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Major storms can cause significant changes to coastal and wetland environments. A series of storm events in 2020 resulted in closure of the historically open estuary at Cabbage Tree Basin, Port Hacking, New South Wales (NSW), Australia. Prolonged tidal impoundment (3 months) led to substantial changes in hydrological and sedimentological processes, resulting in widespread mangrove dieback. This study aimed to quantify the degree of impact and recovery for mangroves, identify factors contributing to dieback, and consider the implications for carbon sequestration. This was achieved using remotely piloted aircraft structure-from-motion approaches, aerial photography, and field-based assessments of vegetation health and above-ground biomass (AGB). Mangroves were classified as 'dead', 'partially dead', and 'live'. In October 2019, there was 10.8 ha of live mangroves, with this reduced to 6.6 ha by August 2022. Digital surface models (DSMs) were intersected with classified mangroves to assess the vertical distribution of each zone. All mangroves classified as 'dead' were distributed at elevations < 0.4 m with respect to the Australian Height Datum (AHD), suggesting these regions were persistently inundated, which was confirmed by water level loggers (inundated during logger deployment). Field data confirmed substrate elevation related to dieback with the proportion of 'live' mangroves greatest at elevations > 0.6 m AHD. Substrate elevation and distance to the estuary mouth were significantly correlated with species, with Avicennia marina located at lower tidal positions and closer to the entrance compared to Aegiceras corniculatum. The dieback event equated to a loss of 81.5 ± 48 Mg of above-ground biomass, 38.1 ± 22.5 Mg C, or 140 ± 82 Mg CO2 equivalence (CO2e). This study provides an important baseline for monitoring dieback events. Continued monitoring is crucial to assess recovery and to tailor management strategies. [ABSTRACT FROM AUTHOR]

Mountain search and rescue (MSAR) seeks to assist people in extreme remote environments. This method of emergency response often relies on crewed aircraft to perform aerial visual search. Many MSAR teams use low‐cost, consumer‐grade unmanned aerial vehicles (UAVs) to augment the crewed aircraft operations. These UAVs are primarily developed for aerial photography and lack many features critical (e.g., probability‐prioritized coverage path planning) to support MSAR operations. As a result, UAVs are underutilized in MSAR. A case study of a recent mountain search and recovery scenario that did not use, but may have benefited from, UAVs is provided. An overview of the mission is augmented with a subject matter expert‐informed analysis of how the mission may have benefited from current UAV technology. Lastly, mission relevant requirements are presented along with a discussion of how future UAV development can seek to bridge the gap between state‐of‐the‐art robotics and MSAR. [ABSTRACT FROM AUTHOR]

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers. [ABSTRACT FROM AUTHOR]

During projects to build roads in China's mountainous areas, which are often characterized by the poor stability of rocky slopes, cases of deformation damage occur frequently. Because of the wide distribution of rocky landslides in the smooth layer, the unavoidable perturbation caused by engineering construction and the seriousness of landslide disasters, the destruction of this type of landslide and the research into prevention of the mechanism cannot be ignored. On 6 February 2023, a rock slide occurred in Yaoping village, Hubei Province, China. Using field investigations, Google satellite images, unmanned aerial vehicle photography, indoor testing and numerical simulations, we analysed the geological situation, deformation characteristics, damage process and triggering mechanism. The results show that the Yaoping landslide is a typical downward plane-sliding rock slide induced by the excavation of a highway. When strain softening occurs, the shear strength and friction coefficient of the slip zone soil are significantly reduced. Excavation caused the factor of safety of the slope to decrease substantially, with a maximum decrease rate of 60.7%. Excavation is considered to be the extrinsic factor that triggered the occurrence of the Yaoping landslide, while the reduction of landslide resistance caused by strain softening of the soil in the slip zone is the intrinsic direct factor that led to the occurrence of this type of landslide. The Yaoping landslide has obvious traction landslide characteristics. The landslide from deformation to destruction has four stages: creep at the leading edge of the landslide; traction deformation in the middle and back of the landslide; slip belt softening; and integral sliding. The prevention and control of this type of landslide requires a larger scope and depth of investigation, in order to identify in advance the possible soft surface in the rock layer, to speculate on the possible formation of landslide destruction mode, and to determine the support scheme and scope according to the geological conditions of the slopes on both sides of the highway. The slip zone soil of the Yaoping landslide was used to obtain the strength index of slip zone soil, which provides data support for the overall deformation characterization of the landslide. The results can provide a reference for the risk management and prevention of rock slides caused by human engineering activities during the excavation of roads. [ABSTRACT FROM AUTHOR]

Structure from motion (SfM) photogrammetry coupled with multiview stereo (MVS) techniques are widely used for generating topographic data for monitoring change in surface elevation. However, study sites on remote glaciers and ice caps often offer suboptimal conditions, including large survey areas, complex topography, changing weather and light conditions, poor contrast over ice and snow, and reduced satellite positioning performance. Here, we provide a review of methodological considerations for conducting aerial photography surveys under challenging field conditions. We generate topographic reconstructions, outlining the entire workflow, from data acquisition to SfM-MVS processing, using case studies focused around two small glaciers in Arctic Canada. We provide recommendations for the selection of photographic and positioning hardware and guidelines for flexible survey design using direct measurements of camera positions, thereby removing the need for ground control points. The focus is on maximising hardware performance despite inherent limitations, with the aim of optimising the quality and quantity of the source data, including image information and control measurements, despite suboptimal conditions. [ABSTRACT FROM AUTHOR]

Chemically distinctive features such as high salinity, low nutrient contents, and extremes of pH can limit the development of a vegetation cover on soil-free bare ground. This study synthesises our previous work on the geoecology of numerous chemically distinctive bare sites in the South Island and adds a temporal component within the last century. The rates of development of surficial cover on decadal time scales have been traced from available historical aerial photography of varying but adequate quality. Rates are highly variable but range between zero and 2 metres per year laterally, with some sites showing localised expansion of bare ground at times. Cover evolution involves the development of a cm-scale proto-soil layer that is at least partially biologically mediated. Geochemical thresholds in surficial substrates control the biological components of encroaching cover. Dynamic surface environments, especially steep and actively eroding slopes, help to maintain bare ground and resist cover development. Some chemically distinctive bare ground, and evolving adjacent proto-soil areas, host specialist tolerant native plant communities and shrinkage of these areas threaten this biodiversity. In contrast, some bare ground sites have elevated arsenic and/or highly acidic pH, and the development of proto-soil and vegetation cover is environmentally positive from a human perspective. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems. [ABSTRACT FROM AUTHOR]

