loss function is replaced with the Poly Loss, which addresses the issue of imbalanced positive and negative samples for small targets. By adjusting the parameters for different datasets, this modification effectively suppresses background positive samples and enhances the detection accuracy. To align with real-world engineering applications, the dataset utilized in this study consists of unmanned aircraft system machine patrol images from the Yunnan Power Supply Bureau Company. The experimental results demonstrate a 9.2% improvement in the algorithm accuracy and a 26.2% increase in the inference speed compared to YOLOv5s. These findings hold significant implications for the practical implementation of target detection in engineering scenarios. [ABSTRACT FROM AUTHOR]

Introduction: Land use classification plays a critical role in analyzing land use/cover change (LUCC). Remote sensing land use classification based on machine learning algorithm is one of the hot spots in current remote sensing technology research. The diversity of surface objects and the complexity of their distribution in mixed mining and agricultural areas have brought challenges to the classification of traditional remote sensing images, and the rich information contained in remote sensing images has not been fully utilized. Methods: A quantitative difference index was proposed quantify and select the texture features of easily confused land types, and a random forest (RF) classification method with multi-feature combination classification schemes for remote sensing images was developed, and land use information of the mine-agriculture compound area of Peixian in Xuzhou, China was extracted. Results: The quantitative difference index proved effective in reducing the dimensionality of feature parameters and resulted in a reduction of the optimal feature scheme dimension from 57 to 22. Among the four classification methods based on the optimal feature classification scheme, the RF algorithm emerged as the most efficient with a classification accuracy of 92.38% and a Kappa coefficient of 0.90, which outperformed the support vector machine (SVM), classification and regression tree (CART), and neural network (NN) algorithm. Conclusion: The findings indicate that the quantitative differential index is a novel and effective approach for discerning distinct texture features among various land types. It plays a crucial role in the selection and optimization of texture features in multispectral remote sensing imagery. Random forest (RF) classification method, leveraging a multi-feature combination, provides a fresh method support for the precise classification of intricate ground objects within the mine-agriculture compound area. [ABSTRACT FROM AUTHOR]

The Mediterranean ramblas, especially in the south-east of the Iberian Peninsula, have had a historical use consisting in the utilization of flood waters, which have been diverted to provide extraordinary irrigation to the cultivated fields. These are centuries-old systems, currently dismantled or abandoned, which had very important hydrogeomorphological consequences. However, their spatial dimension is still not well calibrated and more case studies are needed, applying a shared methodology. This article reflects on the availability and value of documentary sources of different kinds (written historical documentation, oral memory, cartography, aerial photography and field work), taking as the main case study that of the Rambla de la Sarsa-El Derramador (Agost, Alicante). The conclusions show that, in a disaggregated level, each of the sources provides relatively poor information. However, it is in the overall analysis that the most significant results are obtained. [ABSTRACT FROM AUTHOR]

The maps created of reinstated property after 1990 contain numerous errors. These errors have been transferred from the existing cadastral plans, comassation plans, land subdivision plan, aerial photo plans and a large-scale topographic maps created in the years 1964-1968. For the creation of maps of the reinstated property, the sources described above were used directly, without repeated aerial photography or deciphering of the objects as well as without the conduction of direct geodetic measurements [6, 8]. The presence of an obvious factual error is a relatively common phenomenon in the cadastral map. The same applies mainly to linear objects and it is quite possible to remove these errors. These errors can be corrected via separate projects for the specific errors, as well as via projects affecting a large part of land territory and even an entire land territory. [ABSTRACT FROM AUTHOR]

We performed a quantitative and qualitative assessment of the dynamics of gap formation in the canopy of intact old-growth polydominant broadleaved forest in a permanent sample area in the Kaluga Zaseki Nature Reserve. Digital elevation models were obtained from aerial photography data of the forest in 2018 and 2021, from which gap schemas of several height classes were compiled. The resulting schemas were expertly analyzed using orthophotomosaic images, and the gap areas were estimated. A selective ground survey of gaps was carried out, and a regression analysis of the relationship between the relative gap area and the species composition of the stand based on the primary ground survey data was conducted. It was shown that the phenophase during the survey can significantly change the estimation of gap areas, and the height of the stand in the gap cannot serve as a reliable indicator of its age. It was also found that aerial photography reveals a more complex gap structure than ground-based surveys. [ABSTRACT FROM AUTHOR]

