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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]

Tree detection and canopy area measurement are important and difficult tasks in forest inventory, which are important for understanding forest stand structure. This study utilized remotely piloted aircraft (RPA) aerial photography technology to collect remote sensing images of forests in Xiong County, China, creating a dataset comprising 1200 images of six tree species. Based on this dataset, the paper proposes an optimized model, Att-Mask R-CNN, for canopy detection and segmentation. Att-Mask R-CNN outperforms the original models (Mask R-CNN and MS R-CNN) by achieving 65.29% mean average precision for detection, 80.44% mean intersection over union for segmentation, and 90.67% overall recognition rate for the six tree species. In addition, a pixel statistics method based on segmentation masks is introduced for estimating the vertical projected area of individual tree crowns, and comparisons between the measured and predicted vertical projected area of the crowns of six tree species (100 trees of each class) show an overall goodness-of-fit R2 of 85% and a relative root-mean-square error rRMSE of 12.81%. By using remote sensing images from RPAs and optimizing existing deep learning models, the detection and segmentation of individual tree canopies can be achieved, resulting in a more accurate understanding of forest structure, which provides scientific support for forest management and resource monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Virtual Archaeology Review is the property of Virtual Archaeology Review 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.)

In response to the insufficient of the traditional "RTK+leveling"measurement method for retest of existing railway lines, such as high safety risks, slow measurement speed, and poor applicability of multisource data. This research proposed a non-contact measurement operation for existing railway lines based on "UAV+LiDAR". Under the control of flight speed, aerial photography altitude, ground base stations, and equipment pulse frequency, high-precision and high-density point cloud data could be obtained. At the same time, target points were set up along both sides of the existing line at a distance of 500 m. After point cloud aerial belt registration and target correction processing, the relative and absolute accuracy could be further improved. The research results indicate that the mean squared error of the existing line centerline plane and elevation as determined by the method, can reach 1.77 cm and 1.27 cm, respectively. The method enhances safety and flexibility by avoiding on-railway work, reduces field measurement labor, improves production efficiency, and offers extensive potential for point cloud data applications. It shows promising prospects for has good application prospects in railway capacity expansion and transformation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture 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.)

Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image‐derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R2 (p < 0.026). Currently, BaMGRID is best‐suited for site‐by‐site analysis to support practical applications at the reach scale; continuous, basin‐wide mapping of river bathymetry will require additional research. Key Points: Combine depth measurements made during site visits to gaging stations with archived images to enable remote sensing of river bathymetryMultispectral satellite images acquired daily can yield highly accurate depth estimates, but high resolution air photos were less accurateBathymetric Mapping using Gage Records and Image Databases (BaMGRID) is well suited to site‐by‐site analysis for reach‐scale applications [ABSTRACT FROM AUTHOR]

Unmanned aerial systems (UASs) find diverse applications across military, civilian, and commercial sectors, including military reconnaissance, aerial photography, environmental monitoring, precision agriculture, logistics, and rescue operations, offering efficient, safe, and cost-effective solutions to various industries. To ensure the stable and reliable operation of UASs, fault diagnosis is essential, which can enhance safety, and minimize potential risks and losses. However, most existing fault diagnosis methods rely on a single physical quantity as the primary information source or solely consider fault data at a single moment, leading to challenges of low diagnostic accuracy and limited reliability. Aimed at this problem, this paper presents a fault diagnosis method based on time–space domain weighted information fusion for UASs. First, the Gaussian fault model is constructed for the data with different fault features in the space domain. Next, the weighted coefficient method is used to generate the basic probability assignment (BPA) by matching the fault data with the Gaussian fault model. Then, the Dempster's combination rule, which enables the Dempster–Shafer (D-S) evidence theory, is adopted to fuse the generated BPAs. Based on this, the pignistic probability transformation is performed to determine the fault type. Finally, numerical results demonstrate the effectiveness of the proposed fault diagnosis method in accurately identifying the fault types of UASs. [ABSTRACT FROM AUTHOR]

