High-resolution Data Collection Research Articles

BioSense: An automated sensing node for organismal and environmental biology

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

UAV Visual and Thermographic Power Line Detection Using Deep Learning.

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Hourly step recommendations to achieve daily goals for working and older adults

BackgroundThe widespread use of physical activity trackers enables the collection of high-resolution health data, such as hourly step counts, to evaluate health promotion programmes. We aim to investigate how participants meet their daily step goals.MethodsWe used 24-h steps data from the National Steps ChallengeTM Season 3, wherein participants were rewarded with vouchers when achieving specified goals of 5000, 7500, and 10,000 steps per day. We extracted data from 3075 participants’ including a total of 52,346 participant-days. We modelled the hourly step counts using a two-part model, in which the distribution for step counts was allowed to depend on the sum of step counts up to the previous hour and participant demographics.ResultsParticipants have a mean age of 44.2 years (standard deviation = 13.9), and 40.4% are males. We show that on weekdays, the hourly mean step counts among participants aged 60 and above are higher than participants aged 30 to 59 from the start of the day till 6 p.m. We also find that participants who accumulate at least 7000 steps by 7 p.m. are associated with higher success of achieving 10,000 steps.ConclusionsWe provide recommendations on the hourly targets to achieve daily goals, based on different participants’ characteristics. Future studies could experimentally test if prompts and nudges at the recommended times of day could promote reaching step goals.

Application of remote sensing to understand the role of Galician feral horses in the biomass reduction of a shrub-grassland-dominated landscape

Galician forests in northwestern Spain are subject to frequent wildfires with high environmental and economic costs. In addition, due to the consequences of climate change, these fires are becoming more virulent, occurring throughout the year, and taking place in populated areas, in some cases involving the loss of human life. Therefore, forest fire prevention is even more relevant than mitigating its consequences. Given the costs involved in forestry work, alternative measures to reduce fuel load and create vegetation gaps are needed. One involves grazing by an endemic species of feral horses (Equus ferus atlanticus) that feed on thicket-forming gorse (Ulex europaeus). In a 100-ha forest fenced study area stocked with 11 horses, four 50 m2 enclosed plots prevented the access of these wild animals to the vegetation, with the aim of manipulating their impact on the reduction of forest biomass. The measurement of biomass volumes is an important method that can describe the assessment of wildfire risks, unfortunately, high-resolution data collection at the regional scale is very time-consuming. The best result can be using drones (unmanned aerial vehicles - UAVs) as a method of collecting remotely sensed data at low cost. From September 2018 to November 2020, we collected information about aboveground biomass from these four enclosed plots and their surrounding areas available for horses to forage, via UAV. These data, together with environmental variables from the study site, were used as input for a fire model to assess the differences in the surface rate of spread (SROS) among grazed and ungrazed areas. Our results indicated a consistent but small reduction in the SROS between 0.55 and 3.10 m/min in the ungrazed enclosured plots in comparison to their grazed surrounding areas (which have an SROS between 15 and 25 m/min). The research showed that radar remote sensing (UAV) can be used to map forest aboveground biomass, and emphasized the importance and role of feral horses in Galicia as a prevention tool against wildfires in gorse-dominated landscapes.

Geohazard features of the Tyrrhenian Calabria

ABSTRACT This paper accompanies the Maps of Geohazard features of the Cilento and the Calabro-Tyrrhenian continental margin in the southern Tyrrhenian Sea (Italy). The main geohazard-related features were derived from extensive seafloor mapping through the collection of high-resolution multibeam data acquired during several oceanographic cruises. They encompass many fluids seepage features, fault scarps, landslides scars, gullies, channels, and canyons. Hazards related to coastal landslides and shelf-indenting canyons are very high in these sectors (especially in southern Calabria) due to active seismicity coupled with rapid uplift, high sedimentation rates and narrow or totally absent continental shelf, thus promoting a direct connection between steep slopes and coastal areas. In this setting, mass-wasting features can directly impact coastal or submarine infrastructures or indirectly create local tsunami waves, as observed in historical times. Moreover, this physiographic setting of the margin facilitates the transfer of marine litter toward deep-sea areas.

From solution to structure: empowering inclusive cryo-EM with a pre-characterization pipeline for biological samples.

