Woody debris (WD) is an important element in forest ecosystems. It provides critical habitats for plants, animals, and insects. also a source of fuel contributing to fire propagation sometimes leads catastrophic wildfires. WD inventory usually conducted through field surveys using transects sample plots. Light Detection Ranging (LiDAR) point clouds are emerging as valuable the development comprehensive detection strategies. Results from previous LiDAR-based approaches promising. However,...

Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, mapping yields relatively poor results in low textured areas as compared to those LiDAR. This study demonstrates the applicability of LiDAR coastal environments. A...

Objective The aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks. Background Robotic techniques provide improved dexterity, stereoscopic vision, ergonomic control system over laparoscopic surgery, but complexity interfaces operations may pose new challenges surgeons compromise patient safety. Limited studies have objectively quantified its impact on performance surgery. Although not yet implemented minimally...

Click-through rate (CTR) prediction is a crucial task in recommender systems and online advertising. The embedding-based neural networks have been proposed to learn both explicit feature interactions through shallow component deep by network (DNN) component. These sophisticated models, however, slow down the inference at least hundreds of times. To address issue significantly increased serving latency high memory usage for real-time production, this paper presents DeepLight: framework...

Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software automated process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by photogrammetric research community. In this regard, paper presents framework for triangulation UAV images. The proposed conducted three steps. first step,...

Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative conventional remote sensing platforms such as manned aircraft and satellites. In order employ this emerging technology in fields high-throughput phenotyping precision agriculture, direct georeferencing of data using onboard integrated global navigation satellite systems (GNSSs) inertial (INSs) is required. Directly deriving the scanner position orientation requires...

Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images matured the user community access to several commercial opensource tools that provide accurate products at a high level of automation. However, some agriculture, due repetitive image patterns, these approaches are...

Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user telerobotic surgery. This leverages wireless sensors monitor surgeons’ their states. Continuous data across multiple...

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution several crop phenotyping activities (e.g., canopy cover estimation, plant localization, flowering date identification). Among potential products, orthophotos play important role in agricultural management. Traditional orthophoto generation strategies suffer from artifacts...

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at reasonable cost. With recent advances in platform and technologies, uncrewed aerial vehicles (UAV) equipped with imaging Light Detection Ranging (LiDAR) systems have emerged as promising tool due low cost, ease deployment/operation, provide high-resolution geospatial data. In some cases, archaeological sites might be covered vegetation, which makes the...

Connected vehicles are poised to transform the field of environmental sensing by enabling acquisition scientific data at unprecedented scales. Drawing on a real-world dataset collected from almost 70 connected vehicles, this study generates improved rainfall estimates combining weather radar with windshield wiper observations. Existing methods for measuring precipitation subject spatial and temporal uncertainties that compromise high-precision applications like flash flood forecasting....

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, many other materials commonly used in agriculture, highways, industrial applications. Traditional approaches volumetric assessment stockpiles, e.g., truckload counting, are inaccurate prone cumulative errors over long time. Modern aerial terrestrial remote sensing platforms equipped with camera and/or light detection ranging (LiDAR) units have been...

Cross-label suppression dictionary learning is an effective approach to preserve the label property for signal representation in face recognition. This paper presents a proposed improved algorithm, considering tradeoffs between operating time and reconstruction residuals recognition problem that combines optimal loss function cross-label supervised approach. Based on relationship of cost algorithm sparse representations, this attempts select coding dimension original reduce computational...

Lane width evaluation is one of the crucial aspects in road safety inspection, especially work zones where a narrow lane can result reduced roadway capacity and also, increase probability severe accidents. Using mobile mapping systems (MMS) equipped with laser scanners safe cost-effective method for rapidly collecting detailed information along surface. This paper presents an approach to derive estimates using point clouds acquired from geometrically-calibrated system. Starting accurate...

Stockpile monitoring has been recently conducted with the help of modern remote sensing techniques – e.g., terrestrial/aerial photogrammetry/LiDAR that can efficiently produce accurate 3D models for area interest. However, indoor stockpiles still requires more investigation due to unfavorable conditions in these environments such as lack global navigation satellite system (GNSS) signals and/or homogenous texture. This study develops a fully-automated image/LiDAR integration framework is...

The need for accurate 3D spatial information is growing rapidly in many of today’s key industries, such as precision agriculture, emergency management, infrastructure monitoring, and defense. Unmanned aerial vehicles (UAVs) equipped with global navigation satellite systems/inertial systems (GNSS/INS) consumer-grade digital imaging sensors are capable providing at a relatively low cost. However, the use sensors, system calibration critical reconstruction. In this study, ‘consumer-grade’...

Unmanned aerial vehicles (UAVs) are quickly emerging as a popular platform for 3D reconstruction/modeling in various applications such precision agriculture, coastal monitoring, and emergency management. For applications, LiDAR frame cameras the two most commonly used sensors mapping of object space. example, point clouds area interest can be directly derived from onboard UAVs equipped with integrated global navigation satellite systems inertial (GNSS/INS). Imagery-based mapping, on other...

This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms acquiring data that facilitate under-canopy plant trait extraction. The system is based an unmanned ground vehicle (UGV) for in-row, acquisition to deliver accurately georeferenced 2D and 3D products. addresses three main aspects pertaining UGV development: (a) architecture (MMS), (b) quality assessment acquired georeferencing information as...

LiDAR data acquired by various platforms provide unprecedented for forest inventory and management. Among its applications, individual tree detection segmentation are critical prerequisite steps deriving structural metrics, especially at the stand level. Although there localization approaches, a comparative analysis of their performance on with different characteristics remains to be explored. In this study, new trunk-based approach (namely, height-difference-based) is proposed compared two...

Stockpile volume estimation plays a critical role in several industrial/commercial bulk material management applications. LiDAR systems are commonly used for this task. Thanks to Global Navigation Satellite System (GNSS) signal availability outdoor environments, Uncrewed Aerial Vehicles (UAV) equipped with frequently adopted the derivation of dense point clouds, which can be stockpile estimation. For indoor facilities, static scanners usually acquisition clouds from multiple locations....

A lot of image processing research works focus on natural images, such as in classification, clustering, and the recognition artworks (such oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses painter tries find mobile application recognize painter. proposes a cluster multiple kernel learning algorithm, which extracts painting features three aspects: color, texture, spatial layout, generates candidate kernels with different functions. With...