The current global water body maps have a resolution of approximately 30 m depending on the available remote sensing data. A water body map with higher spatial resolution is required to distinguish smaller rivers for advanced applications involving global carbon cycle and real-time flood predictions. Conventional water extraction methods use water indices that combine visible and infrared spectra. State-of-the-art remote sensing data including aerial photography, offers a few-meter spatial resolution but only contains the visible spectrum. Here, we established a water extraction method at 60-cm resolution via Bayesian inference, using aerial photography's visible spectrum combining the Landsat-based Global 1-second Water Body Map and the Open Street Map (OSM). The Japan Flow Direction Map (J-FlwDir) was used to link the water bodies. Our method detected the main streams of the Tsurumi and Tama rivers and their tributaries, which were not resolved by the Landsat-based dataset. With our approach, rivers with widths >10 m were detected and water extent was obtained for 37% of small rivers, represented as lines on the OSM. These findings indicate that our method, solely based on visible spectra with hydrography data and existing water body maps, improves the spatial resolution of water mapping without requiring infrared. [ABSTRACT FROM AUTHOR]

Over the years, archaeologists have relied on maps and spatial analysis to visually understand the distribution of artifacts and features across archaeological sites. However, introducing aerial photography, digital remote sensing, geographic information systems (GIS), and global positioning systems (GPS) has provided archaeologists with sophisticated tools to analyze and depict archaeological distribution and data innovatively. Consequently, geospatial analysis has gained significant recognition as a tool to assist archaeologists. By integrating aerial photos, remote sensing, GIS, and GPS into their research, archaeologists can now analyze large quantities of data while understanding the spatial aspects simultaneously. This has enabled them to gain new insights into the distribution and patterns of archaeological features. This article examines the difficulty factors in modeling an archaeological site through geospatial approaches. Among the diverse factors that pose challenges in modeling archaeological sites are the archaeological site itself, the environment, behavior or conduct, and technical factors. With technological advancements, geospatial approaches represent the best method for archaeological investigations. At the same time, archaeologists must be cautious and weigh the benefits and challenges of geospatial analysis, ensuring that this method is used judiciously and ethically. [ABSTRACT FROM AUTHOR]

The article is devoted to the development of an intelligent technology for the construction of building plans based on the data of remote sensing of the Earth. Such data can be the data of aerial photographs or high-quality satellite images. A detailed review of the types of roofs was carried out by analyzing the most common classifications, and the most typical of them were determined, on the example of which, the improvement of the intelligent technology of building plan construction based on aerial photography data can be carried out. Modern and traditional methods of image analysis that can be used to solve this problem are characterized. The methods are chosen, which are the most advanced and can be effective for this class of tasks. Generalized algorithm for the class of single-pitched, gable, flat and hip roofs has been developed. It is proposed to improve the intelligent technology of constructing a plan of buildings based on the data of aerial photography of their roofs, by integrating the DETR detection model (“DEtection TRansformer”) together with the segmentation based on ViTs (Vision Transformers) for a comprehensive solution to the problems of finding and identifying roofs, in a first approximation, for further improving the construction of building plans. Combined approach is proposed that takes advantage of the strengths of both models by using the DETR model to localize groups of roofs in large-scale images, then using ViTs to accurately segment similar types of roofs. Comparison of the accuracy of models for image segmentation and object detection in images was made. The results of the approbation of the improved technology of construction of the plan of buildings based on the data of aerial photography of their roofs are characterized: the algorithm, approaches and software on the test data of aerial photography of the public dataset, which proved their effectiveness. Possible improvements of the proposed technology due to the use of pseudo masks are suggested. The results of the work can be extended to other types of building roofs, on the condition of proper adaptation in accordance with the characteristic features of specific types of roofs. [ABSTRACT FROM AUTHOR]

