Rock pillars shaped by columnar joints in granite at Mt. Mizugaki, Central Japan

Recent advances in structure from motion (SfM) and dense matching algorithms enable surface reconstruction from unmanned aerial vehicle (UAV) images with high spatial resolution, allowing for new insights into earth surface processes. However, accuracy issues are inherent in parallel‐axes UAV image configurations. In this study, the quality of digital elevation models (DEMs) is assessed using images from a simulated UAV flight. Five different SfM tools and three different cameras are compared. If ground control points (GCPs) are not integrated into the adjustment process with parallel‐axes image configurations, significant dome‐effect systematic errors are observed, which can be reduced based on calibration parameters retrieved from a testfield captured with convergent images immediately before or after the UAV flight. A comparison between DEMs of a soil surface generated from UAV images and terrestrial laser‐scanning data show that natural surfaces can be very accurately reconstructed from UAV images, even when GCPs are missing and simple geometric camera models are considered.

Structure-from-motion (SfM) and multi-view stereo (MVS) algorithms coupled with the use of unmanned aerial vehicles (UAVs) have become a popular tool in the geosciences for modelling complex landscapes on-demand allowing for high-resolution topographic change-detection studies to be conducted at minimal cost. To identify the effects of UAV image orientation on the accuracy of SfM-MVS 3D surface models, we tested four different UAV image acquisition schemes that incorporated both nadir and oblique imagery of an agricultural field. The coupling of nadir and oblique imaging angles led to the highest surface model accuracy in the absence of ground control points (GCPs; vertical RMSE: 0.047 m, horizontal RMSE: 0.019 m), while with a normative distribution of GCPs the nadir-only image sets had similar accuracy metrics (vertical RMSE 0.028 m, horizontal RMSE 0.017 m) to surface models generated with nadir and oblique imaging angles (vertical RMSE 0.028 m, horizontal RMSE 0.013 m). Homologous keypoint matching between nadir and oblique imagery was poor when the survey conditions were bright and the surface texture of the field was homogeneous, leading to broad-scale vertical noise in the generated surface models. Results indicate that a nadir-only image set accompanied with a dense deployment of GCPs is the most ideal for SfM-MVS agricultural 3D surface reconstructions. The diachronic analysis of surface models generated from nadir-only image sets were able to detect surface-change >0.040 m in depth (i.e., rill and gully erosion, depositional zones) and were comparable to results obtained from a terrestrial laser scanner. Stable GCPs should be used where possible to ensure precise co-registration between subsequent UAV surveys.

The use of unmanned aerial vehicles (UAVs) for natural resource applications has increased considerably in recent years due to their greater availability, the miniaturization of sensors, and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory, assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semiautomated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between imageand ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace, the results of this study show that UAVs can be used successfully to obtain imagery for rangeland monitoring, and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the UAV mission, image processing and analysis, and accuracy assessment.

The use of unmanned aerial vehicles (UAVs) is becoming increasingly widespread in many areas of human life every year. Not only military, but also civilian use of UAVs in various fields of science, industry, forestry, agronomy, cartography, ecology, and space research is hard to overestimate. For this reason, the use and development of UAVs in various fields remains extremely promising in the near future. Compared to manned vehicles, UAVs have the following advantages: no crew and no need for life support systems, large airfields; lower cost and low expenses for their development, production and operation; better weight and dimensions; high reliability and maneuverability; a wide range of specialized equipment for placement on board. UAVs are being introduced into all spheres of human life, even those where UAVs have no manned alternative. Some models of multicopters are already being used to deliver goods and extinguish fires. UAVs of new designs continue to be developed.In all areas of UAV application, the task of increasing autonomy remains relevant and important. The duration of con-tinuous operation and flight range directly affect the capabilities of the UAV, its ability to perform tasks and efficiency.UAV power consumption depends on the weight of the payload, weather conditions and drone speed. Energy consumption is a critical criterion for a drone to perform its missions and needs to be further studied. Many studies have been conducted to increase flight time by improving energy efficiency or reducing the energy consumed by the drone.Study the influence of battery parameters (capacity and weight) and propeller group (number of propellers and their diameter) on the maximum possible flight duration of UAVs of different masses.The main factors affecting the maximum flight duration of a UAV are considered. A model for calculating the power consumed by the device in the hover mode is presented. The maximum flight duration of a UAV is calculated depending on the battery capacity and weight, the number of propellers, and the diameter of the propellers for vehicles of different masses.It is shown that there is no point in increasing the battery capacity indefinitely, and there is an optimum point that depends on the ratio of the battery mass to the mass of the vehicle. It is also shown that increasing the diameter of the propellers significantly increases the flight time, while increasing their number is not as effective.