Although object detection has achieved great success in the field of computer vision in the past few years, the performance of detecting small objects has not yet achieved ideal results. For instance, UAV aerial photography object detection plays an important role in traffic monitoring and other fields, but it faces some great challenges. The objects in aerial images are mainly small objects, the resolution of whom is low and the feature expression ability of whom is very weak. Information will be lost in high-dimensional feature maps, and this information is very important for the classification and positioning of small objects. The most common way to improve small object detection accuracy is to use high-resolution images, but this incurs additional computational costs. To address the above-mentioned problems, this article proposes a new model SINextNet, which uses a new dilated convolution module SINext block. This module is based on depth-separable convolution, and can improve the receptive field of the model. While extracting small object features, it can combine small object features with background information, greatly improving the feature expression ability of small objects. The experimental results indicate that the method proposed in this paper can achieve advanced performance across multiple aerial datasets. [ABSTRACT FROM AUTHOR]

In Master Plan for Parks and Open Spaces (or Midori-no-Kihonkeikaku), municipalities delineate their future visions, goals, and strategies for the conservation and promotion of green areas. Regular surveys are typically conducted, especially in densely populated urban areas, to assess and monitor these green areas. However, the data collection approaches are not standardized and left to the discretion of each municipality, considering factors such as cost, effort, desired accuracy, and comparability with historical data. Consequently, the data published by various municipalities lack uniformity, complicating comparisons and a comprehensive understanding of the situation. Furthermore, the rapid advancement in image analysis technology has placed municipal survey methods in a transitional phase. This study targeted 20 special status cities, called “ordinance designated cities” in Japan, to ascertain the current state of vegetation cover surveys method in each city. Interviews were conducted with ordinance designated city from 30th May to 2nd June. As of 2024, eight ordinance designated cities utilized aerial photography and nine used satellite imagery to measure vegetation cover, indicating a nearly equal distribution. Additionally, three cities did not measure vegetation cover. Most cities had not altered their survey methods, but those that did primarily cited cost as the driving factor, with many transitioning from aerial photography to satellite imagery. This shift raised concerns about the consistency of past and future data, underscoring the need to develop methods for integrating data with varying resolutions and coverage areas.

Unmanned Aerial Vehicle (UAV) technology in conjunction with aerial photography and aerial Light Detection and Ranging (LiDAR) was used for topographic surveying of the Win 3 Wind Power Project area, a complex mountainous terrain situated in the Huong Hoa district of Quang Tri province, Vietnam with the primary objective to produce a map with a scale of 1:1,000 with a contour interval of 1.0 m, accompanied by 3D point clouds, a Digital Elevation Model (DEM), and an orthomosaic. Employing the UAV LiDAR system of DJI Matrice 300 RTK, we conduct ed survey flights, achieving a high point density laser scanning of 229 points/m2 and capturing ultra-high-resolution imagery at 4.22 cm/pixel. The integration of aerial LiDAR and photographic data results in a rich and detailed information repository regarding the surveyed terrain demonstrating that UAV LiDAR technology is a robust tool for geographic data collection, particularly in challenging terrain conditions in Vietnam. We also evaluated the accuracy of the created topographic map, achieving results that surpass the technical requirements and quality standards of the project. This underscores the potential and effectiveness of UAV LiDAR technology in the field of large-scale geographic data collection and mapping in Vietnam.

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.

The Roman town of Falerii Novi sits 50km north of Rome on the ancient Via Amerina. According to historical sources, it was founded after the Roman conquest of the nearby Faliscan centre of Falerii Veteres in 241 BC (Polyb. 1.65; Liv. Epit. 20; Eutrop. 2.28; Oros. 4.11; Zon. 8.18). The urban site appears to have persisted at least until the first half of the sixth century AD, with the bishopric of Aquaviva ascribed to the site in AD 465 (Duchesne, 1892). A monastery and church of Santa Maria di Falleri were founded by Cistercians and first mentioned in documents of the eleventh and twelfth centuries, now forming the only standing structure on site other than the ancient circuit walls.