An improved YOLOv7 aerial image object detection algorithm is proposed to solve the problems of low detection accuracy caused by mesoscale changes, small targets and dense occlusion in UAV aerial images. Firstly, a weighted sampling module with joint dynamic convolution is designed to capture features from multiple dimensions and improve the feature extraction ability of the model. Secondly, add a shallow feature detection head to retain more detailed information and enhance the ability to utilize small target features. Then, a multi- scale feature aggregation module (C2-Res2Block) with residual structure is constructed in the feature fusion part to make the model fuse rich multi-scale information. Finally, the MPDIoU measure is used to replace the traditional IOU to calculate the boundary regression loss and improve the localization ability of the model to the densely occluding target. Experiments on UAV aerial photography data set VisDrone2019 show that the improved algorithm is 4.3 percentage points higher than the original model on mAP@0.5, 2.4 percentage points on mAP@0.5: 0.95, the number of parameters is reduced by 6.81x106, and the detection accuracy is higher than the current mainstream object detection algorithms. It effectively improves the detection accuracy of UAV aerial images, and obviously improves the false detection and missing detection of aerial objects. [ABSTRACT FROM AUTHOR]

Landslides are the main gravitational phenomena in the Neogene basins of Algeria. This feature is mainly favored by the lithological nature of the fill materials, the climate, and the slope morphology. They are known for their high residential concentration and urban development. For better management and planning of future projects in landslide-prone areas, the use of Landslide Susceptibility Maps (LSMs) is indispensable. This study proposes a combined statistical and limit equilibrium method for the analysis of slope stability and for the production of LSMs using GIS in the Aomar Neogene basin (Bouira, Algeria). For this purpose, different work steps were carried out. Initially, a landslide inventory map was prepared based on existing studies, high-resolution aerial photography and satellite imagery analysis, as well as on detailed fieldwork. Besides, the geotechnical characterization of each geological formation was determined from the physical and mechanical parameters of recent and old geotechnical studies conducted in the region. A total, 169 landslides were compiled and randomly divided into training (70%) and validation (30%) datasets. Furthermore, eight factors influencing landslide occurrence (including slope angle, lithology, slope aspect, elevation, drainage density, distance to the rivers, distance to the roads, and rainfall) were selected and applied for the analysis. Moreover, the inventoried landslide and their associated factors were taken into account when mapping landslide susceptibility in the Aomar Neogene basin using Frequency Ratio (FR) models in GIS. On the other hand, a two-dimensional limit equilibrium (LE) model was used to determine the landslide-prone zones with a distinctive stability factor for each slope and the failure surface depth. Then, we have been able to verify and validate the obtained results for the statistical method using the Area Under the Curve (AUC) value of the Receiver Operating Characteristics Curves (ROC) method. For the frequency ratio models, the AUC is 83.23%, and for the limit equilibrium method, the safety factor is less than one for a slope greater than 16°. Based on these findings, we draw the conclusion that the maps may be exploited as practical instruments for land use planning and risk reduction in the Aomar Neogene basin. [ABSTRACT FROM AUTHOR]

Landslide inventories are crucial for evaluating susceptibility, hazards, and risks, and for devising resilience strategies in mountainous regions. This importance is amplified in the context of climate change, as existing inventories might not adequately reflect changing stability conditions. In May 2023, the Emilia-Romagna region of Italy was hit by two major rainfall events, leading to widespread flooding and the triggering of thousands of landslides. Predominantly, these were shallow debris slides and debris flows, occurring on slopes previously deemed stable based on historical data with no prior landslides recorded. Our team supported the Civil Protection Agency through field surveys and mapping efforts to pinpoint and record these landslides, prioritizing areas critical to immediate public safety and focusing on thorough mapping for future recovery planning. The outcome is a detailed map of all landslides induced by these events, manually identified using high-resolution aerial photography (0.2 m pixel resolution, RGB+NIR four bands) and categorized with the help of a 3D viewer. This comprehensive landslide inventory, comprising 80997 polygons, has been made openly accessible to the scientific community. [ABSTRACT FROM AUTHOR]

[Objective] Severe gully erosion in the black soil region of Northeast China is continuously eroding arable land resources, posing a threat to the country's food security. The development of unmanned aerial vehicle (UA V) technology provides an effective way for monitoring gully erosion, but the higher the resolution of data obtained, the longer it takes. The challenge lies in configuring UA V parameters to simultaneously meet accuracy requirements and enhance aerial photography efficiency. _Methods］ The Hebei catchment in the rolling hills region of Northeast China was chosen as the study area. Using actual cross-section measurement data as a validation value, the accuracy of gully parameters extracted from UA V data at different resolutions, flight directions, and types was assessed to explore the suitable conditions for various UAV parameter configurations. Results] (1) Compared with field measurement data, data at a 1 cm resolution extracted the gully parameters with the highest accuracy, the average percentage error for all parameters was less than 5.0%, suitable for monitoring typical gully development processes. Data extracted at 3 cm and 5 cm resolutions had an average percentage error of less than 1().()% for gully width, and the average error increased as gully depth decreased. For gullies deeper than 1 m, the average percentage error was less than 1().()%，suitable for rapid regional sampling surveys. Data extracted at resolutions of 8 cm and 10 cm had average percentage errors greater than 4().()% for gully depth and cross-sectional area, suitable for extracting gully distribution locations and planar parameters. (2) Although fixed-wing UAV obtaining data slightly outperformed in extracting two-dimensional gully features, multirotor UAV using oblique photogrammetry were better in extracting three-dimensional gully features. (3) The average percentage errors for gully depth and cross-sectional area extracted from single-direction oblique photogrammetry data were 1.7 and 1.9 times those from cross-direction data. UAV data obtained from cross-direction flights provided higher accuracy in gully parameter extraction and richer details. _ Conclusion H Setting UA V parameters to a resolution of less than 5 cm and obtaining cross-direction oblique aerial photography data can meet the accuracy requirements for monitoring gully morphology in the black soil region of Northeast China. [ABSTRACT FROM AUTHOR]

We present new data of White Sea Coast dynamics within NE of the Gorlo Strait that were generated by satellite images, geomorphological and ground penetrating radar (GPR) profiling, aerial photography, and topographic surveys of coastal terraces and dunes. Our paleogeomorphological studies, supported by laboratory findings (diatom analysis and 14C dating), allowed us to reconstruct the morphodynamics of coastal and aeolian landforms. The obtained reconstruction enabled the evaluation of relative sea level (RSL) changes and the evolution of coastal landforms over the past ~ 3.7 cal ka BP. According to our research, sand was supplied to coastal zone and coastal dunes through scarp erosion (0.5–3.7 m/year) as well as from the shoreface, while the role of alluvial runoff is insignificant. The largest dune massifs were formed in the areas of longshore drift (from NE to SW) discharge. At the mouth of the Maida River, the barrier spit and foredune plain have been evolving from the end of the Holocene transgression (∼3.7–2.3 cal ka BP). The mean high water at this time was slightly higher (up to ∼2.5 m a.s.l.), and coastal processes were significantly more intense than current ones. Then, against a decrease in sea level and weakening sediment flows, the growth of the spit slowed. The ancient foredunes were stabilized by vegetation. Aeolian processes were activated ∼2.1 and after ∼0.8–0.7 cal ka BP. [ABSTRACT FROM AUTHOR]

