Inaccessible Areas Research Articles

Accuracy assessment of post‐processing kinematic georeferencing based on uncrewed aerial vehicle‐based structures from motion multi‐view stereo photogrammetry

AbstractWe verified a method of acquiring digital surface models of steep, forested, small watershed topography, where real‐time kinematic processing is difficult because of the presence of interfering objects. Our approach involved using an uncrewed aerial vehicle with a built‐in global navigation satellite system, which reduces time and labour costs. We tested the applicability of structure‐from‐motion multi‐view stereo processing, and post‐processing motion corrections of positional coordinate data were also tested. Nine verification points were established in a small 0.5 km2 watershed, with check dams established in the headwaters of the forested area. The position accuracy and overall working time of verification points extracted by the method were compared with the position accuracy and work time obtained by a field survey using a conventional total station. The results show that the vertical error between the total station and each verification point at an altitude of 150 m ranged from 0.006 to 0.181 m. The working time of the survey was 13% of that of the total station survey. The proposed workflow shows that safe and non‐destructive topographic surveying, including fluvial geomorphological mapping, is possible with a vertical error of ±0.103 m in small watersheds. This method will be useful for rapid topographic surveying in inaccessible areas during disasters, namely, debris flow monitoring at check dam sites and efficient topographic mapping of steep valleys in forested areas where positioning ground control points is laborious. It should also be of widespread interest to geographers working with spatial challenges related to land management.

Methods of Measuring Air Pollution in Cities and Correlation of Air Pollutant Concentrations

The monitoring of air quality continues to be one of the most important tasks when ensuring the safety of our environment. This paper aims to look at correlations between different types of pollutants, so that robust air quality measurement systems can be deployed in remote, inaccessible areas, at a reduced cost. The first matter at hand was to design an affordable and portable system capable of measuring different air pollutants. A custom PCB was designed that could support the acquisition of readings of, among others, particulate and CO sensors. Then, correlations between the concentrations of different pollutants were analyzed to identify if measuring the concentration of one type of pollutant can allow the extrapolation of the concentration of another. This particular study focuses on the correlations between the concentrations of particulate matter and CO. Finally, after observing a moderate correlation, it was proposed to measure the concentrations of pollutants that require less expensive sensors, and to extrapolate the concentrations of pollutants that require a more expensive sensor to measure their concentration. The link between particulate pollution and CO concentrations was identified and discussed as the result of this study.

Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data

Water depth estimation is paramount in various domains, including navigation, environmental monitoring, and resource management. Traditional depth measurement methods, such as bathymetry, can often be expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, specifically focusing on the Baidu Easy DL model for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using ACOLITE software, this research compares the performance of several machine learning algorithms, including the Stumpf model, Log-Linear model, and the Baidu Easy DL model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0–11 m depth range. This study showcases the substantial potential of machine learning in remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research opens avenues for the further exploration of machine learning applications in remote sensing and highlights the promising prospects of online model APIs when streamlining remote sensing data processing.

UAV-mounted Ground Penetrating Radar: an example for the stability analysis of a mountain rock debris slope

This paper describes scientific research conducted to highlight the potential of an integrated GPR-UAV system in engineering-geological applications. The analysis focused on the stability of a natural scree slope in the Germanasca Valley, in the western Italian Alps. As a consequence of its steep shape and the related geological hazard, the study used different remote sensed methodologies such as UAV photogrammetry and geophysics survey by a GPR-drone integrated system. Furthermore, conventional in-situ surveys led to the collection of geological and geomorphological data. The use of the UAV-mounted GPR allowed us to investigate the bedrock depth under the detrital slope deposit, using a non-invasive technique able to conduct surveys on inaccessible areas prone to hazardous conditions for operators. The collected evidence and the results of the analysis highlighted the stability of the slope with Factors of Safety, verified in static conditions (i.e., natural static condition and static condition with snow cover), slightly above the stability limit value of 1. On the contrary, the dynamic loading conditions (i.e., seismic action applied) showed a Factor of Safety below the stability limit value. The UAV-mounted GPR represented an essential contribution to the surveys allowing the definition of the interface debris deposit-bedrock, which are useful to design the slope model and to evaluate the scree slope stability in different conditions.

The microbial biodiversity at the archeological site of Tel Megiddo (Israel).

