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William Leybourn (1626 – 1716) was a printer, author, teacher, surveyor, and mathematician. The occupation of “printer” is often associated with passivity: a simple artisan whose sole focus is to bring books to sale and therefore a stranger to the knowledge contained within the books. This paper seeks to revisit this view, showing that William Leybourn was the exact opposite of a passive printer. He introduced to the English public the works of crucial protagonists of the European scientific community (including the works of Galileo Galilei), promoted debates among scholars and natural philosophers, and presented complex topics previously reserved for academic disputations in a manner accessible to a more general audience. Employing both traditional historical research and tools in Digital Humanities, the paper discusses Leybourn’s works as an example of a conceptually and methodologically novel approach to the study of intellectual networks. This bottom-up approach recenters traditionally peripheral figures, such as Leybourn, and reveals crucial social, economic, and political aspects of early modern European cultural and intellectual debates.

The research paper investigates multitiered web application architecture and layers of web application architectures based on relevant literature using the scientific method of content analysis. Based on the results of the research (survey, where the scientific method of surveying based on a sample of 42 respondents), was used information was obtained: (1) whether the respondents had heard of tools and technologies that serve as help in programming and testing, such as: JUnita, Apache JMeter, Mockita, (2) whether the respondents used tools and technologies that serve as help in programming and testing such as: JUnita, Apache JMeter and Mockite, (3) which of the above tools are the best in the opinion of the respondents for help in programming and testing from the above: JUnita, Apache JMeter, Mockite, (4) whether the respondents have used BootStrap so far, (5) what according to respondents BootStrap enables, (6) which tools do web developers prefer to use when programming for the purpose of creating websites (applications), (7) what is most important for respondents in order for a website to be evaluated as successfully created and of high quality, (8) did the respondents know that the multi-layer architecture of web applications consists of a presentation layer, a controller layer, a repository layer, a model layer and an auxiliary layer. The research came to the conclusion that the hypothesis "H1" was accepted, while H2 is rejected. In the research, hypothesis H3 was also rejected, while the alternative hypothesis H4 was accepted, based on the chi-square test for all tested cases. [ABSTRACT FROM AUTHOR]

This article offers a new approach to analysing the medieval vaults of Norwich cathedral cloister, which at first appearance seem to be of a consistent design, yet detailed inspection and analysis reveals this not to be the case. Whilst previous scholars have recognised the presence of inconsistencies, their focus has been on chronology, patronage as well as the iconographic programme of the extraordinary range of sculpted bosses. Our approach is based on accurate digital surveying and analysis methods and comparison with similar data obtained from other sites. This enables us to understand the design of the cloister vaults in plan, including bay proportions and the nuances of the tierceron star arrangement of ribs. We then employ a representation technique termed the 'middle plan' to identify and analyse specific design runs and their breaks in a greater level of detail than previously achieved, including analysis of geometric data to explain different processes that were likely used to create the curvatures of individual ribs. In doing so, we have identified methods not previously described, which reveal the inventiveness of medieval masons faced with a series of complex constructional issues and enabling them to achieve a range of visual effects. [ABSTRACT FROM AUTHOR]

Long-term historical data on the presence of invasive pests can inform current pest management. By examining correlations between shifts in pest occurrences and various potential drivers, we can better inform decision-making and management strategies. However, the availability of such long-term data is often limited. We apply a data rescue protocol to recover difficult-to-access pest information from periodical annual forestry reports on the island of Ireland from 1970 to 2020, resulting in an open-access dataset of pest dynamics and their management for the island. We combined the pest dataset with auxiliary weather data to estimate the effects of surveying effort, control measures and weather upon observed pest outbreak dynamics. A first-order auto-logistic regression model was used to model rates of transition between observed presences and absences of non-native insects, fungi, chromista, and bacteria. The results provide evidence that multi-year systematic surveillance efforts have improved the detection of pest species before they have arrived and help towards preventing false absences of invaded species being recorded. We provide reporting recommendations for invasive species which would improve the usability of reported data to better understand observed pest dynamics going forward. Our methodology for data collection and analysis serves as a blueprint for other regions of the world and other invasive species assemblages where data is physically available but not ready for analysis. [ABSTRACT FROM AUTHOR]

Remote survey is becoming increasingly common for the mining industry, so studying the choice of a UAV device is a very relevant issue, which will help increase the productivity of the survey service. The aim of the study is to study UAV species, compare species, and choose the optimal one based on the results of the study. Considering the above-mentioned, there is a question of choosing the type of UAV to conduct surveys in the mining industry. To date, there is no clear comparison and description of the predominant types of UAVs for surveying. The choice of a certain type of UAV for surveying raises a number of questions related to the type of work performed, weather conditions, and the terrain of the survey. The main research methods are literature analysis, factor analysis method, analysis of UAV types for performing survey using UAVs. The above helped to justify the best devices for conducting surveying. The work carried out made it possible to determine the optimal UAV device for carrying out these works.