In the early hours of June 24, 2017, a major landslide event occurred in Xinmo Village, Sichuan Province, China. The landslide instantly devastated the whole village. Ten people died and 73 were missing in this major landslide event. The study area has suffered from several strong earthquakes in the past 100 y. Present studies have reported that the cumulative damage effect of the Xinmo landslide induced by earthquake is obvious. In this study, we conducted a shaking table test based on the detailed geological survey, historical seismic data, satellite optical image, unmanned aerial vehicle photography. The test result presents the characteristics of multistage seismic damage and progressive deformation process of the Xinmo landslide model, and shows that the historical earthquakes have caused serious damage to the interior of rock mass in the source area. The test also shows that the cumulative damage of the model increases with an increase in duration of earthquake loading. When the excitation intensity increases to a certain value, the damage accumulation velocity of the model suddenly increases. It reveals that frequent historical earthquake loads can be regarded as a main reason for the damage and deterioration of landslide rock mass. Damage accumulation and superposition occur in the slope. Under a long-term gravity, deformation of the slope gradually increases until catastrophic failure is triggered. The progressive deformation process of slope is summarized. Firstly, under strong earthquakes loading, a tensile fracture surface forms at the rear edge of the wavy deformation high and steep bedding slope. It reaches a certain critical depth and expands along the interlayer structural plane. Meantime, damaged fissures perpendicular to the structural plane also appear in the steep-gentle turning area of the slope. Secondly, under a coupling action of seismic loading and gravity, the interlaminar tensile crack surface at the rear edge of the slope extends to depth continuously. Meanwhile, rock fracture occurs in the steep-gentle turning area. The "two-way damage propagation" mode of the inter-layer tensile crack surface occurs until the sliding surface is connected. However, due to the "locking section" effect of rock mass at the slope foot, it can still maintain a short-term stability. Thirdly, under the influences of the heavy rainfall before a landslide and the long-term gravity of the upper sliding mass, rock mass in the steep section at the slope foot breaks outward. Finally, a catastrophic landslide occurs. [ABSTRACT FROM AUTHOR]

Coastal erosion and sea level rise has become a significant threat to coastal heritage due to climate change. Management bodies are monitoring the risks posed by undertaking studies, ranging from Rapid Coastal Zone Assessments to much larger government and research council funded studies. These studies have incorporated varying techniques and datasets such as environmental sampling, LiDAR, GPS, and UAV surveys. However, these approaches rarely combine study of historic shoreline change with physical susceptibility modeling. This paper presents a combined methodology, utilizing LiDAR and survey data collected by the Climate, Heritage, Environments of Reefs, Islands and Headlands (CHERISH) project, along with historic mapping and aerial photography to produce a risk assessment for the heritage assets located on Ynys Enlli (Bardsey Island), North Wales. By constructing an Historic Erosion Model and an Erosion Susceptibility Model using ESRI's ArcGIS 10.5.1. with the DSAS Plugin and combining them with the LiDAR-derived spatial extents of archaeological features on the island, this project has shown that using both methodologies alongside each other can create a more complete overview of coastal environment change for the creation of vulnerability indexes. [ABSTRACT FROM AUTHOR]

The increasing use of drone technology to produce high-resolution digital imagery and elevation models has been associated with a growing interest in developing quantitative morphometric analysis (QMA). QMA analysis is an invaluable part of creating detailed topographic models in landslide scars that are still highly unstable and prone to erosion. This paper presents the results of a research that aims to create a topographic model in a landslide scarred area where the slope configuration is still varied. The study area was located in the landscape of the Cretaceous-Tertiary volcanic transition where many landslides have occurred. Three landslides were selected on the basis of different soil material characteristics that affect the topographic condition of the landslide scar. Aerial photography was recorded using a UAV with a flying height of 80 m, with an orthomosaic resolution of 1 cm. In detail, three morphometric variables (slope, plan curvature, topographic position index) were selected and calculated with the output evaluated based on visual-spatial interpretation. The results showed that morphometric variables performed well in modeling land surface topography. Steep slopes and surfaces with convex curvature are abundant at the ledges and landslide heads that allow water runoff to disperse as the initiation of gully erosion. The multidimensional gully erosion network is concentrated at relatively low elevations and surfaces with concave curvature. The undulating micro-relief of the land surface as a result of the process of material disposition builds up on each other to a gentle slope. Finally, the topographic model of the landslide surface can be used as a base material in implementation of both physical and vegetative land conservation strategies. [ABSTRACT FROM AUTHOR]

[Objective] With the continuous advancement of digital infrastructure construction and the flourishing development of digital cities, unmanned aerial vehicle (UAV) photogrammetric modeling technology is rapidly emerging. However, because of the differences in the number of photos taken and the shooting angles, the model often exhibits certain phenomena, such as holes and distortions, leading to a decrease in model accuracy. Moreover, excessive image overlap can lead to redundant image information, which results in modeling algorithms being unable to accurately identify and match feature points, thereby reducing modeling accuracy and reliability and negatively affecting modeling effectiveness. In addition, the high overlap rate leads to data redundancy, increases data processing time and cost, and reduces modeling efficiency. How to improve model accuracy while balancing modeling efficiency has received widespread attention. [Methods] This study regarded the Honghui Cultural and Sports Complex Building of Fuzhou University as the experimental object and selected three flight parameters, namely image overlap ratio, margin setting, and flight speed. By recording relevant data, such as the duration of UAV field photography flight, aerial triangulation time, duration of 3D modeling, and model size, the relationship between UAV flight parameter setting, model accuracy, and modeling efficiency was investigated to determine the optimal flight parameter setting for modeling efficiency. The experiment used the method of controlling variables to reduce the impact of secondary factors. To avoid the impact of light and shadow changes on modeling, the field data collection time was scheduled from 10 AM to 3 PM on cloudy days. In terms of duration calculation, to avoid human interference, only objective automatic operation time was calculated, that is, the flight duration of the UAV (from takeoff to shooting to landing), the aerial triangulation time, and the duration of 3D modeling were calculated, and all three duration statistical data were displayed in the software. All experimental data needed to be tested twice and averaged to ensure the accuracy of the results. If there was a significant difference between the two test data, then the experiment was repeated and the most similar data were selected from the three similar groups to calculate the average value to avoid the impact of errors on the experimental data. Six sets of experiments were conducted in this research, with the first group searching for the relationship between image overlap ratio and modeling efficiency on a large scale and the second and third groups searching for the relationship between flight speed, margin setting, and modeling efficiency based on the first group. The fourth, fifth, and sixth groups optimized the image overlap ratio span considering speed and margin conditions, gradually narrowed the experimental range, and compared the data to determine the optimal modeling efficiency. [Results] Results showed that UAV has the optimal modeling efficiency when the sideward overlap rate is 53%, the heading overlap rate is 53%, the flight speed is 9.3 m\s, and the margin setting is automatic. The image overlap ratio complies with the relevant regulations in the technical specifications for oblique digital aerial photography (GB/T 39610-2020) and the specifications for inspection and acceptance of the quality of three-dimensional model data products on geographical information (CH/T 9024-2014). [Conclusions] As an important technical support for digital infrastructure construction, UAV photogrammetric modeling provides new avenues for urban planning, environmental monitoring, power inspection, disaster assessment, and other fields. The experimental results of this research achieve the expected goal, enhance the efficiency of UAV photogrammetric modeling, and improve model accuracy. [ABSTRACT FROM AUTHOR]