In addressing the challenges faced by laboratories and universities with limited (or no) cryo-electron microscopy (cryo-EM) infrastructure, the ESRF, in collaboration with the Grenoble Institute for Structural Biology (IBS), has implemented the cryo-EM Solution-to-Structure (SOS) pipeline. This inclusive process, spanning grid preparation to high-resolution data collection, covers single-particle analysis and cryo-electron tomography (cryo-ET). Accessible through a rolling access route, proposals undergo scientific merit and technical feasibility evaluations. Stringent feasibility criteria demand robust evidence of sample homogeneity. Two distinct entry points are offered: users can either submit purified protein samples for comprehensive processing or initiate the pipeline with already vitrified cryo-EM grids. The SOS pipeline integrates negative stain imaging (exclusive to protein samples) as a first quality step, followed by cryo-EM grid preparation, grid screening and preliminary data collection for single-particle analysis, or only the first two steps for cryo-ET. In both cases, if the screening steps are successfully completed, high-resolution data collection will be carried out using a Titan Krios microscope equipped with a latest-generation direct electron counting detector coupled to an energy filter. The SOS pipeline thus emerges as a comprehensive and efficient solution, further democratizing access to cryo-EM research.

Demonstrating the relevance of spatial-functional statistical analysis in marine ecological studies: The case of environmental variations in micronektonic layers

In this study, we conducted an analysis of a multifrequency acoustics dataset acquired from scientific echosounders in the West African water. Our objective was to explore the spatial arrangement of marine organism aggregations. We investigated various attributes of these intricate biological entities, such as thickness, relative density, and depth, in relation to their surroundings. These environmental conditions were represented at a fine scale using a towed multiparameter system. This study is closely intertwined with two key domains: Fisheries acoustics techniques and functional data analysis. Fisheries acoustics techniques facilitate the collection of high-resolution spatial and temporal data concerning marine organisms at various depths and spatial scales, all without causing any disturbance. On the other hand, spatial-functional data analysis is a statistical approach for examining data characterised by functional attributes distributed across a spatial domain. This analysis encompasses dimension reduction techniques, as well as supervised and unsupervised methods, which take into consideration spatial dependencies within extensive datasets.We began by applying multivariate statistical techniques and subsequently employed Functional Data Analysis (FDA). In the modeling section, we introduced the spatial dimension with the spatial coordinates as covariates in the General Additive Model (GAM) and Functional Generalized Spectral Additive Model (FGSAM) models, aiming to underscore its relevance in those contexts. In an exploratory phase, Multivariate Functional Principal Component Analysis provided detailed insights into the variations of parameters at different depths, a capability not offered by traditional Principal Component Analysis. When it came to regression tasks, we explored the interactions between descriptors of Sound Scattering Layers and key environmental variables, both with and without considering spatial dimensions. Our findings revealed significant distinctions between northern and southern Sound Scattering Layers, as well as between coastal and high-sea regions. The use of the spatial locations enhanced the performance of GAM and FGSAM, particularly in the case of salinity, reflecting the influence of water mixing and seawater temperature. The multifaceted effects of environmental variations on Sound Scattering Layers underscore the importance of spatial-functional statistical analysis in ecological studies involving complex, spatially functional objects. Beyond the scope of this specific case study, the application of functional data analysis shows promise for a wide array of ecological studies dealing with extensive spatial datasets.

Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS)

Abstract This article discusses the use of a deep learning neural network (DLNN) as a tool to improve maritime safety by classifying the potential threat to shipping posed by unexploded ordnance (UXO) objects. Unexploded ordnance poses a huge threat to maritime users, which is why navies and non-governmental organisations (NGOs) around the world are using dedicated advanced technologies to counter this threat. The measures taken by navies include mine countermeasure units (MCMVs) and mine-hunting technology, which relies on the use of sonar imagery to detect and classify dangerous objects. The modern mine-hunting technique is generally divided into three stages: detection and classification, identification, and neutralisation/disposal. The detection and classification stage is usually carried out using sonar mounted on the hull of a ship or on an underwater vehicle. There is now a strong trend to intensify the use of more advanced technologies, such as synthetic aperture sonar (SAS) for high-resolution data collection. Once the sonar data has been collected, military personnel examine the images of the seabed to detect targets and classify them as mine-like objects (MILCO) or non mine-like objects (NON-MILCO). Computer-aided detection (CAD), computer-aided classification (CAC) and automatic target recognition (ATR) algorithms have been introduced to reduce the burden on the technical operator and reduce post-mission analysis time. This article describes a target classification solution using a DLNN-based approach that can significantly reduce the time required for post-mission data analysis during underwater reconnaissance operations.