The advancement of UAV technologies has enabled the automated capture of photos and videos, eliminating the need for manual intervention in flight control. (Research purpose) This research aims to conduct a retrospective analysis of the evolution offlight control systems and the development of aerial photography equipment for agricultural land, covering the period from the mid-19th century to present. (Materials and methods) A systematic literature review was conducted using the historical-analytical method. The paper examines original works by both domestic and international authors, including monographs, scientific journals, conference proceedings, museum exhibitions, photographic archives, and open-source software code. (Results and discussion) The paper identifies six distinct phases in the development of aerial photography and flight control systems. The classification is based on key innovations in camera types, control systems, and aircraft designs. Each phase highlights the predominant cameras, control systems, and aircraft utilized for agricultural applications. (Conclusions) Over the past 165 years, notable changes have occurred in aerial photography parameters, including the type of photographic material, image spatial and spectral resolution, camera weight and mounting, shutter types and their mechanisms, inertial control units, integrated GPS/GLONASS receivers, and light sensors. In terms of flight control systems for UAVs, significant developments over the last 106 years include variations in flight control types, the number offlight-stabilizing sensors, obstacle detection systems, size of the flight control units, flight modes, and takeoff/landing techniques, along with interfaces for attachments. It is anticipated that future intellectualization and miniaturization of flight control systems will not only boost UAV performance but also reduce the economic costs associated with the aerial monitoring of agricultural biological assets.

Debris flow is a common geological disaster in mountainous areas, which occurs frequently in the rainy season and has strong mobility and destructiveness, often bringing serious loss of life and property. Previous studies have focused more on single outbreaks of debris flow disasters, and less on multiple outbreaks of debris flow disasters in the same gully during the rainy season, which makes it more difficult to carry out prevention and control work for similar disasters. Because the occurrence of the disaster has its corresponding geomechanical model as the basis, which provides the necessary scientific basis for the prevention and control of the disaster. Through data collection, field investigation, drone aerial photography, modelling and rainfall data analysis, the geological environment of Yizhong River Basin and the frequent occurrence mechanism of debris flow disasters were investigated, and a conjugate shear-type disaster-inducing geomechanical model was proposed. With the help of RAMMS numerical simulation software, the dynamic mechanism of the disaster threatening the life and property safety of the residents in Deqin County was analysed to provide the necessary scientific basis for the prevention and control of the Yizhong River debris flow disaster. Base on the characteristics of the geological environment in alpine and gorge areas, the necessity and advantage of applying the nap-of-the- object photogrammetry technology to engineering geological survey in alpine and gorge areas are put forward. [ABSTRACT FROM AUTHOR]

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git. [ABSTRACT FROM AUTHOR]

Agriculture is an area that currently benefits from the use of new technologies and techniques, such as artificial intelligence, to improve production in crop fields. Zacatecas is one of the states producing the most onions in the northeast region of Mexico. Identifying and determining vegetation, soil, and humidity zones could help solve problems such as irrigation demands or excesses, identify spaces with different levels of soil homogeneity, and estimate the yield or health of the crop. This study examines the application of artificial intelligence through the use of deep learning, specifically convolutional neural networks, to identify the patterns that can be found in a crop field, in this case, vegetation, soil, and humidity zones. To extract the mentioned patterns, the K-nearest neighbor algorithm was used to pre-process images taken using unmanned aerial vehicles and form a dataset composed of 3672 images of vegetation, soil, and humidity (1224 for each class). A total of six convolutional neural network models were used to identify and classify the patterns, namely Alexnet, DenseNet, VGG16, SqueezeNet, MobileNetV2, and Res-Net18. Each model was evaluated with the following validation metrics: accuracy, F1-score, precision, and recall. The results showed a variation in performance between 90% and almost 100%. Alexnet obtained the highest metrics with an accuracy of 99.92%, while MobileNetV2 had the lowest accuracy of 90.85%. Other models, such as DenseNet, VGG16, SqueezeNet, and ResNet18, showed an accuracy of between 92.02% and 98.78%. Furthermore, our study highlights the importance of adopting artificial intelligence in agriculture, particularly in the management of onion fields in Zacatecas, Mexico. The findings can help farmers and agronomists make more informed and efficient decisions, which can lead to greater production and sustainability in local agriculture. [ABSTRACT FROM AUTHOR]