For monitoring hard-to-reach thermal facilities, remote surveillance techniques from unmanned aerial vehicles (UAVs) equipped with a thermal infrared camera represent a progressive platform for rapid information collection. This paper presents the first results of such an application of UAVs to study five thermal objects on the Kuril Islands. Four of them (Kipyashchaya Riwer on Iturup Island, Lake Biryuzovoye on Simushir Island, Yankich Island and Yurieva River on Paramushir Island) are associated with active volcanoes. Heat maps with precise geographical reference have been obtained for all sections of gas hydrothermal discharge. For Kipyashchaya River and Yankich Island verification of air thermometry by ground data was performed and real atmospheric attenuation coefficients were obtained for different flight altitudes under observed meteorological conditions. The first data on the parameters of the thermal plume from the hot Yurievа River flowing into the Sea of Okhotsk were obtained.

The characteristics of fan sediment in the distributive fluvial system are evaluated based on a thorough analysis of modern silt in the Guertu river distributive fluvial system, as well as data obtained from UAV aerial photography and satellite remote sensing. The Guertu River Distributive Fluvial System (DFS) is classified into three stages, namely "proximal," "middle," and "distal," based on the examination of river morphology, sediment variations, sedimentary attributes, and other relevant aspects throughout different sections of its tributaries, from the source to the mouth. At the upstream section of the downstream fining sequence, the slope is the steepest and the hydrodynamic conditions are intense, resulting in the formation of a predominantly big gravel braided river. The river bed section has a morphology resembling a combination of a "V" and a "U" shape. It is characterized by a narrow and deep configuration, with a relatively short breadth. The sediment primarily consists of medium to large-sized gravel with minimal sand content. The gravel exhibits good roundness and displays a considerable degree of orientation. The primary microfacies present are braided channel and flood plain. The slope of the central area is decreased in comparison to the nearby end, primarily due to the presence of extensive braided rivers. The river bed has a greater width, with minor eolian dunes visible in the river channel. The gravel particles are predominantly fine to medium in size, and there is an increased amount of sand present. The predominant microfacies are braided channels, floodplains, and eolian dunes. At the distal end, the slope is minimal, the landscape is level, the braided river transitions into a meandering river, the sediment consists primarily of sand, and the signs of bioturbation are clearly visible. The primary microfacies consist of braided channels, meandering channels, floodplains, eolian dunes, lakes, and swamps. [ABSTRACT FROM AUTHOR]

Efficient and accurate extraction of discontinuity geometric parameters is significant for evaluating the stability and seepage characteristics of a rock mass. Traditional means of rock discontinuity measurement are susceptible to the influence of terrain and have issues such as high operational workload and low efficiency. An approach was proposed to comprehensively and efficiently obtain the geometric parameter information of the discontinuity, using an Unmanned Aerial Vehicle (UAV), which consists of the following five procedures: (1) measurement of a rock slope with UAV, (2) creation of three‐dimensional (3D) point cloud model for the slope, (3) voxel filtering of point cloud data, (4) planar segmentation based on modified region growing (MRG) algorithm, and (5) acquisition of parameter set of the discontinuity (orientation, trace length, and spacing). A new code, FacetDetect, has been developed based on this method to identify discontinuity from a 3D point cloud. Meanwhile, the trace length and spacing are calculated by the code written in Python. A comparison with the three‐point method reveals that most errors are less than 3° for dip angles and dip directions. These deviations are reasonable and confirm the reliability of the method. Overall, this method is a valuable reference for automatic discontinuity interpretation and related fields. [ABSTRACT FROM AUTHOR]

The development of precise analytical methodologies, together with the systematic use of verifiable data, aims to raise standards for treatment, risk management, and maintenance programs. This endeavor aims to diminish the frequency of subsequent restoration cycles, thereby positively impacting public spending and fostering the preservation of the authentic characteristics of historical buildings, particularly by mitigating the effects of decay. The Damage Map through Heritage Building Information Modeling (HBIM) project, funded by the National Council for Scientific and Technological Development (CNPq) and Minas Gerais Research Support Foundation (FAPEMIG), both Brazil, aims to establish protocols for risk management in historical buildings, with a focus on decorative stone elements integrated into the construction. For this reason, the stone relief on the façade of the church of St Francis of Assisi in Ouro Preto, Brazil, was chosen as a case study. This paper outlines the data collection methodology applied to study the stone. This approach encompasses historical examinations of restoration cycles, along with preliminary findings derived from extensive fieldwork, conducted by a multidisciplinary team in 2023. It elucidates the data collection stage, employing photography and aerial photogrammetry, ultraviolet fluorescence photography, mobile laser scanning, and thermography. Subsequently, all collected data will be used to construct a diagnostic model within the HBIM. [ABSTRACT FROM AUTHOR]

Matching the terrestrial laser scan point clouds with the unmanned aerial oblique photography point clouds of high-rise irregular buildings can make them complementary. The challenge lies in the different sources and qualities, besides arbitrary angular deviations of the two types of point clouds. We propose a registration method based on point and line features, which automatically recognises the features of these point clouds using normal vectors and 3D-Harris, and solves the registration parameters through rigorous adjustment. The registration model, process and method are introduced. Finally, through the analysis of real case examples, it was found that the registration result of this method was better than the traditional Iterative Closest Point (ICP) in the analysed cases, the proposed registration method not only exhibits good automation but also has the ability to ignore various differences in heterogeneous point clouds, thereby improving the accuracy and efficiency of registration for such point clouds. [ABSTRACT FROM AUTHOR]