Geospatial technology is still a growth area and knowledge of these technologies remains essential for a skilled workforce. In higher education, geospatial curriculum design is predominantly determined by educators and is influenced by what educators believe should be taught. Knowledge of national or regional provision can support a richer understanding of expected provision and areas of over or under-provision to better support the needs of varied geospatial roles and identify possible programme specialisations. In this paper, we describe an approach for evaluating Aotearoa New Zealand’s provision of geospatial education in the tertiary education sector. First, we examine and compare current course offerings in New Zealand’s tertiary sector against selected institutions in five countries using the GIS&T Body of Knowledge and Geospatial Technology Competency Model as a framework for comparison. Second, we evaluate the results of a focus-group workshop of geospatial education providers and geospatial industry stakeholders about perceived needs in geospatial education in New Zealand. We find this approach works well to support a better understanding of what is provided for both educators and industry. By including industry geospatial professionals in identifying perceived gaps, educators’ knowledge of industry needs will grow as well as industry’s understanding of current provision. [ABSTRACT FROM AUTHOR]

This paper utilizes the concept of the agency of material objects, proposed as a tenet by several historians of science in the late twentieth century. It argues that scientific instruments have agency in the field due to their value and fragility, both of which attributes served to dictate a social hierarchy of activity. Furthermore, the resulting numerical outputs served to discriminate between various groups. The principal focus is the role of instruments used by travellers sponsored by the Royal Geographical Society, London, but it also looks at the role of instruments on a larger triangulation; the project to survey the African part of the arc of the 30th meridian east of Greenwich. Seeing the instruments as material objects allows insights into the social arrangements and interactions taking place. It is argued that instruments, and the results of instrument use, entrenched existing power hierarchies, imparting greater prestige to those who were previously endowed with privilege. It is argued the instruments contributed to ‘othering’ both in the field and in the publications resulting from the expeditions. [ABSTRACT FROM AUTHOR]

The article examines the problem of perceiving and making sense of a literary text, using interdisciplinary connections for this purpose. A brief retrospective analysis is introduced, then the relevance of the problem is emphasized. The methodology of the research is presented with the survey conducted among teachers from the initial stage of the basic level of education and conclusions from the two components are drawn. The survey is entirely online. It is based on two criteria with corresponding co-indicators to them. It includes questions about the perception and understanding of literary text by students in the elementary stage, as well as the use of intersubject connections by teachers - opinions about their usefulness, examples of their application and achieving the motivation of students for the learning process; as well as the strategies used to improve skills, the difficulties students encounter, and the joint activities with parents. Relevant conclusions have been drawn. [ABSTRACT FROM AUTHOR]

Contextualization: The incorporation of new techniques and technologies to learn about the composition and characteristics of soils used in agricultural production is booming. Free license products such as Sentinel-2 satellite images allow the correlation of soil properties with spectral indices. Knowledge gap: In the agricultural sector, indices calculated from satellite images have focused on vegetation characterization. To a lesser extent, studies have been conducted to evaluate the correlation of those applied to mineralogical prospecting to correlate them with the chemical characteristics of the soil. Purpose: This study was developed with to evaluate the correlation of the indexes of ferric oxides (B11/B8), iron silicates (B12/B11), ferrous iron (B12/8 + B3/4), and alteration (B11/B12), with the content of chemical elements of importance in crop production. Methodology: A crop of 282 hectares was selected in the municipality of Puerto López in the Meta department. Using a Sentinel-2 image, the evaluated indices were calculated. After obtaining the indices, 40 sites were selected for a composite sampling of soils to determine the following characteristics: pH in water (1:1 potentiometric), organic matter (Walkey and Black), P (Bray II), Al (KCl 1N), Ca, Mg, K, and Na (Ammonium Acetate), Fe (DTPA). With the information obtained, a multiple regression analysis was carried out using the “Stepwise” method. The raster layers with the calculated indices were used as independent variables, and the values of the properties determined in the laboratory were used as the variables to predict. Results and conclusions: The evaluated indexes showed a correlation greater than 0.60 with Mg and Ca content. A correlation between 0.40 and 0.60 was found between the indices and organic carbon and Fe content. The calculated regression models revealed fits of 0.54, 0.47, and 0.33 to Mg, Ca, and Fe, respectively. The findings of this research lead to the conclusion that it is possible from Sentinel-2 images to estimate the concentration of elements available to plants in the edaphoclimatic conditions of the Colombian Altillanura. [ABSTRACT FROM AUTHOR]

