Timely estimations of magma volumes emitted during an eruption or a sequence of explosive events are vital for investigating the eruptive activity and evaluating the associated hazard. A reliable ...method for estimating erupted volumes is based on the analysis of digital surface models that nowadays can be obtained subsequently using stereo or tri‐stereo optical satellite imagery. However, the real‐time estimation of the erupted volumes is still an open challenge. Here, we explore the capacity of extracting volume estimates from continuous measurements of volumetric strain changes recorded by borehole dilatometers. We compare the volumes derived from numerous high spatial resolution satellite images with high precision strain records at Etna during 2020–2022, when more than 60 lava fountains occurred. The good correlation between the two data sets shows that strain changes can be used as a proxy to estimate the emitted volumes both over time and in real‐time.
Plain Language Summary
Quantifying erupted volumes is fundamental in volcanology to provide a robust characterization of eruptions. To date, estimates of erupted volumes have been calculated after the eruptions have ended and the real‐time estimation of the erupted volumes is still an on‐going challenge. In recent decades, the sequences of lava fountain‐type eruptions at Etna, with dozens of episodes close in time and more than 100 episodes occurring since 2011, has made the task of estimating the volumes in real‐time during each single eruption ever more essential. This would enable providing precise information to Civil Protection authorities and contribute toward hazard evaluation. In this study, we took on this new challenge by exploring the potential to extract erupted volume estimates from continuous measurements of strain changes, recorded by high‐precision borehole instruments installed on the volcano's flanks. We compared and validated the volumes deriving from numerous high spatial resolution satellite images with high‐precision strain records at Etna during 2020–2022, when more than 60 lava fountains occurred. The good correlation between the two data sets shows that strain changes can be used effectively as a proxy to estimate the emitted volumes, both over time and, more importantly, in real‐time.
Key Points
Detection of strain changes during sequences of lava fountain eruptions from continuous recording high‐precision borehole instruments
Quantifying erupted volumes from strain changes, comparison and validation with the volumes calculated from high spatial resolution satellite measurements
New method to estimate the erupted volumes in real‐time by using the continuous strain recording
The share of the annual volume of harvester-produced timber in Czech forest bioeconomy has increased in the last decades. To estimate under-bark timber volume, harvester systems allow choosing ...between two different bark deduction models – diameter band (DBM) and linear model. However, linear models were not calibrated for the conditions of Czech forestry. Therefore, the objective of this research was to develop, for local conditions in Czechia, linear functions for estimating the double bark thickness of two groups of broadleaved species (beech and oak) and to test their viability based on real harvest data. To create the linear functions, official Czech cubing tables were used. Data from real harvests were gathered from fifteen harvesters. A sample containing 4995 logs belonging to the beech group was analyzed using descriptive statistics and the Paired Wilcoxon tests. The mean double bark thickness for beech group was 15.1 mm (polynomial and linear model). For oak group, it was 15.48 mm (polynomial) or 15.49 mm (linear). The results of real harvests for beech group revealed that the mean double bark thickness estimated by the polynomial function was 7.08 mm. The linear function estimates were closer to the value estimated by the polynomial (6.84 mm) than DBM estimates (6.68 mm). Therefore, we can state that the newly developed linear models can be used in fully mechanized harvesting instead of manual bark deduction methods in Czechia.
Vehicle counting and traffic volume estimation using videos are difficult tasks crucial for efficient traffic control in smart cities. Several existing techniques rely in tracking and detecting ...mechanisms of the vehicles. These methods are ineffective for detecting occluded and small vehicles. Also, contextual information loss occurs in deep counting networks. An innovative Internet-of-Things (IoT)- driven Intelligent Transportation Management (ITM) system is proposed to address these issues. Initially, traffic videos are converted to temporal-spatial images instead of using complex detection and tracking methods. The density map for the temporal spatial image is estimated using an occlusion-aware spatio-temporal multi-scale network (OSTM-Net). It consists of two sub-networks for capturing occluded and small vehicles simultaneously. The scale-aware column network (SCNet) accurately captures small vehicles and preserves contextual information through enhanced scale representation. At the same time, the occlusion management network (OM-Net) uses position-sensitive regions of interest (PSRoI) deformable pooling to address the occlusion issues. Finally, volume estimation and counting are calculated in accordance with the density map obtained from OSTM-Net. Every path in the videos is processed separately using OSTM-Net to calculate the vehicle count in every path for effective traffic control in this proposed approach. Furthermore, the effectiveness of the sub-networks (SCNet and OM-Net) is validated using ablation experiments. The proposed IoT-based ITM achieves high performance in counting vehicles and estimating traffic volume compared to other existing approaches.