Considering the disadvantages of traditional railway operation and maintenance manual inspection, including low efficiency, high cost, and detection blind spots, to solve the problems faced by drones in data acquisition, data processing and application, this paper innovatively proposed a route planning algorithm with a safe buffer zone. The algorithm adopted a partition operation strategy, and used two methods of orthography and side-view shooting to effectively improve the operation efficiency and the security of data acquisition. In addition, based on the data of UAV inspection aerial photography project, this paper formed a typical disease sample library in railway operation and maintenance environment through relevant processing, and used YOLOv7 target detection algorithm to realize the rapid automatic detection of three typical diseases of color steel tile, dustproof net and plastic film. Combined with human-computer interaction, the intelligent extraction and rapid filing of external environmental hazards were realized, and a set of intelligent monitoring technology scheme of railway operation and maintenance environment based on UAV platform was formed. The results show that, the effective combination of UAV technology, target detection technology and computer technology can realize rapid automatic hidden danger identification, which can improve the efficiency of railway operation and maintenance inspection by 150%, and provide important technical support for railway safety management. [ABSTRACT FROM AUTHOR]

Cross measurement is an important foundational work for special surveying and survey of newly built railways. The existing cross measurement is mainly completed through manual field measurement methods such as angle shifting, polar coordinate method, and GNSS-RTK method, which has problems such as low operational efficiency, multiple rework measurements, and high safety hazards. Aiming at this technical problem, this paper proposed a new railway crossing survey method based on UAV laser point cloud and image fusion, including key technologies such as data acquisition and pre-processing of railway band UAV aerial photography, sorting of design line positions and determination of cross measurement positions, and extraction of cross measurement information. The purpose is to complete the cross measurement work of the newly built railway through non-contact means. The results show that, compared to traditional operation methods, this method avoids difficulties in entering complex areas, significant safety hazards, and inconvenient installation of conventional measuring instruments. The efficiency can be improved significantly in terms of the flexibility and safety of measurement field operations. Based on the fusion processing technology of drone laser point cloud and image, the characteristics of high density, high accuracy, rich image texture, and high visualization degree of drone laser point cloud are fully utilized, laying a good foundation for determining the cross measurement position and extracting information. The method for calculating cross measurement parameters can improve the accuracy and reliability of cross measurement, and can also provide reference for the research of other types of cross, cross measurement, or other technical means. [ABSTRACT FROM AUTHOR]

Mining is an environmentally destructive human activity. Consequently, community expectations and legislation require minimisation of impacts and rehabilitation once mining ceases. Rehabilitation standards now include restoration of structural and functional attributes of pre-disturbed landscapes. However, insufficient baseline data before, and during, mining often makes it difficult to assess impacts and develop rehabilitation objectives. Techniques that retrospectively document the pre-impact condition and environmental history of wetlands affected by mines can provide this information. We demonstrate how this can be achieved using data from palaeoecology and remote sensing, to understand mine impact on Fishermans Wetland, North Stradbroke Island (Minjerribah), by inferring its environmental history from formation to present. Fishermans Wetland is a small, clear, open water, wetland with extensive macrophyte growth. A lack of information about the wetland's pre-mine condition created uncertainty about the effects of upstream sand mining. Contrary to local community concerns that Fishermans Wetland was ancient and hydrologically modified by mining, it only formed in the 1950s. Moreover, changes to site hydrology predated mining. Consequently, ongoing supplementation of water is unnecessary for maintaining the wetland's ecological character. Similar techniques could be used elsewhere where mine impacts are poorly understood or contested. [ABSTRACT FROM AUTHOR]

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The pavement texture wavelength and mean texture depth (MTD) of the pavement macrotexture significantly affect functional performance. However, owing to its higher correlation with the wavelength, the Exposed Aggregate Concrete Pavement (EACP) texture was evaluated based on the MTD and exposed aggregate number (EAN) in the same location. The MTD contributes significantly to road surface friction and tire-pavement noise and is vital for anti-sliding and noise reduction. Conventional methods for MTD measurement require considerable human effort when the sample size is large and sensitive to the operator. Furthermore, it is time-consuming to measure the MTD together with the EAN. Recently, image-based estimation has become a new trend, owing to its economy and convenience. Therefore, this study aimed to estimate the MTD of the EACP pavement surface texture at an image-based level as an alternative measurement approach simultaneously within the EAN location. Initially, the image acquisition was created based on aerial photography. Subsequently, photogrammetry was used to reconstruct a high-resolution point cloud of the pavement texture. Subsequently, the MTD was estimated analytically from image-based point clouds. Experiments were conducted at over 60 locations in three field tests of the EACP in South Korea. The MTD results showed good agreement and a higher correlation between the image-based and sand-patch test (SPT) methods. The image-based method showed results higher than the SPT with a value of 22%. Therefore, the developed method can be used to estimate the MTD using the established regression equation. [ABSTRACT FROM AUTHOR]

Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale. [ABSTRACT FROM AUTHOR]

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor intensive and time consuming. To address this limitation, remote sensing technology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Shoal Creek and Talladega Ranger Districts of the Talladega National Forest by employing ALS-derived data and aerial photography. The quality of the predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species group models. This study confirms that quality level 2 LiDAR data were sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82), when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species group predictive models were of higher quality than general predictive models. Lastly, landscape level maps were created from the predictive models and these may be helpful to planners, forest managers, and landowners in their management efforts. [ABSTRACT FROM AUTHOR]

The rapid expansion of informal settlements poses significant challenges to urban ecosystems and their services, a pressing issue that this study addresses by examining its impacts in Addis Ababa. This study delves into the impact of informal settlements on ecosystem services in Addis Ababa, comparing them with formal settlements. Utilizing geographic information systems (GIS) and remote sensing, it employed Landsat imagery, aerial photography, and base maps to track spatiotemporal changes and analyze trends. Through a GIS overlay approach, informal settlements and associated land use changes were identified, while the benefit transfer method assessed ecosystem service values. The findings revealed a significant increase in informal settlements on the city's outskirts, expanding from 77 ha in 2009 (2% of the total area) to 765.6 ha in 2023 (21% of the total area). Nearly half (48.5%) of these informal settlements were established on previously natural land covers through encroachment. The research highlighted the ongoing conversion of agricultural land, forests, and urban green spaces into informal settlements, particularly on the city's outskirts, leading to significant encroachment on these areas. This transformation caused an annual loss of $1,665,033.7 in ecosystem services from 2009 to 2023. Significant impacts were identified on food production, climate regulation, and habitat ecosystem services. Overall, the annual loss in ecosystem services due to land use changes amounted to $1,933,320.08. Informal settlements accounted for 86.1% of this total loss, highlighting the urgent need for targeted interventions to mitigate their disproportionate effects on ecosystem services. The study underscores the urgency of addressing the impact of informal settlements on ecosystem services through effective urban planning, sustainable land management practices, prevention of encroachment on natural land covers, and promotion of sustainable ecosystem management and utilization. [ABSTRACT FROM AUTHOR]

Objectives: The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage. Methods: This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends. Results: The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide. Conclusions: A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation 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.)