Investigating post-earthquake surface ruptures is important for understanding the tectonics of seismogenic faults. The use of unmanned aerial vehicle (UAV) images to identify post-earthquake surface ruptures has the advantages of low cost, fast data acquisition, and high data processing efficiency. With the rapid development of deep learning in recent years, researchers have begun using it for image crack detection. However, due to the complex background and diverse characteristics of the surface ruptures, it remains challenging to quickly train an effective automatic earthquake surface rupture recognition model on a limited number of samples. This study proposes a workflow that applies an image segmentation algorithm based on convolutional neural networks (CNNs) to extract cracks from post-earthquake UAV images. We selected the 16-layer visual geometry group (VGG16) network as the primary network architecture. Then, we improved the VGG16 network and deleted several convolutional layers to reduce computation and memory consumption. Moreover, we added dilated convolution and atrous spatial pyramid pooling (ASPP) to make the network perform well in the surface crack identification of post-earthquake UAV images. We trained the proposed method using the data of the MS 7.4 Maduo earthquake and obtained a model that could automatically identify and draw small and irregular surface ruptures from high-resolution UAV images.

In order to ensure the safety of transmission lines, the use of unmanned aerial vehicle (UAV) images for automatic object detection has important application prospects, such as the detection of birds’ nests. The traditional bird’s nest detection methods mainly include the study of morphological characteristics of the bird’s nest. These methods have poor applicability and low accuracy. In this work, we propose a deep learning-based birds’ nests automatic detection framework—region of interest (ROI) mining faster region-based convolutional neural networks (RCNN). First, the prior dimensions of anchors are obtained by using k-means clustering to improve the accuracy of coordinate boxes generation. Second, in order to balance the number of foreground and background samples in the training process, the focal loss function is introduced in the region proposal network (RPN) classification stage. Finally, the ROI mining module is added to solve the class imbalance problem in the classification stage, combined with the characteristics of difficult-to-classify bird’s nest samples in the UAV images. After parameter optimization and experimental verification, the deep learning-based bird’s nest automatic detection framework proposed in this work achieves high detection accuracy. In addition, the mean average precision (mAP) and formula 1 (F1) score of the proposed method are higher than the original faster RCNN and cascade RCNN. Our comparative analysis verifies the effectiveness of the proposed method.

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resources.

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation.

Tea leaf blight (TLB) is a common disease of tea plants and is widely distributed in tea gardens. Although the use of unmanned aerial vehicle (UAV) remote sensing can help to achieve a wider scale for TLB detection, the blurring of UAV images, overlapping of tea leaves, and small size of TLB spots pose significant challenges to the task of detection. This study proposes a method of detecting TLB in UAV remote sensing images by integrating super-resolution (SR) and detection networks. We use an SR network called SERB-Swin2sr to reconstruct the detailed features of UAV images and solve the problem of detail loss caused by the blurring in UAV images. In SERB-Swin2sr, a squeeze-and-excitation ResNet block (SERB) is introduced to enhance the models' ability to extract the target details in the images, and the convolution stem replaces the convolution block in order to increase the convergence rate and stability of the network. A detection network called SDDA-YOLO is applied to achieve precise detection of TLB in UAV remote sensing images. In SDDA-YOLO, a shuffle dual-dimensional attention (SDDA) module is introduced to enhance the feature fusion capability of the network, and an Xsmall-scale detection layer is used to enhance the detection ability of small lesions. Experimental results show that the proposed method is superior to current detection methods. Compared with a baseline YOLOv8 model, the precision, mAP@0.5, and mAP@0.5:0.95 of the proposed method are improved by 4.2%, 1.6%, and 1.8%, and the size of our model is only 4.6MB.