The ancient city of Tel Megiddo in the Jezreel Valley (Israel), which lasted from the Neolithic to the Iron Age, has been continuously excavated since 1903 and is now recognized as a World Heritage Site. The site features multiple ruins in various areas, including temples and stables, alongside modern constructions, and public access is allowed in designated areas. The site has been studied extensively since the last century; however, its microbiome has never been studied. We carried out the first survey of the microbiomes in Tel Megiddo. Our objectives were to study (i) the unique microbial community structure of the site, (ii) the variation in the microbial communities across areas, (iii) the similarity of the microbiomes to urban and archeological microbes, (iv) the presence and abundance of potential bio-corroding microbes, and (v) the presence and abundance of potentially pathogenic microbes. We collected 40 swab samples from ten major areas and identified microbial taxa using next-generation sequencing of microbial genomes. These genomes were annotated and classified taxonomically and pathogenetically. We found that eight phyla, six of which exist in all ten areas, dominated the site (>99%). The relative sequence abundance of taxa varied between the ruins and the sampled materials and was assessed using all metagenomic reads mapping to a respective taxon. The site hosted unique taxa characteristic of the built environment and exhibited high similarity to the microbiome of other monuments. We identified acid-producing bacteria that may pose a risk to the site through biocorrosion and staining and thus pose a danger to the site's preservation. Differences in the microbiomes of the publicly accessible or inaccessible areas were insignificant; however, pathogens were more abundant in the former. We found that Tel Megiddo combines microbiomes of arid regions and monuments with human pathogens. The findings shed light on the microbial community structures and have relevance for bio-conservation efforts and visitor health.

Drone-based magnetometer prospection for archaeology

Magnetometry is one of the most efficient and successful methods of archaeological prospection. Drone-based prospecting is increasingly being used in many fields of remote sensing but with respect to magnetometry, with rather poor results. For magnetic surveys, drone prospecting comes with the problem that magnetic and mechanical disturbances originating from the aircraft decrease the quality of the measurements.Here we present the adaptation of a commercial three-axis fluxgate magnetometer setup, which can be suitable for archaeological prospection, after applying appropriate filter methods and minimising the flying altitude and speed. For approval of the system, we performed drone-based surveys in high spatial resolution (50 cm line spacing) at constant, ultra-low sensor altitudes of 45 ± 10 cm and 75 ± 10 cm. We chose an archaeological site from the roman period as survey site (3.8 ha), where high-quality ground-based caesium magnetometer data was available. This allows us to demonstrate the first detailed comparison of drone-based and ground-based magnetic survey data for archaeology. For further evaluation, the influence of the drone on the measurements is assessed, drift and sensor resolution checks are carried out, and suitable data filtering methods are evaluated.Our results show that we can detect main archaeological features such as ditches, pits, fireplaces and remnants of stone fundaments with the drone-based setup, which are the relevant structures for the majority of archaeological prospections worldwide. Further, we can prove that the data quality can significantly be increased by lowering the flight altitude and speed. We conclude that drone-based magnetometry serves above all to cover large and inaccessible areas in short time. Our findings are a first step towards the development of standards for drone-based archaeological prospection approaches.

VDIU: VLC-Based Debugging Information Upload Method for Self-Powered Sensor Nodes

Currently, more and more self-powered sensor nodes have been deployed in remote and inaccessible areas, and thus online debugging of sensor nodes becomes more necessary and important. However, the existing sensor online debugging approaches mainly rely on original network infrastructure (such as Zigbee or NB-IoT). Once the communication infrastructure is invalid, it is almost impossible to retrieve debugging information and repair the lost self-powered sensor nodes. To this end, in our previous study, we employed the onboard optical modules of the self-powered sensor node to realize a Visible Light Communication(VLC)-based hybrid communication debugging system(VHCDS). In this paper, to improve the efficiency of uploading debugging information, we propose VDIU to optimize the digital pulse interval modulation applied in the uplink of VHCDS. This method reasonably groups and sorts the origin data to reduce the number of empty time slots (unused time slots) and complete data compression in source coding for saving energy. To improve the reliability of the uplink, we also design two frame synchronization mechanisms suitable for the receiver-side optical cameras of the uplink. One mechanism considers lightweight synchronization design via restricting the position of the frame header; the other one focuses on reducing the occupation of the downlink of VHCDS via predicting the position of the frame header using a temporal convolutional network (TCN). Experiments results illustrate that the upload efficiency using VDIU is higher 78% and 28% than DPIM and DH-PIM. Both synchronization mechanisms can arrive the similar performance, but the predicting mechanism reduces the 93% occupation on the downlink.