Drones are a promising technology performing various functions, ranging from aerial photography to emergency response, requiring swift fault diagnosis methods to sustain operational continuity and minimise downtime. This optimises resources, reduces maintenance costs, and boosts mission success rates. Among these methods, traditional approaches such as visual inspection or manual testing have long been utilised. However, in recent years, data representation methods, such as deep learning systems, have achieved significant success. These methods learn patterns and relationships, enhancing fault diagnosis, but also face challenges with data complexity, uncertainties, and modelling complexities. This paper tackles these specific challenges by introducing an efficient representation learning method denoted Multiverse Augmented Recurrent Expansion (MVA-REX), allowing for an iterative understanding of both learning representations and model behaviours and gaining a better understanding of data dependencies. Additionally, this approach involves Uncertainty Bayesian Optimisation (UBO) under Extreme Learning Machine (ELM), a lighter neural network training tool, to tackle both uncertainties in data and reduce modelling complexities. Three main realistic datasets recorded based on acoustic emissions are involved in tackling propeller and motor failures in drones under realistic conditions. The UBO-MVA Extreme REX (UBO-MVA-EREX) is evaluated under many, error metrics, confusion matrix metrics, computational cost metrics, and uncertainty quantification based on both confidence and prediction interval features. Application compared to the well-known long-short term memory (LSTM), under Bayesian optimisation of the approximation error, demonstrates performances, certainty, and cost efficiency of the proposed scheme. More specifically, the accuracy obtained by UBO-MVA-EREX, ~0.9960, exceeds the accuracy of LSTM, ~0.9158, by ~8.75%. Besides, the search time for UBO-MVA-EREX is ~0.0912 s, which is ~98.15% faster than LSTM, ~4.9287 s, making it highly applicable for such challenging tasks of fault diagnosis-based acoustic emission signals of drones. [ABSTRACT FROM AUTHOR]

Plane rotary landslides are characterized by their concealment and challenges in prevention, having caused considerable impacts in recent years. This study aims to elucidate the causative factors behind plane rotary landslides and develop a novel method for assessing their stability through a case. Our focal case, a landslide with weak interlayers, occurred on 1 April 2020, in an open‐pit limestone mine in Sichuan Province, China. The landslide demonstrated distinctive rotational behaviour deviating 45° from the expected direction, presenting a unique case of plane rotary deformation. Employing a multifaceted approach, including geological data analysis, UAV aerial photography, field investigations, laboratory experiments and displacement monitoring, we delved into the deformation and failure mechanisms. Furthermore, A three‐dimensional discrete element method was employed to examine the evolution of the landslide and its relationship with the stable rock mass barrier. The study unravelled the structural alterations of the slope due to mining activities and the degradation of physical and mechanical properties in weak interlayers, which were instrumental in triggering this geological event. The presence of the stable rock mass barrier causes the resultant forces acting on the sliding mass not to pass through its centre of gravity during deformation, thereby generating a moment that induces the rotation. The additional resistance also causes the landslide displacement to transition from being initially dominated by the leading edge to being driven by the trailing edge. This unique displacement pattern serves as a crucial reference for identifying plane rotary landslides. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of reconstruction and immersive visualization of cultural heritage objects such as medieval strongholds. Geomatics-specific methods were applied, based on the use of reference data such as maps, topographic databases, LIDAR, aerial photography, and three-dimensional modeling. Search and transformation of reconstructed historical data were also carried out according to various historical–geographical–archaeological theories. The result of the work was the creation of a desktop application that presents the interactive landscape reconstruction as an immersive geovisualization of a typical medieval Eastern European earth-and-timber stronghold in two time periods and in its current state. Conclusions include the changes in land topographical levels, demonstrating how the topographical level has changed over time, and data identification to categorize and process it in order to visualize cultural heritage objects such as medieval strongholds in today's landscape. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geomechanics is the property of Journal of Geomechanics 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 Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset. [ABSTRACT FROM AUTHOR]

Addressing the issue of incorrect and missed detections caused by the complex types, uneven scales, and small sizes of defect targets in transmission lines, this paper proposes a defect-detection method based on cross-scale feature fusion, PGE-YOLO. Firstly, feature extraction is enriched by replacing the convolutional blocks in the backbone network that need to be cascaded and fused using the Par_C2f network module, which incorporates a parallel network (ParNet). Secondly, a four-layer efficient multi-scale attention (EMA) mechanism is incorporated into the network's neck to address long and short dependency issues. This enhancement aims to improve global information retention by employing parallel substructures and integrating cross-space feature information. Finally, the paradigm of generalized feature fusion (GFPN) is introduced and reconfigured to develop a novel CE-GFPN. This model effectively integrates shallow feature information with deep feature information to enhance the capability of feature fusion and improve detection performance. Using a real transmission line multi-defect dataset from UAV aerial photography and the CPLID dataset, ablation and comparison experiments with various models demonstrated that our model achieved superior results. Compared to the initial YOLOv8n model, our model increased the detection accuracy by 6.6% and 1.2%, respectively, while ensuring there is no surge in the number of parameters. This ensures that the real-time and accuracy requirements for defect detection in the industry are satisfied. [ABSTRACT FROM AUTHOR]

Urban sections of watercourses are increasingly valued today, especially if accompanied by trees. In many places, their reconstruction is beginning, for which it is essential to understand their formation and history. We studied the changes of woody vegetation and landscape use on the basis of military and cadastral maps from the 18th to the 20th century, aerial photographs from the second half of the 20th century, photographic records from the 20th and 21st century and satellite images from the 21st century in relation to the changes of the riverbed morphology along the Bükkös Stream in Visegrád Mountains (Central Hungary), thus tracing the changes over a period of 257 years. A review of descriptions in local publications of landscape use, and particularly the associated riverbed adjustments, helped to build up a detailed picture. The band of woody vegetation along the stream is still visible in the middle reaches of the stream on the 1765 boundary survey map but was absent for a considerable length by the 1780s. Thereafter, from the 1880s to the 1970s, the stream bank was forested along its entire length, except for the downtown section in Szentendre. In some stretches alder groves have developed, in others alder-dominated or mixed woodland strips thrived, while elsewhere alder has failed to re-establish or persist due to maintenance or previous forestry and flood defence works. In the upper sections of the stream valley, oak, beech, and ravine and scree slope forests may have been, and still are, present. Spontaneous regeneration of woody vegetation along the stream depends on the species involved, their dispersal pattern, the underlying environmental conditions and the morphology of the stream bed and valley. The results can contribute to the planning and implementation of process-based habitat restoration, which has now become an objective of the local government. [ABSTRACT FROM AUTHOR]