The National Aerial Photo Database is a collection of aerial photographs obtained through aerial photography using specific cameras or sensors with manned or unmanned aerial vehicles across the territory of the Republic of Indonesia. These photographs are stored in a computer system for easy access, management, and updating. No national aerial photo database system currently meets the need for accurate and complete aerial photos and other geospatial information to support decision-making. The present study aimed to develop a model for the National Aerial Photo Database from a geospatial intelligence perspective based on statutory regulations. The collection of aerial photo data in a centralized database commenced with scanning negative film based on location and flight paths and copying all digital data. The original negative films were scanned at 20 µm resolution in a Vexcel Ultrascan 5000 photogrammetry scanner. The resulting size of one scanned photo is 140 Mb to 160 Mb. This research adopts a qualitative research method with a case study approach. This research involved the electronic implementation of geospatial data and information by utilizing Geographic Information System (GIS) software, image and geospatial information data management, and the Scrum framework. This research concluded that geospatial intelligence efforts can better understand activity patterns, infrastructure development, and potential threats in the national territory. The insights gained from the aerial photo database aid in identifying trends, detecting anomalies, and assessing risks, all of which are critical for better decisionmaking in national security. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information on urban built-up areas is significant for managing urban growth and development. Earth Observation (EO) data are valuable sources for meeting this demand. However, the extraction of urban built-up areas from EO data is challenging due to the limitations of EO data sources. To overcome this challenge, this study follows an approach that assesses the performance of optical (Sentinel-2), radar (Sentinel-1) and fused (Sentinel-1 and Sentinel-2) data to extract urban built-up areas using machine learning algorithms including Random Forest (RF), K-Nearest Neighbors (KNN) and KDTree KNN. The results were statistically analyzed by considering the Overall Accuracy (OA) and kappa coefficient. In addition, 15 cm GSD (Ground Sample Distance) aerial photography of the study area was used to validate the results. According to the results, Sentinel-2 produced better representation and accuracy of urban built-up areas than Sentinel-1 and even the fused image. Regarding to machine learning algorithms classification performance, RF performed better in both OA and Kappa coefficient along all datasets. The research findings can have significant implications for various domains, such as urban planning, land use management and open avenues for further comparisons of different EO data sources and machine learning algorithms for built-up areas extraction. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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.)

Since 2018, the authors have been working on reconstructing the exterior of the Great Pitiunt Roman fortress in 3D. This article presents the results of the visualization of the exterior and interior of the temple complex. During the study, the dimensions and plans of the site were analyzed, revealing discrepancies in various sources. To clarify the complex's dimensions, aerial photography using UAV was conducted, and photogrammetric models, orthophoto, and digital surface models were created. The research also uncovered previously unrecorded architectural features. During the reconstruction of the temples, much attention was paid to the structural design of the buildings, connections and load distribution. Engineering calculations have been carried out for the clarification of the structural solutions. The article presents the results of a detailed reconstruction of the exterior, interior and structural features of Temple Nos. 1–4, based on preserved archaeological evidence, excavation results, contemporaneous Early Christian sites, and an analysis of the materials and technologies used at the time. The reconstruction of the mosaic floor of Temple No. 2 allowed a realistic visualization of the interior. [ABSTRACT FROM AUTHOR]

A destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year−1 and between − 24 and 6 mm year−1, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year−1 and between − 18 and 20 mm year−1, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the mechanisms and kinematic characteristics of landslides in the southwestern mountainous regions of China. Field surveys and aerial photography were conducted to establish a discrete element method model with calibrated mesomechanical parameters. By considering internal and external triggering factors, a coupled finite difference and discrete element method was used to simulate the deformation, failure mechanism and movement process of slopes as well as to analyse the characteristics of debris movement. The findings revealed that the unique lithological and unfavourable geological structures resulted in an extremely fragmented rock mass, which was the internal cause of landslides. The main external cause was the mining of coal seams beneath the slumped mass, leading to extensive cracks at the trailing edge of the slope and subsequent debris movement. The average velocity of the slumped mass exhibited fluctuation before 3.5 s, followed by a rapid increase at 3.5 s, reaching a maximum speed of approximately 9.5 m/s at 9.8 s. The velocity and displacement of the debris gradually decreased from the bottom to the top in the sliding source area. The maximum displacement of the debris was 1.24 km. The entire landslide process lasted approximately 47 s, with the debris accumulating mostly at the toe of the slope. The research findings provide valuable theoretical support for the prevention and control of geological hazards induced by underground mining activities. [ABSTRACT FROM AUTHOR]

Copyright of VAD: Veredes, Arquitectura y Divulgación is the property of Alberto Alonso Oro 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.)