Abstract. The rapid development of unmanned aerial vehicles is a challenge for applied research. Many technologies are developed and then researcher are looking up for their application in different sectors. Therefore, we decided to verify the use of the unmanned aerial vehicle for wood chips pile monitoring. We compared the use of GNSS device and unmanned aerial vehicle for volume estimation of four wood chips piles. We used DJI Phantom 3 Professional with the built-in camera and GNSS device (geoexplorer 6000). We used Agisoft photoscan for processing photos and ArcGIS for processing points. Volumes calculated from pictures were not statistically significantly different from amounts calculated from GNSS data and high correlation between them was found (p = 0.9993). We conclude that the use of unmanned aerial vehicle instead of the GNSS device does not lead to significantly different results. Tthe data collection consumed from almost 12 to 20 times less time with the use of UAV. Additionally, UAV provides documentation trough orthomosaic.

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

Bridges are an important part of railway infrastructure and need regular inspection and maintenance. Using unmanned aerial vehicle (UAV) technology to inspect railway infrastructure is an active research issue. However, due to the large size of UAV images, flight distance, and height changes, the object scale changes dramatically. At the same time, the elements of interest in railway bridges, such as bolts and corrosion, are small and dense objects, and the sample data set is seriously unbalanced, posing great challenges to the accurate detection of defects. In this paper, an adaptive cropping shallow attention network (ACSANet) is proposed, which includes an adaptive cropping strategy for large UAV images and a shallow attention network for small object detection in limited samples. To enhance the accuracy and generalization of the model, the shallow attention network model integrates a coordinate attention (CA) mechanism module and an alpha intersection over union (α-IOU) loss function, and then carries out defect detection on the bolts, steel surfaces, and railings of railway bridges. The test results show that the ACSANet model outperforms the YOLOv5s model using adaptive cropping strategy in terms of the total mAP (an evaluation index) and missing bolt mAP by 5% and 30%, respectively. Also, compared with the YOLOv5s model that adopts the common cropping strategy, the total mAP and missing bolt mAP are improved by 10% and 60%, respectively. Compared with the YOLOv5s model without any cropping strategy, the total mAP and missing bolt mAP are improved by 40% and 67%, respectively.

The use of unmanned aerial vehicles (UAVs) in the construction industry is swiftly growing worldwide. UAVs are changing the way construction companies do business. Contractors are increasingly using camera-mounted UAVs to monitor the full range of activities. Rapid advances in camera, sensing, aeronautics, battery and autopilot navigation technologies have helped make UAVs affordable, reliable and easy to operate. The US Federal Aviation Administration predicts that UAVs hold tremendous promise and commercial drone sales could increase from 600,000 to 2.7 million over the next four years. On the one hand, UAVs are extremely cost-effective in conducting aerial surveys, worksite surveillance, real-time inspections, and monitoring jobsite safety. On the other hand, with UAV ownership rapidly rising, the number of reported UAV safety, risk, and liability incidents is increasing. With this new technology use in the construction industry, construction companies must be aware of all regulations, legislations, privacy liability, and risks for construction related businesses. Operating a drone or hiring a subcontractor to operate it is not something to do on the spur of the moment. Emphasis and effort should be placed on safety, risk control, training and education. The goal of this paper is to provide construction professionals with timely and pertinent information on UAV use with a focus on risk management based on current industry practice, experience and literature review.

Modelling farmland topography for suitable site selection of dam construction using unmanned aerial vehicle (UAV) photogrammetry

This paper presents a novel automated road damage detection approach using Unmanned Aerial Vehicle (UAV) images and deep learning techniques. Maintaining road infrastructure is critical for ensuring a safe and sustainable transportation system. However, the manual collection of road damage data can be labor-intensive and unsafe for humans. Therefore, we propose using UAVs and Artificial Intelligence (AI) technologies to improve road damage detection’s efficiency and accuracy significantly. Our proposed approach utilizes three algorithms, YOLOv4, YOLOv5, and YOLOv7, for object detection and localization in UAV images. We trained and tested these algorithms using a combination of the RDD2022 dataset from China and a Spanish road dataset. The experimental results demonstrate that our approach is efficient and achieves 59.9% mean average precision mAP@.5 for the YOLOv5 version, 65.70% mAP@.5 for a YOLOv5 model with a Transformer Prediction Head, and 73.20% mAP@.5 for the YOLOv7 version. These results demonstrate the potential of using UAVs and deep learning for automated road damage detection and pave the way for future research in this field.