Archaeological aerial thermography has traditionally focused on bare ground terrain; however, recent developments in drone technology have prompted a reconsideration of thermal analysis on cultivated fields. This study investigates three different sites using drones equipped with thermal, RGB and multispectral sensors to identify archaeological anomalies. This research challenges the traditional focus of thermal cameras on vegetation‐free terrains by investigating cultivated land, where the perceived temperature is influenced by evapotranspiration—a combination of soil evaporation and vegetation transpiration. While agricultural studies have emphasized the ability of thermal sensors to detect varying temperatures in irrigated vegetation, archaeology has mainly used multispectral sensors for vegetated land. The study shows that in wheat‐covered fields, thermal analysis outperforms multispectral and RGB sensors in detecting anomalies associated with archaeological features. Unexpectedly, optimal anomaly detection occurs during mid‐morning and mid‐afternoon flights, challenging traditional ideas about the timing of thermal analysis. The research highlights the need for renewed interest in the use of thermal cameras for archaeological anomaly detection in cultivated fields. However, further comparative studies between thermal and multispectral analyses on different sites are essential to establish the wider effectiveness of thermal sensors. This study challenges established notions of archaeological aerial thermography and argues for a re‐evaluation of sensor selection and flight timing to improve the detection of archaeological features in cultivated fields. [ABSTRACT FROM AUTHOR]

Automated observation methods for monitoring river ice in high-latitude urban areas are crucial for resource utilization, risk assessment, and navigation. However, current research lacks actual-scale river ice classification, such as low-altitude surveys. This study established a dataset of river ice in the Songhua River near Harbin, Northeast China, using UAV aerial photography and applied the RININet semantic segmentation algorithm for precise classification of different ice types in low-altitude aerial remote sensing images. To address environmental challenges, a feature extraction method integrating channel and spatial attention mechanisms was adopted, along with an improved pyramid pool structure to enhance feature recognition. Additionally, a two-stage transfer learning method established an ice recognition database, overcoming issues like small data volume and high annotation costs. Comparative evaluation metrics demonstrated the high accuracy of the semantic segmentation framework. Furthermore, a method for estimating ice blockage risk was proposed, applicable to various urban river ice management tasks, with practical significance. [ABSTRACT FROM AUTHOR]

This article explores a little-known archive of historical aerial photographs curated by the General Directorate of Mapping of the Republic of Türkiye's Ministry of Defense and discusses the historical context of their production by US Navy aerial photography squadrons in the 1950s. While the images themselves enable a technical analysis of the method of their collection, contemporary military manuals, domain-specific magazines and newsletters, and eyewitness accounts of how similar photographs were captured fill out the contexts of their production for cartographic purposes, with information about the aircraft involved, their cameras and camera configurations, and mission characteristics. Continuing sections situate the aerial surveys within the framework of US-led initiatives in mapping NATO territories following World War II. As one example of what must have been many special mapping agreements made between NATO countries at this time, the US cartographic surveys over Türkiye discussed here are an expression of postwar realignments of global power, put to the purposes of containment-based security preparations and infrastructure development, and neatly intertwining American military and commercial interests early in the Cold War. [ABSTRACT FROM AUTHOR]

A common practice used by the Germans and collaborators in World War II, as part of the Holocaust, was to use existing Jewish cemeteries as places for mass burial. Research was completed at the Old Jewish Cemetery in Riga, Latvia, the Livas Jewish Cemetery in Liepaja, Latvia, and the Zaliakalnis Jewish Cemetery in Kaunas, Lithuania. The Old Jewish Cemetery in Riga was adjacent to the Riga Ghetto and was used to bury individuals murdered in the ghetto. In Kaunas, an area of the Zaliakalnis Jewish Cemetery is devoid of grave stones, and literature sources and testimony indicate that this area was used for the mass burial of Jews from the Kaunas Ghetto and other mass killings. In Liepaja, the local Jewish Heritage Foundation believes that there are mass graves within the Livas Cemetery. Methodologies for this research include the use of a pulseEkko Pro 500-megahertz ground-penetrating radar (GPR) system. Electrical resistivity tomography (ERT) data were collected through a linear array of electrodes coupled to a direct current (DC) resistivity transmitter and receiver. Analysis of aerial photography and satellite images was also employed at each location. ERT and GPR data indicate three separate trench anomalies in the Old Jewish Cemetery in Riga. The presence of these anomalies corroborates Holocaust survivor testimony that bodies were buried in mass graves in that area. In the Zaliakalnis Jewish Cemetery in Kaunas, ERT and GPR data indicate an anomaly in the western part of the cemetery, and ERT data further indicate two other possible mass graves. In Liepaja, preliminary GPR analysis indicates an anomaly in a cleared section of the cemetery. Based on the presence of geophysical anomalies in all three cemeteries, which correlate with literature sources and Holocaust survivor testimony, there is a high probability that mass graves are present at each site. Future research directions include expanding the search areas in each cemetery, additional literature and testimony-based research, and the addition of other geophysical methodologies. [ABSTRACT FROM AUTHOR]

Remote sensing was used to map the invasion of yellow-flowered legumes on the Central Plateau of New Zealand to inform weed management strategy. The distributions of Cytisus scoparius (broom), Ulex europaeus (gorse) and Lupinus arboreus (tree lupin) were captured with high-resolution RGB photographs of the plants while flowering. The outcomes of herbicide operations to control C. scoparius and U. europaeus over time were also assessed through repeat photography and change mapping. A grid-square sampling tool previously developed by Manaaki Whenua—Landcare Research was used to help transfer data rapidly from photography to maps using manual classification. Artificial intelligence was trialled and ruled out because the number of false positives could not be tolerated. Future actions to protect the natural values and vistas of the Central Plateau from legume invasion were identified. While previous control operations have mostly targeted large, highly visible legume patches, the importance of removing outlying plants to prevent the establishment of new seed banks and slow spread has been underestimated. Outliers not only establish new, large, long-lived seed banks in previously seed-free areas, but they also contribute more to range expansion than larger patches. Our C. scoparius and U. europaeus change mapping confirms and helps to visualise the establishment and expansion of uncontrolled outliers. The power of visualizing weed control strategies through remote sensing has supported recommendations to improve outlier control to achieve long-term, sustainable landscape-scale suppression of invasive legumes. [ABSTRACT FROM AUTHOR]