Traditional manual inspection technology has the problems of high risk, low e.ciency, and being time-consuming in bridge safety management.)e unmanned aerial vehicle (UAV)-based detection technology is widely used in bridge structure safety monitoring. However, the existing deep learning-based concrete crack identi6cation method has great limitations in dealing with complex background and tiny cracks in bridge structures. To address these problems, this study designs a crack pixel-level highperformance segmentation model for bridge concrete cracks that is suitable for UAV detection scenarios using machine vision (MV) and deep learning (DL) algorithms. First, considering the high requirements for the computing performance of the MVbased model for UAV-based detection, the ResNet-18-based lightweight convolutional neural network is used to represent the traditional large-scale backbone network of the pyramid scene parsing network (PSPNet) to develop a high-performance crack automatic identi6cation model.)en, considering that bridge concrete cracks have the characteristics of subtle shapes and complex backgrounds, the spatial position self-attention module is inserted into the PSPNet to improve its detection accuracy. A concrete bridge is used for the case study, and a dataset of cracks in bridge concrete structures collected by UAVs is constructed and used for model training.)e experimental results show that the loss function of the developed method in the training process results in a smooth decline, and the developed algorithm achieves the evaluation indicators of 0.9008 precision, 0.8750 recall, 0.8820 accuracy, and 0.9012 IOU on the bridge concrete crack dataset, which are signi6cantly higher than other state-of-the-art baseline methods. In addition, four common UAV bridge detection scenarios, including low light, complex crack forms, high background roughness, and complex background scenes, are used to further test the crack detection ability of the developed crack identi6cation model.)e experimental results show that the proposed crack identi6cation method can e@ectively overcome interference and real-size pixel-level segmentation of crack morphology. In addition, it also achieved a detection e.ciency of 35.04 FPS, which shows that the real-time detection ability of the method has good applicability in the UAV detection scene. [ABSTRACT FROM AUTHOR]

To address the challenges of detecting a large number of objects and a high proportion of small objects in aerial drone imagery, an aerial dense small object detection algorithm called coordinate position attention module you only look once (CPAM‐YOLO) is proposed based on the coordinate position attention module (CPAM). In the backbone network of CPAM‐YOLO, a CPAM is proposed and embedded that decomposes channel attention into two 1D feature encoding processes, and selectively combines the features of each position through the weighted sum of all position features. Finally, features are aggregated along two spatial directions, increasing the effective information utilization of input feature positions and channels. The backbone network, feature enhancement network, and detection heads have been optimized to improve detection accuracy while ensuring a lightweight detection network. Using lightweight backbone networks to significantly reduce the number of parameters while using high‐resolution feature enhancement networks to retain more semantic and detailed features. The algorithm's performance was evaluated using the publicly available VisDrone2019 dataset. Compared to the baseline network YOLOv5l, CPAM‐YOLO achieved a 4.5% improvement in mAP0.5 and a 3.2% improvement in mAP0.95. These experimental results demonstrate the strong practicality of the CPAM‐YOLO object detection network for detecting dense small objects in aerial image. [ABSTRACT FROM AUTHOR]

This article uses archival resources to investigate the material remains of tank warfare at Bullecourt. During the Battle of Arras, tanks were used to assist the British 1917 Spring Offensive. The first assault at Bullecourt was a complete disaster and in addition to the high infantry casualties, the majority of the tanks employed were destroyed on the battlefield. The action of the tanks, their routes and final positions in the landscape has often been neglected because accurate sources are limited. This article incorporates tankography viz. combining unit war diaries, contemporary historical aerial and ground photographs, and soldier's testimonies to determine the spatial distribution and possible sites for locating material remains of the derelict tanks. [ABSTRACT FROM AUTHOR]