In the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite‐derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high‐resolution satellite imagery and in‐situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground‐truth data and imagery from Aster, Landsat‐8, and Sentinel‐2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K‐Neighbors Regressor, k‐fold cross‐validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat‐8 data set with the GPR algorithm achieves R2 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel‐2 data set with GPR and RF algorithms attains R2 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation. Plain Language Summary: Researchers are leveraging advancements in space technology to enhance the mapping and comprehension of coastal regions, particularly focusing on satellite‐derived bathymetry (SDB). SDB utilizes high‐resolution satellite imagery in conjunction with on‐site data to estimate the submerged terrain. However, the application of these methodologies along the Indian coastline poses challenges due to factors such as turbidity and the presence of complex geological formations like creeks and deltas. This study explored the utilization of multivariate machine learning (ML) regression techniques to improve the estimation of SDB. Various algorithms were tested using data sourced from satellites such as Aster, Landsat‐8, and Sentinel‐2 across two different sites with diverse underwater landscapes. Results demonstrate promising accuracy, particularly when employing Landsat‐8 data in conjunction with Gaussian Process Regression (GPR), yielding an R2 value of 0.94. Similarly, at another site, the combination of SENTINEL‐2 data with GPR and RF achieved an R2 value of 0.97, underscoring the potential of ML techniques in mapping intricate coastal terrains despite challenges like turbid waters. Key Points: Advancements in space technology for coastal bathymetry mappingPredicting coastal geomorphology with satellite‐derived bathymetry (SDB)Multivariate machine learning regression for estimating SDB [ABSTRACT FROM AUTHOR]

The importance of governance arrangements for access, use and control of land and resources is being increasingly recognized by international institutions. This paper overviews land governance initiatives being promoted by UN Habitat, and the growing importance of rapidly developing geospatial technologies. The paper next addresses land governance issues in the MENA region, particularly the Arab Land Governance initiative, and presents a brief case study of Oman's land governance, and its effects upon society, urban settlement patterns, and land markets. Issues of education and training for better land governance are discussed. Recommendations and conclusions address the issues and ways forward for the region. [ABSTRACT FROM AUTHOR]

Measuring nearshore waves remains technically challenging despite wave properties are being used in a variety of applications. With the promise of high-resolution and remotely-sensed measurements of water surfaces in four dimensions (spatially and temporally), stereo-photogrammetry applied to video imagery has grown as a viable solution over the last ten years. However, past deployments have essentially used costly cameras and optics, requiring fixed deployment platforms and hindering the applicability of the method in the field.Focusing on close-range measurements of nearshore waves at break point, this paper presents a detailed evaluation of a field-oriented and cost-effective stereo-video system composed of two GoProTM (Hero 7) cameras capable of collecting 12-megapixel imagery at 24 frames per second. The so-called ‘Stereo-GoPro’ system was deployed in the surf zone during energetic conditions at a macrotidal field site using a custom-assembled mobile tower. Deployed concurrently with stereo-video, a 16-beam LiDAR (Light Detection and Ranging) and two pressure sensors provided independent data to assess stereo-GoPro performance. All three methods were compared with respect to the evolution of the free-surface elevation over 25 min of recording at high tide and the wave parameters derived from spectral analysis. We show that stereo-GoPro allows producing digital elevation models (DEMs) of the water surface over large areas (250 m2) at high spatial resolution (0.2 m grid size), which was unsurpassed by the LiDAR. From instrument inter-comparisons at the location of the pressure transducers, free-surface elevation root-mean square errors of 0.11 m and 0.18 m were obtained respectively for LiDAR and stereo-GoPro. This translated into a maximum relative error of 3.9% and 12.5% on spectral wave parameters for LiDAR and stereo-GoPro, respectively. Optical distortion in imagery, which could not be completely corrected with calibration, was the main source of error. Whilst stereo-video processing workflow remains complex, cost-effective stereo-photogrammetry already opens new opportunities for deriving wave parameters in coastal regions, as well as for various other practical applications. Further tests should try to address specifically challenges associated to variable ambient conditions and acquisition configurations, affecting measurement performance, to guarantee a larger uptake of the technique.