A novel method for measurements of surface topography in previously inaccessible areas

Abstract Since mechanical properties of parts in mechanical engineering are influenced in many ways by their surface structure, detailed knowledge of the surface structure is essential for predicting and describing these properties. Tactile and optical measurement methods are well established but cannot always reach every area of a part due to geometric constraints. To enable measurements in these areas, a method utilizing impressions and a laser scanning microscope is proposed. It is easy to use and able to correctly reproduce surface structures of technical surfaces, which is proven by a comparison of original parts and impressions of three surface areas on an example specimen. Two application examples from the domain of fatigue strength tests are shown. The surface structure is measured directly in the notch radius of shaft shoulders and inside hollow shafts made in different manufacturing processes. Utilizing modern optical measurement instruments, the proposed method enables accurate measurements of surface structure in previously inaccessible areas.

FOREST FIRE BURNT AREA EXTRACTION USING FUZZY INTEGRATION OF MULTI-SENSOR SATELLITE DATA FOR THE HIMALAYAN STATE

Abstract. Burnt area assessment due to forest fires is an important aspect to estimate the extent of loss of biodiversity which has become feasible even in hilly and inaccessible areas with the help of geospatial technologies. But satellite data also has some limitations as it increases commission error by misclassifying non-burnt areas as burnt areas. To reduce this commission error, present study has attempted to integrate multi-sensor satellite data to characterize and extract forest fire burnt areas in Uttarakhand which is a fire prone hilly state in Western Himalaya. Landsat-8 and Sentinel-2 optical datasets have been used to calculate eleven vegetation/burn indices to identify burn patches for fire season of 2022 (February to June). These vegetation/burn indices have been calculated from Landsat-8 and Sentinel-2 datasets and integrated using Fuzzy Logic Modelling to get characterized forest fire burnt area maps. Accuracy assessment has been done using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points for the characterized map of burnt area by Landsat-8, Sentinel-2 and combining indices from both sensors. The fuzzy map of burnt area using Landsat-8 showed the accuracy of 66.25%, while Sentinel-2 showed accuracy of 59.79% and the integration of fuzzy burnt area maps of both sensors showed the highest accuracy of 79.66%. This information of characterized burnt areas of a region can help forest managers to identify high vulnerable areas to focus on during the fire season to prevent the losses to natural resources, life and property in the region.

SPATIAL MAPPING OF TRAVEL INFORMATION AND ASSESSMENT OF ROAD CONNECTIVITY

Abstract. Roads play a crucial role in the urban spatial structure. A place's development and growth depend on the road network connectivity and accessibility being the socio-economic and transportation carrier. It involves the mobility of people and goods from one place to another. The choice of mode of travel depends on the living standards, connectivity, and vicinity to the work area. The study uses satellite data to analyze road network connectivity using the connectivity indices of Mangalore City Corporation, a port city in India. The connectivity indices alpha, beta, gamma, and eta showed the Area's good connectivity with proper roads and interconnectivity. Using Dijkstra's algorithm, the least cost path is identified on which the spatial mapping of the travel information is made. The travel information raster served the commuter in knowing the time, distance, and cost of modes from all possible origins to each city center. Specifically, it serves as the base map for bus routes, their cost, and travel time for significant city bus stations. The cost of travel, Duration, and distance information is mapped for two-wheeler and four-wheeler commuters. The study used the Modis Land Use Land Cover Data to identify inaccessible road network areas.