Drone semantic segmentation is a challenging task in computer vision, mainly due to inherent complexities associated with aerial imagery. This paper presents a comprehensive methodology for drone semantic segmentation and evaluates its performance using the ICG dataset. The proposed method leverages hierarchical multi-scale feature extraction and efficient channel-based attention Atrous Spatial Pyramid Pooling (ASPP) to address the unique challenges encountered in this domain. In this study, the performance of the proposed method is compared to several state-of-the-art models. The findings of this research highlight the effectiveness of the proposed method in tackling the challenges of drone semantic segmentation. The outcomes demonstrate its superiority over the state-of-the-art models, showcasing its potential for accurate and efficient segmentation of aerial imagery. The results contribute to the advancement of drone-based applications, such as surveillance, object tracking, and environmental monitoring, where precise semantic segmentation is crucial. The obtained experimental results demonstrate that the proposed method outperforms these existing approaches regarding Dice, mIOU, and accuracy metrics. Specifically, the proposed method achieves an impressive performance with Dice, mIOU, and accuracy scores of 86.51%, 76.23%, and 91.74%, respectively. [ABSTRACT FROM AUTHOR]

Barrier islands are highly dynamic coastal landforms that are economically, ecologically, and societally important. Woody vegetation located within barrier island interiors can alter patterns of overwash, leading to periods of periodic‐barrier island retreat. Due to the interplay between island interior vegetation and patterns of barrier island migration, it is critical to better understand the factors controlling the presence of woody vegetation on barrier islands. To provide new insight into this topic, we use remote sensing data collected by LiDAR, LANDSAT, and aerial photography to measure shrub presence, coastal dune metrics, and island characteristics (e.g., beach width, island width) for an undeveloped mixed‐energy barrier island system in Virginia along the US mid‐Atlantic coast. We apply decision tree and random forest machine learning methods to identify new empirical relationships between island geomorphology and shrub presence. We find that shrubs are highly likely (90% likelihood) to be present in areas where dune elevations are above ∼1.9 m and island interior widths are greater than ∼160 m and that shrubs are unlikely (10% likelihood) to be present in areas where island interior widths are less than ∼160 m regardless of dune elevation. Our machine learning predictions are 90% accurate for the Virginia Barrier Islands, with almost half of our incorrect predictions (5% of total transects) being attributable to system hysteresis; shrubs require time to adapt to changing conditions and therefore their growth and removal lags changes in island geomorphology, which can occur more rapidly. Plain Language Summary: In this study, we present two machine learning models for predicting the presence of shrubs on barrier islands. We use data derived from satellites, LiDAR, and aerial imagery to create machine learning models. Using these models, we find that whether or not shrubs are present on barrier islands depends on dune elevation and the width of island interior sustained over time; sufficiently high dune elevations and sufficiently wide widths are necessary to support shrubs. Additionally, in certain areas, we observe a lag between predicted and observed behavior. We attribute this lag to the different time scales over which shrub and barrier island geomorphology processes operate; barrier island geomorphology can change rapidly, but it can take several years for shrubs to respond to these changes. Key Points: Decision tree analysis and random forest modeling can predict shrub presence on barrier islands in Virginia with ∼90% accuracyShrub presence on barrier islands correlates with dune elevations >1.9 m and maintenance of island interior widths >160 m over a ∼6‐year periodShrub establishment and removal lags changes in geomorphic conditions, indicating hysteresis [ABSTRACT FROM AUTHOR]

Homeowners in coastal environments often augment their access to estuarine ecosystems by building private docks on their personal property. Despite the commonality of docks, particularly in the Southeastern United States, few works have investigated their historical development, their distribution across the landscape, or the environmental justice dimensions of this distribution. In this study, we used historic aerial photography to track the abundance and size of docks across six South Carolina counties from the 1950s to 2016. Across our roughly 60-year study period, dock abundance grew by two orders of magnitude, mean length of newly constructed docks doubled, and the cumulative length of docks ballooned from 34 to 560 km. Additionally, we drew on census data interpolated into consistent 2010 tract boundaries to analyze the racial and economic distribution of docks in 1994, 1999, 2011, and 2016. Racial composition, measured as the percentage of a tract's population that was White, positively correlated with dock abundance in each year. Median household income and dock abundance were only correlated in 2011. Taken together, these metrics indicate the growing desire for direct estuary access, however, that access does not appear to be equally spread across racial groups. Because docks enhance estuarine access and demarcate private property, our study provides longitudinal insights into environmental justice concerns related to disparate private property ownership. We found a persistent correlation between the racial characteristics of an area and dock abundance, strongly indicating that White South Carolinians have had disproportionately greater private water access for the past two decades. [ABSTRACT FROM AUTHOR]