Bankfull channel extents are of fundamental importance in fluvial geomorphology, to describe the geomorphic character of a river, and to provide a boundary for further processing of morphologic and hydraulic attributes. With ever‐increasing availability of high‐resolution spatial data (e.g., lidar, aerial photography), manual delineation of channel extents is a bottleneck which limits the geomorphic insights that can be gained from that data. To address this limitation, we developed and tested two automated channel delineation methods that define bankfull according to different conceptualisations of bankfull extent: (a) a cross‐sectional method called HydXS that identifies the elevation which maximizes hydraulic depth (cross‐section area/wetted width); and (b) a neural network image segmentation model based on a pretrained model (ResNet‐18), retrained with images derived from a digital elevation model. The cross‐sectional method outperformed the neural network method overall. Its prediction accuracy varied according to channel size and type, with overall precision of 0.87 and recall of 0.80. The neural network method was strongest in larger streams, and outperformed the cross‐sectional method in channel sections with inset benches. A tool to delineate morphological bankfull conditions can allow us to more efficiently implement high‐resolution and large‐scale analyses of channel morphology (e.g., regional hydraulic geometry, channel evolution, physical complexity/habitat surveys), and improve management of fluvial geomorphology and stressors. Plain Language Summary: Identifying the extents of rivers in the landscape is a necessary first step to better understand and manage them. Historically, delineating these extents has been an intensive manual task. We developed and tested two methods of automatically identifying river extents from high resolution elevation data. The first method used cross‐section data, and identified the elevation in each cross‐section where flow first starts to spread across the floodplain. The second method worked directly on gridded elevation data, identifying in‐river and out‐of‐river pixels using a neural network (a type of artificial intelligence model) trained on other image segmentation problems then retrained on our river elevation data. Both methods showed promise. The cross‐sectional method was most accurate overall, performing well on channels 7.5–26 m in width. The neural network method was more accurate for larger than smaller stream channels. It outperformed the cross‐sectional method in channels which had inset low flow channels within a larger high‐flow channel. The methods developed will allow us to map channel extents in a standardized and efficient way, opening the door to much larger‐scale analyses of river dimensions and processes. Key Points: Two automated bankfull identification methods based on lidar‐derived data were shown to perform well on alluvial stream channelsThe HydXS objective‐function cross‐sectional method performed best overall, with precision of 0.87 and recall of 0.80The neural network image segmentation method performed better in larger channels and outperformed HydXS in channels with inset benches [ABSTRACT FROM AUTHOR]

This research was carried out to investigate the application of unmanned aerial photography in estimating the respective tonnages of extracted rocks at Beautiful Rock Quarry, Lokoja, Nigeria. To attain the aim of the research, the aerial views of the 2D images captured were used to generate aerial views in the form of 3D models, namely, Digital Terrain Models (DTMs) and Digital Surface Models (DSMs). ImageJ and Regard3D components of Image Processing Software, were used for the processing of the images. Owing to the tilted aerial photograph, correction checks were conducted for the sensor coordinates, and the ground coordinates were established. Images of the extracted pits and stockpiles were analyzed by applying image processing software, which identified elevation spots and generated Z-plots for these feature. With the results generated from the image processing software, the extracted rocks from the pit were calculated to weigh 1,027,204.512tons, while the total production of aggregates sold from the quarry between 2012 and 2020 was documented to weigh 1,715,800tons. The difference between the tonnage of extracted rocks from the pit and the total production of aggregates sold was due to the swell factor of the rock (1.70), the fly rock issuing from the blasting operations and the loss of aggregates during the loading and haulage phases of the mining operation. The stockpile calculated from the 3D images weighed 44,695.39tons. The expected tonnage of the extracted rocks from the pit after excavation and crushing into aggregates was expected to be 1,746,247.670tons, which is about two percent higher than the total aggregate weighing 30,447.67 tons produced from the quarry. Therefore, approximately two percent should be allowed for the extracted tonnage expected from the pit after excavation and crushing into aggregates, as well as for the total aggregate produced from the quarry. [ABSTRACT FROM AUTHOR]