Analysis of the Stocks and Conditions of Harvesting for Forest Berries with Considering Their Spatial Distribution and Availability

Within the framework of the multi-purpose forest management concept, the resource and economic potential of harvesting wild berries was analyzed under different forest management scenarios. As a tool for forecasting and analysis, scenario-based simulation was used to model the forest ecosystems dynamics under the clearcut and the selective felling management types. The object of research is the Pashe-Kapetskoe district forestry of the Leningrad Region, the calculation was carried out for a period of 120 years. Based on the forest condition types’, species composition and simulated illumination at the ground level data, the potential productivity of wild berries was calculated. The most productive resources on the territory were bilberry and lingonberries, the predicted yield of which reaches 25–48 t/ha and 7–15 t/ha respectively. Zoning of the territory was carried out according to the resources availability for industrial harvesting, taking into account the interests of the local population. 37–48% of the wild berries harvest was available for industrial harvesting, about 30% was allocated for the needs of the local population, and 27–36% of the resource remained in economically inaccessible areas. The most promising was the scenario with artificial restoration of 50% of the clearcut areas and a full maintenance cycle. For this scenario, the maximum profitability was predicted from both the food resources procurement (4.1–5.7 million rubles per year) and the harvesting of timber.

Forgotten giants: Robust climate signal in pollarded trees

Tree ring records are among the most valuable resources to create high-resolution climate reconstructions. Most climate reconstructions are based on old trees growing in inaccessible mountainous areas with low human activity. Therefore, reconstruction of climate conditions in lowlands is usually based on data from distant mountains. Albeit old trees can be common in humanized areas, they are not used for climate reconstructions. Pollarding was a common traditional management in Europe that enabled trees to maintain great vitality for periods exceeding the longevity of unmanaged trees. We evaluate the potential of pollarded deciduous oaks to record past climate signal. We sampled four pollarded woodlands in Central Spain under continental Mediterranean climate. We hypothesized that pollarded trees have a strong response to water availability during current period without pollarding management, but also in the period under traditional management if pruning was asynchronous among trees. Moreover, we hypothesized that if climate is a regional driver of oak secondary growth, chronologies from different woodlands will be correlated. Pollard oaks age exceeded 500 years with a strong response to Standardized Precipitation-Evapotranspiration Index (SPEI) from 9 to 11 months. Climate signal was exceptionally high in three of the sites (r2 = 0.443–0.655) during low management period (1962–2022). The largest fraction of this climate signal (≈70 %) could be retrieved during the traditional management period (1902–1961) in the three sites where pollarding was asynchronous. Chronologies were significantly correlated since the 19th century for all the studied period, highlighting a shared climate forcing. We identified critical points to optimize pollard tree sampling schema. Our results show the enormous potential of pollarded woodlands to reconstruct hydroclimate conditions in the Mediterranean with a fine spatial grain. Studying pollarded trees is an urgent task, since the temporal window to retrieve the valuable information in pollarded trees is closing as these giants collapse and their wood rots.

Inaccessibility regions for the sterile neutrino searches at the long baseline experiments

We provide a novel approach based on χ2 analysis that investigates regions in the neutrino parameter space where long baseline experiments cannot discriminate between the (3+1)ν oscillation scheme and the standard 3ν oscillation scheme. We call such region of parameter space “the inaccessible region”. The inaccessible area of parameter space is so named because the minimal value of χ2 remains almost unaltered even after inserting the sterile neutrino in the description of the neutrino oscillation data. Such inaccessible zones are discovered for the three long-baseline experiments (T2K, NOva and DUNE). Finally, we find the region of the sterile neutrino parameter space that remains inaccessible even after combining all three experiments. If sterile neutrinos exist in nature but their mixing parameters are in this range, these long-baseline experiments cannot prove their existence. The significance of our finding stems from the likelihood of sterile neutrinos existing in the accessible zone, which may be tested in these experiments in near future.

Quantitative Characterization of Coastal Cliff Retreat and Landslide Processes at Portonovo–Trave Cliffs (Conero, Ancona, Italy) Using Multi-Source Remote Sensing Data

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD.

Resilience and reliable integration of PV-wind and hydropower based 100% hybrid renewable energy system without any energy storage system for inaccessible area electrification

Achieving sustainable development goals requires off-grid electrification of remote areas using sustainable energy systems (SES). However, capacity planning for SES is challenging due to the long-term need to meet fluctuating demand and the intermittent and unpredictable nature of renewable energy (RE) sources. The best system size for resilience and reliable load servicing of a 100% hybrid renewable energy system has been investigated in this study. The ideal system configuration for a hybrid solar PV, wind, and hydro energy system has been achieved by applying the multi objective genetic algorithm (MOGA) optimization technique to assess optimal size of the renewable energy system. The PV/Wind/Hydro system has the lowest NPC and COE with the best target capabilities among all the communities examined, provided the system lasts 25 years, according to the NPC study. The most ideal configuration (PV 88 kW, wind 18 kW, Hydro1 215 kW, and Hydro2 197 kW) investigated for deployment of hybrid power system at the specified site has NPC, total system cost, and energy cost of $163,238 and $517,141, respectively, and $0.35/kWh. Finally, the implementation of this innovative proposal demonstrated that reliable load serving is possible with 100% renewable energy without the use of an energy storage device.

Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation

In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R2 coefficient of determination of 0.92.

Diversity and independent evolutionary profiling of rodent-borne viruses in Hainan, a tropical island of China

The risk of emerging infectious diseases (EID) is increasing globally. More than 60% of EIDs worldwide are caused by animal-borne pathogens. This study aimed to characterize the virome, analyze the phylogenetic evolution, and determine the diversity of rodent-borne viruses in Hainan Province, China. We collected 682 anal and throat samples from rodents, combined them into 28 pools according to their species and location, and processed them for next-generation sequencing and bioinformatics analysis. The diverse viral contigs closely related to mammals were assigned to 22 viral families. Molecular clues of the important rodent-borne viruses were further identified by polymerase chain reaction for phylogenetic analysis and annotation of genetic characteristics such as arenavirus, coronavirus, astrovirus, pestivirus, parvovirus, and papillomavirus. We identified pestivirus and bocavirus in Leopoldoms edwardsi from Huangjinjiaoling, and bocavirus in Rattus andamanensis from the national nature reserves of Bangxi with low amino acid identity to known pathogens are proposed as the novel species, and their rodent hosts have not been previously reported to carry these viruses. These results expand our knowledge of viral classification and host range and suggest that there are highly diverse, undiscovered viruses that have evolved independently in their unique wildlife hosts in inaccessible areas.

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies.

Design and implementation of sustainable solar energy harvesting for low-cost remote sensors equipped with real-time monitoring systems

Data acquisition systems, such as Wireless Smart Sensor Networks (WSSNs) can increase the resilience of infrastructure by providing real-time monitoring and data collection of environmental parameters. Yet, sustainable energy supplies for sensor networks established in remote and inaccessible areas still present a challenge. Previously, researchers have attempted to address this difficulty by proposing different energy systems including solar energy harvesting, however, significant prolonged experimental data for the operation of extensive networks powered by solar energy has not been reported. This paper presents an original design and implementation of an energy system for a large WSSN and provides the sensors' power status data over a significant duration. A network of low-cost flood monitoring sensors, including twenty-six water level sensors, twenty rain gauges, and eight communication nodes were deployed and tested on summer and fall 2022 at six remote locations at the northern New Mexico Pueblo, Ohkay Owingeh. A thermometer and a humidity sensor were added to each communication node to record temperature and air's moisture level. In addition, a networked voltage monitoring system was deployed to observe the sensors energy status in real-time. The items of the WSSN are composed of two differing energy circuits suited for their energy demands. The sensors' energy circuits contain a photovoltaic panel, a lithium-polymer battery, a control device, and a DC-to-DC converter. Whereas the communication nodes contain another photovoltaic panel, a lead-acid battery, and a solar charging controller. The findings provide a perspective on the long-term field deployment of WSSNs consisting of low-cost sensors.

MR-guided HDR prostate brachytherapy with teleoperated steerable needles

Conformity of tumour volumes and dose plans in prostate brachytherapy (BT) can be constrained by unwanted needle deflections, needle access restrictions and visualisation limitations. This work validates the feasibility of teleoperated robotic control of an active steerable needle using magnetic resonance (MR) for guidance. With this system, perturbations can be counteracted and critical structures can be circumvented to access currently inaccessible areas. The system comprises of (1) a novel steerable needle, (2) the minimally invasive robotics in an MR environment (MIRIAM) system, and (3) the daVinci Research Kit (dVRK). MR scans provide visual feedback to the operator controlling the dVRK. Needle steering is performed along curved trajectories to avoid the urethra towards targets (representing tumour tissue) in a prostate phantom with a targeting error of 1.2 ± 1.0 mm. This work shows the potential clinical applicability of active needle steering for prostate BT with a teleoperated robotic system in an MR environment.