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of − 10 dB using the LSTM network and fused features. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement. [ABSTRACT FROM AUTHOR]

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on "space-sky-earth" remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have become a popular and cost-effective technology in urban scenarios, encompassing applications such as material transportation, aerial photography, remote sensing, and disaster relief. However, the execution of prolonged tasks poses a heightened challenge owing to the constrained endurance of UAVs. This paper proposes a model to accurately represent urban scenarios and an unmanned system. Restricted zones, no-fly zones, and building obstructions to the detection range are introduced to make sure the model is realistic enough. We also introduced an unmanned ground vehicle (UGV) into the model to solve the endurance of the UAVs in this long-time task scenario. The UGV and UAVs constituted a heterogeneous unmanned system to collaboratively solve the path-planning problem in the model. Building upon this model, this paper designs a Three-stage Alternating Optimization Algorithm (TAOA), involving two crucial steps of prediction and rolling optimization. A three-stage scheme is introduced to rolling optimization to effectively address the complex optimization process for the unmanned system. Finally, the TAOA was experimentally validated in both synthetic scenarios and scenarios modeled based on a real-world location to demonstrate their reliability. The experiments conducted in the synthetic scenarios aimed to assess the algorithm under hypothetical conditions, while the experiments in the scenarios based on real-world locations provided a practical evaluation of the proposed methods in more complex and authentic environments. The consistent performance observed across these experiments underscores the robustness and effectiveness of the proposed approaches, supporting their potential applicability in various real-world scenarios. [ABSTRACT FROM AUTHOR]

The western centre of France is one of the richest regions of Western Europe in terms of causewayed enclosures from the Neolithic period. To date, more than 300 such sites have been identified. Most causewayed enclosures in the region range in size from some hectares to over 10 ha. Exhaustive excavation of such sites is rarely performed because such operations are both financially expensive and time intensive. Completing an exhaustive excavation of these sites is also extremely complicated due to their complexity. Recording the most complete possible site plan is a major challenge for optimizing excavation. Traditionally, aerial photography has been the primary method used for delineating causewayed enclosure plans. A magnetic survey can also provide complementary information. Magnetic imaging reveals both enclosure ditches and internal features (pits, postholes, etc.) quickly and with high spatial resolution. At some sites, occupation layers dating from the time of enclosure may be preserved and contain archaeological artefacts or small features in situ. This article proposes a protocol for locating a Neolithic occupation layer inside a Neolithic causewayed enclosure. To locate the areas where this layer is likely to be present, a map of the archaeological potential of the Le Pontet site was produced based on a thickness map of the soil‐sedimentary cover. This map was created by combining an apparent electrical resistivity map, the results of electrical resistivity tomography, an orthophotograph with contrasting cropmarks and the results of dynamic cone penetration tests. To validate the archaeological potential map, an excavation campaign was conducted in 2020 to investigate several sectors; the aim was to prove the presence of the occupation layer and study the pedo‐sedimentary stratigraphy of the site. [ABSTRACT FROM AUTHOR]

Rainfall and land-use interactions drive temporal shifts in suspended sediment sources, yet the magnitude of such changes remains poorly understood due to the lack of land-use specific source tracers. We investigated α,ω-dicarboxylic fatty acid root-specific biomarkers, as diagnostic tracers for apportioning sources of time-integrated suspended sediment samples collected from a grassland dominated agricultural catchment in the southwest of England during the wet winter period. Applying fatty acids-specific stable carbon isotope analysis and a Bayesian isotope mixing model, we show that stream banks contributed most of the sediment in the early winter, i.e. October–December, while winter cereal-dominated arable land contributed more than half of the sediment during the late winter, i.e. January–March. The dominant sediment source shifted in conjunction with a period of prolonged consecutive rainfall days in the later period suggesting that intervention required to mitigate soil erosion and sediment delivery should adapt to changing rainfall patterns. Our novel findings demonstrate that isotopic signatures of α,ω-dicarboxylic fatty acids are promising tracers for understanding the resistance of agricultural soils to water erosion and quantifying the interactive effects of extreme rainfall and land use on catchment sediment source dynamics. [ABSTRACT FROM AUTHOR]