Small object detection for unmanned aerial vehicle (UAV) image scenarios is a challenging task in the computer vision field. Some problems should be further studied, such as the dense small objects and background noise in high-altitude aerial photography images. To address these issues, an enhanced YOLOv8s-based model for detecting small objects is presented. The proposed model incorporates a parallel multi-scale feature extraction module (PMSE), which enhances the feature extraction capability for small objects by generating adaptive weights with different receptive fields through parallel dilated convolution and deformable convolution, and integrating the generated weight information into shallow feature maps. Then, a scale compensation feature pyramid network (SCFPN) is designed to integrate the spatial feature information derived from the shallow neural network layers with the semantic data extracted from the higher layers of the network, thereby enhancing the network's capacity for representing features. Furthermore, the largest-object detection layer is removed from the original detection layers, and an ultra-small-object detection layer is applied, with the objective of improving the network's detection performance for small objects. Finally, the WIOU loss function is employed to balance high- and low-quality samples in the dataset. The results of the experiments conducted on the two public datasets illustrate that the proposed model can enhance the object detection accuracy in UAV image scenarios. [ABSTRACT FROM AUTHOR]

In response to the challenges of remote sensing imagery, such as unmanned aerial vehicle (UAV) aerial imagery, including differences in target dimensions, the dominance of small targets, and dense clutter and occlusion in complex environments, this paper optimizes the YOLOv8n model and proposes an innovative small-object-detection model called DDSC-YOLO. First, a DualC2f structure is introduced to improve the feature-extraction capabilities of the model. This structure uses dual-convolutions and group convolution techniques to effectively address the issues of cross-channel communication and preserving information in the original input feature mappings. Next, a new attention mechanism, DCNv3LKA, was developed. This mechanism uses adaptive and fine-grained information-extraction methods to simulate receptive fields similar to self-attention, allowing adaptation to a wide range of target size variations. To address the problem of false and missed detection of small targets in aerial photography, we designed a Semantics and Detail Infusion Feature Pyramid Network (SDI-FPN) and added a dedicated detection scale specifically for small targets, effectively mitigating the loss of contextual information in the model. In addition, the coordinate adaptive spatial feature fusion (CASFF) mechanism is used to optimize the original detection head, effectively overcoming multi-scale information conflicts while significantly improving small target localization accuracy and long-range dependency perception. Testing on the VisDrone2019 dataset shows that the DDSC-YOLO model improves the mAP0.5 by 9.3% over YOLOv8n, and its performance on the SSDD and RSOD datasets also confirms its superior generalization capabilities. These results confirm the effectiveness and significant progress of our novel approach to small target detection. [ABSTRACT FROM AUTHOR]

Nowadays, unmanned aerial vehicles (UAVs) have achieved rapid development due to their flexible flight modes and broad application prospects, which have played an important role in scenarios such as aerial photography, unmanned mapping, agricultural plant protection, and power inspection. At the same time, as the frequency of global forest fires is increasing year by year, traditional monitoring and search and rescue methods have little effect. People have begun to consider the introduction of UAVs for forest monitoring and disaster relief. However, in practical applications, when a single UAV performs tasks, there are problems of limited energy, poor robustness, and easy failures that affect the execution efficiency of global tasks. Therefore, it is necessary to obtain the deployment plan of the UAVs offline in advance, and then fine-tune the position of the UAVs online. This paper focuses on the solution of UAV deployment model offline. In complex forest scenarios, due to terrain and signal coverage issues, we need to optimize this when deploying UAVs. At the same time, our goal is to find the location of deployable UAVs in the forest area to maximize the global coverage area of UAVs. Therefore, we divide the forest areas where UAVs need to be deployed and perform a grid modeling on them. On the basis of grid modeling, an Adaptive Multiple Pruning Search Method (AMPSM) is proposed to solve the global UAV deployment and maximum coverage area. In our experiments, we conducted a comparative analysis with the geometric solution to solve the maximum coverage area, which proved the feasibility of the model and the method. The results show that in the complex forest environment, our research can meet the requirements to a great extent, which can be extended to more application scenarios. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing is a new paradigm for intelligent mobile devices to collect and share various types of sensing data in the urban environment. The recent rapid development of unmanned aerial vehicle (UAV) technology facilitates the realization of crowdsensing because UAVs have high-efficiency mobility in the urban environment and have been used in various areas of aerial photography, agriculture, plant protection, express transportation, disaster relief, and so on. However, for UAVs, one of the key issues for the archival of efficient crowdsensing is task allocation, which must balance the task quality and cost. This paper first proposes a mathematical model for task allocation for UAVs in crowdsensing. Then, for the effectiveness of data sensing, three algorithms are proposed to allocate tasks for the purpose of minimizing the incentive cost while ensuring the quality of sensing data. The proposed algorithms include the minimum cost first (MCF) algorithm, which assigns a high priority to UAVs with the lowest cost, the maximum ratio first (MRF) algorithm, which assigns a high priority to UAVs with a high ratio of sensing quality and sensing cost, and a genetic algorithm-based one (GA-TA), which comprehensively considers the factors such as UAV sensing quality, sensing cost, and execution ability. Experimental results show that, compared with MCF and MRF, GA-TA achieves the lowest total sensing cost, average task cost, and total moving distance of UAVs, and the highest average contribution of UAVs. Considering all factors, GA-TA is the best task allocation algorithm for UAVs in crowdsensing on average. [ABSTRACT FROM AUTHOR]

The relevance of the problem examined is the need for research to ensure the safety and efficiency of construction and operation of utility networks, in light of the rapid development and growth of urban infrastructure. The purpose of this research is to explore and evaluate the application of topographic-geodetic studies in the context of the design and construction of utilities. The methods used include analytical method, classification method, functional method, statistical method, synthesis method. At the preparatory stage of works the schedule of all stages of project implementation was developed, coordinate and height systems accepted in the territory of the settlement, topographic and geodetic materials, general plans, detailed planning plans, and schemes of settlement development were established. Executive surveys, the territory of Aksukent settlement, and other materials and documents identified in the process of preparatory works were collected and analysed. Using an unmanned aerial vehicle (UAV) data of plan-altitude substantiation and aerial photography were obtained, orthophoto map in WGS84 and MSC coordinate system of the Aksukent settlement territory was obtained. To update the data on existing points of the state geodetic network, complexes of geodetic measurements and calculations were performed and parameters (key) of transformation between WGS84 and WCS coordinate systems were obtained. In the course of the field works, deciphering work was performed based on the obtained orthophoto. The inventory of engineering networks was conducted, which included engineering-geodetic surveys of underground and surface engineering networks (sewerage, water supply, gas supply, heating mains), and engineering-geodetic surveys of overhead and underground cable power lines. The results of the survey of the traces of engineering communications on the territory of Aksukent village were agreed with the balance holders of networks JSC "Turkestan Regional Telecommunications Directorate Kazakhtelecom", JSC "KazTransGas Aimak", LLP "Sairam Tazalyk", LLP "Ontustik Zharyk Transit". The research is of practical significance, as it contributes to the accurate determination of network parameters, identification of potential problem areas and ensuring effective planning and management of engineering projects. [ABSTRACT FROM AUTHOR]