Excavators are crucial in the construction industry, and developing autonomous excavator systems is vital for enhancing productivity and reducing the reliance on manual labor. Accurate estimation of ...the volume of the excavator bucket fill is key for monitoring and evaluating system automation performance. This paper presents the use of 2D depth maps as input to a Faster Region Convolutional Neural Network (Faster R-CNN) deep learning model for bucket volume estimation. This structure enables high estimation accuracy while maintaining fast processing speed. An excavator operation monitoring test bench was established, and the datasets used in the study were self-generated for training. A loss function is proposed, combining Cross Entropy with Root Mean Squared Error to improve generalization and precision. Comparative results indicate that the proposed approach achieves 96.91% accuracy in fill factor estimation and predicts in real-time at about 10 fps, highlighting its potential for practical use in automated excavator operations.
•Tailored Faster R-CNN for accurate real-time excavator bucket fill estimation.•Use of 2D depth maps enhances processing speed without compromising accuracy.•Development of diverse image datasets matching excavator loading conditions.•Custom loss function integrates Cross Entropy with MSE, enhancing accuracy in bucket fill estimation.•Achieving 96.91% accuracy in fill estimation test, proving its benefits in automated monitoring.
•A spatiotemporal matrix completion model for network-wide traffic flow estimation.•The proposed model is formulated as a quadratic programming and solved by ADMM.•A spatial smoothing index based on ...the divergence is developed to measure the difficulty of estimation.•Both real-world and synthetic datasets to evaluate algorithm performances and acquire insights.
With the rapid development of urbanization and modernization, it is increasingly crucial to sense network-wide traffic. Network-wide traffic volume information is of great benefit for traffic planning, government management and vehicle emissions control. However, it is difficult to install detectors on every intersection due to the expensive deployment and maintenance costs, and the insufficient sensor coverage across the network limits the direct availability of network-wide traffic flow information. Whereas, crowdsourcing floating car data with a high coverage rate are currently available, which creates an opportunity to address this problem. In this paper, we propose a novel methodology to estimate network-wide traffic flow, which incorporates flow records and crowdsourcing floating car data into a geometric matrix completion model. Furthermore, a spatial smoothing index based on the divergence is developed to measure the difficulty of volume estimation for each road segment. We conduct extensive experiments on both real-world and synthetic datasets. The results demonstrate that our approach consistently outperforms other benchmark models and that the proposed index is highly correlated to estimation accuracy.
Non-invasive estimation of bladder volume could help patients with impaired bladder volume sensation to determine the right moment for catheterisation. Continuous, non-invasive impedance measurement ...is a promising technology in this scenario, although influences of body posture and unknown urine conductivity limit wide clinical use today. We studied impedance changes related to bladder volume by simulation, in-vitro and in-vivo measurements with pigs. In this work, we present a method to reduce the influence of urine conductivity to cystovolumetry and bring bioimpedance cystovolumetry closer to a clinical application.
Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity ...that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip's trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.
In recent years, automation of construction and civil engineering industry sites is highly demanded. In particular, ``excavation and loading'' is an essential task that is achieved with a ...collaborative work between backhoes and dump trucks. For the automation of the work, backhoes need information of soil condition inside the truck bed such as volume, shape and position of the soil loaded in the bed. For example, overloaded soil causes collapse from the truck bed while moving. That is why these kinds of information is necessary for backhoes to achieve the loading task efficiently. In this paper, we propose a method to measure soil in the truck bed while the loading work to obtain the volume and the shape of loaded soil and to locate the position to load next. Especially, in order to measure the inside of the truck bed with high accuracy, we constructed a system to measure the loaded soil directly from a backhoe. We measure inside truck bed from multiple views while loading by mounting multiple sensors on the arm and on the cabin of the machinery. The performance of the proposed system is validated in an experiment with actual construction machineries at a test field.
Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant ...architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.
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