Spatial patterns of PM2.5 air pollution in Jakarta: Insights from mobile monitoring

A mobile air quality monitoring study in Jakarta was undertaken in 2022 by the Clean Air Catalyst consortium, in partnership with Google Earth Outreach and Aclima, taking air pollution measurements using four specially equipped Google Street View cars. This study aims to map street-level PM2.5 pollution in a set of neighborhoods (polygons) throughout Jakarta to evaluate the variability caused by local emission sources and to observe the exposure to air pollution in areas with different income levels. To address these aims, we monitored the concentration five days a week, 8 am to 5 pm, from September to December in eight polygons, stratified to include high and low-income areas. The cars covered a distance of 7,750 km and at least 22 visits per location. PM2.5 concentrations were measured at 1-second intervals using a light-scattering sensor. This high-resolution data collection allowed for detailed monitoring and analysis of concentrations in the study area. The measurements showed temporal variability consistent with meteorological and activity patterns in Jakarta, with higher measured concentrations in the morning compared to the afternoon at most polygons. Median concentrations varied by 2x - >5x within individual polygons, and this variability was particularly evident in the northern part of Jakarta, encompassing both high-income and low-income neighborhoods, where these areas are significantly affected by traffic and industrial activities. The results demonstrate the extent to which PM2.5 concentrations and exposures within Jakarta neighborhoods depend on proximity to local air pollution sources.

Comparison of TLS and SLAM technologies for 3D reconstruction of objects with different geometries

Technological advances have made the 3D mapping process easily available and simpler. However, there are still aspects that need to be improved and enhanced. The efficient acquisition of 3D data and reconstruction of objects with high accuracy continues to be a challenge for the scientific community. One of the most frequently used 3D mapping methods is Terrestrial Laser Scanning (TLS), which allows the collection of high-resolution and precise data. Another method gaining popularity among researchers and professionals is mobile scanning technology, which enables real-time data capture. Its mobility and speed make it an effective alternative to traditional scanning technologies. This article compares two mapping technologies: SLAM (Simultaneous Localization and Mapping) and TLS taking into account the technical aspects of the instruments, processing methods, time and cost, and concluding with an assessment of the final accuracy. The geometry of several selected objects was analyzed. The resulting root mean square error (RMSE) for the compared distances on the two point clouds was 5 cm, which proves that the SLAM technology can be successfully applied for scenarios requiring centimeter-level accuracy.

Cryo-Electron Microscopy Screening Automation across Multiple Grids using Smart Leginon.

Advancements in cryo-electron microscopy (cryoEM) techniques over the past decade have allowed structural biologists to routinely resolve macromolecular protein complexes to near-atomic resolution. The general workflow of the entire cryoEM pipeline involves iterating between sample preparation, cryoEM grid preparation, and sample/grid screening before moving on to high-resolution data collection. Iterating between sample/grid preparation and screening is typically a major bottleneck for researchers, as every iterative experiment must optimize for sample concentration, buffer conditions, grid material, grid hole size, ice thickness, and protein particle behavior in the ice, amongst other variables. Furthermore, once these variables are satisfactorily determined, grids prepared under identical conditions vary widely in whether they are ready for data collection, so additional screening sessions prior to selecting optimal grids for high-resolution data collection are recommended. This sample/grid preparation and screening process often consumes several dozen grids and days of operator time at the microscope. Furthermore, the screening process is limited to operator/microscope availability and microscope accessibility. Here, we demonstrate how to use Leginon and Smart Leginon Autoscreen to automate the majority of cryoEM grid screening. Autoscreen combines machine learning, computer vision algorithms, and microscope-handling algorithms to remove the need for constant manual operator input. Autoscreen can autonomously load and image grids with multi-scale imaging using an automated specimen-exchange cassette system, resulting in unattended grid screening for an entire cassette. As a result, operator time for screening 12 grids may be reduced to ~10 min with Autoscreen compared to ~6 h using previous methods which are hampered by their inability to account for high variability between grids. This protocol first introduces basic Leginon setup and functionality, then demonstrates Autoscreen functionality step-by-step from the creation of a template session to the end of a 12-grid automated screening session.

The role of core in twenty-first century reservoir characterization: an introduction

Abstract The first two decades of the twenty-first century have seen significant advances across a wide range of reservoir characterization techniques, from microscale digital rock physics to macroscale 3D and 4D seismic. At the same time, industry downturns and the requirements of the energy transition have demanded improved understanding of the value and impact of subsurface data to justify their acquisition and commercial relevance. Despite changing technologies and demands, the acquisition, description and analysis of core remains a fundamental tool in managing subsurface uncertainty and associated risk. Value continues to be created in relation to the reservoir property, sedimentological, diagenetic and structural characterization of subsurface reservoirs, and these are the focus of the Core Values volume. The enduring business impact of core reflects advances in acquisition methods and laboratory-based core analysis (Theme 1 of the volume); the recent development of multi-sensor core scanning and associated artificial intelligence (AI) tools that allow unprecedented high-resolution data collection and visualization (Theme 2); the integration of core-derived data with new complementary technologies, leading to improved characterization of both cored and uncored intervals (Theme 3); the changing nature and role of legacy core collections due to digitization and improved data access (Theme 4). These are complemented by the need to better understand both existing hydrocarbon resources and other subsurface energy-related systems, particularly CCUS (carbon capture, utilization and storage), geothermal energy and the long-term storage of nuclear waste (Theme 5). Through the energy transition core will remain the ground truth foundation to any subsurface understanding and evaluation. At the same time, the technologies available to maximize the applied value of core will continue to develop and evolve, with the integration of diverse and complex core-derived and core-related datasets becoming the norm. Even in the face of AI's impact and value in handling such datasets, those earth scientists who can effectively analyse, interpret and integrate core will still be best placed to meet the subsurface challenges of the future.

Research on the Application Status of UAV in Agriculture

UAVs are widely used in agriculture. Through high-resolution image and data collection, they help farmers carry out crop monitoring and evaluation, precision agriculture, plant protection and crop spraying. However, there are still challenges such as high cost, low degree of agricultural mechanization, laws and regulations and privacy protection. Future development trends include cost reduction, improvement of data processing and analysis technology, integration with other agricultural technologies, autonomous flight and intelligent technology. The application potential of UAV is huge, which can improve agricultural efficiency and quality, and achieve sustainable development. UAV technology should be popularized and applied, and relevant problems should be solved.

Modular Open-Core System for Collection and Near Real-Time Processing of High-Resolution Data from Wearable Sensors

Wearable devices, such as smartwatches integrating heart rate and activity sensors, have the potential to transform health monitoring by enabling continuous, near real-time data collection and analytics. In this paper, we present a novel modular architecture for collecting and end-to-end processing of high-resolution signals from wearable sensors. The system obtains minimally processed data directly from the smartwatch and further processes and analyzes the data stream without transmitting it to the device vendor cloud. The standalone operation is made possible by a software stack that provides data cleaning, extraction of physiological metrics, and standardization of the metrics to enable person-to-person and rest-to-activity comparisons. To illustrate the operation of the system, we present examples of datasets from volunteers wearing Garmin Fenix smartwatches for several weeks in free-living conditions. As collected, the datasets contain time series of each interbeat interval and the respiration rate, blood oxygen saturation, and step count every 1 min. From the high-resolution datasets, we extract heart rate variability metrics, which are a source of information about the heart’s response to external stressors. These biomarkers can be used for the early detection of a range of diseases and the assessment of physical and mental performance of the individual. The data collection and analytics system has the potential to broaden the use of smartwatches in continuous near to real-time monitoring of health and well-being.

High-resolution powder diffraction data collection on beamline ID22 at ESRF

High-resolution powder diffraction data collection on beamline ID22 at ESRF

Application of UAV for Village Mapping in Pangambatan, Sumatra Utara

Pangambatan Village, as part of the Lake Toba National Strategic Area and a tourist destination, still lacks facilities, infrastructure, and professional human resources to manage its cultural and tourism potential. An initial mapping of the conditions and potential of the area is needed to drive tourism in Pangambatan Village towards sustainable tourism. This can be done by mapping environmental conditions and tourism potential in Pangambatan Village, providing recommendations for sustainable tourism management, and providing environmental management facilities. The study was carried out using Unmanned Aerial Vehicle (UAV) technology. UAV technology offers new opportunities for fast and efficient remote environmental monitoring and the collection of high spatial and temporal resolution data. The benefits of this mapping can be used to see environmental conditions and environmental pollution that may occur because of tourist activities. In addition, the use of this technology is expected to help related parties in making decisions and planning.

A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research.

Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2-5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.

Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development.

Mapping the eco-evolutionary factors shaping the development of animals' behavioural phenotypes remains a great challenge. Recent advances in 'big behavioural data' research-the high-resolution tracking of individuals and the harnessing of that data with powerful analytical tools-have vastly improved our ability to measure and model developing behavioural phenotypes. Applied to the study of behavioural ontogeny, the unfolding of whole behavioural repertoires can be mapped in unprecedented detail with relative ease. This overcomes long-standing experimental bottlenecks and heralds a surge of studies that more finely define and explore behavioural-experiential trajectories across development. In this review, we first provide a brief guide to state-of-the-art approaches that allow the collection and analysis of high-resolution behavioural data across development. We then outline how such approaches can be used to address key issues regarding the ecological and evolutionary factors shaping behavioural development: developmental feedbacks between behaviour and underlying states, early life effects and behavioural transitions, and information integration across development.

Fully automated multi-grid cryoEM screening using Smart Leginon.

Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.

3D mapping and photogrammetry sensor payload for unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) have shown great potential for data collection and monitoring of areas. Sensors such as LiDARs and cameras can be used on UAVs for high-resolution data collection and used for various applications like Digital Surface Models (DSM), photogrammetry, inspection systems, and maintenance applications. This paper describes the design and implementation of a sensor payload for UAVs using a LiDAR sensor, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). The payload collects data that can be used to georeference LiDAR and camera data, which can later be used to generate a georeferenced map and perform object detection and classification.