Supervised deep learning algorithms have recently achieved state-of-the-art performance in the classification, segmentation and analysis of 3D LiDAR point cloud data in a wide-range of applications and environments. One of the main downsides of deep learning-based approaches is the need for extensive training datasets, i.e. LiDAR point clouds that have been annotated for target tasks by human experts. One strategy for addressing this issue is the use of simulated/synthetic data (with automatically generated annotations) for training models which can then be deployed on real target data/environments. This paper explores using synthetic data of realistic forest trees to train deep learning models for tree part segmentation from real forest LiDAR data. We develop a new pipeline for generating high-fidelity simulated LiDAR scans of synthetic forest trees and combine this with an unsupervised domain adaptation strategy to adapt models trained on synthetic data to LiDAR data captured in real forest environments.Models were trained for semantic segmentation of tree parts using a PointNet++ architecture and evaluated across a range of medium to high-resolution laser scanning datasets collected across both ground-based and aerial platforms in multiple forest environments. Results of our work indicated that models trained on our synthetic data pipeline were competitive with models trained on real data, in particular when real data came from non-target sites, and our unsupervised domain adaptation method further improved performance. Our approach has implications for reducing the burden required in manual human expert annotation of large LiDAR datasets required to achieve high-performance from deep learning methods for forest analysis. The use of synthetically-trained models shown here provides a potential way to reduce the barriers to the use of deep learning in large-scale forest analysis, with implications to applications ranging from forest inventories to scaling-up in-situ forest phenotyping.

Leveraging ground-annotated data for scene analysis on unmanned aerial vehicles (UAVs) can lead to valuable real-world applications. However, existing unsupervised domain adaptive (UDA) methods primarily focus on domain confusion, which raises conflicts among training data if there is a huge domain shift caused by variations in observation perspectives or locations. To illustrate this problem, we present a ground-to-UAV fire segmentation method as a novel benchmark to verify typical UDA methods, and propose an effective framework, Colour-Mix, to boost the performance of the segmentation method equivalent to the fully supervised level. First, we identify domain-invariant fire features by deriving fire-discriminating components (u*VS) defined in colour spaces Lu*v*, YUV, and HSV. Notably, we devise criteria to combine components that are beneficial for integrating colour signals into deep-learning training, thus significantly improving the generalisation abilities of the framework without resorting to UDA techniques. Second, we perform class-specific mixing to eliminate irrelevant background content on the ground scenario and enrich annotated fire samples for the UAV imagery. Third, we propose to disentangle the feature encoding for different domains and use class-specific mixing as robust training signals for the target domain. The framework is validated on the drone-captured dataset, Flame, by using the combined ground-level source datasets, Street Fire and Corsica Wildfires. The code is available at https://github.com/Rui-Zhou-2/Colour-Mix.

Plant traits play a pivotal role in steering ecosystem dynamics. As plant canopies have evolved to interact with light, spectral data convey information on a variety of plant traits. Machine learning techniques have been used successfully to retrieve diverse traits from hyperspectral data. Nonetheless, the efficacy of machine learning is restricted by limited access to high-quality reference data for training. Previous studies showed that aggregating data across domains, sensors, or growth forms provided by collaborative efforts of the scientific community enables the creation of transferable models. However, even such curated databases are still sparse for several traits. To address these challenges, we investigated the potential of filling such data gaps with simulated hyperspectral data generated through the most widely-used radiative transfer model (RTM) PROSAIL. We coupled trait information from the TRY plant trait database with information on plant communities from the sPlot database, to build a realistic input trait dataset for the RTM-based simulation of canopy spectra. Our findings indicate that simulated data can alleviate the effects of data scarcity for highly underrepresented traits. In most other cases, however, the effects of including simulated data from RTMs are negligible or even negative. While more complex RTM models promise further improvements, their parameterization remains challenging. This highlights two key observations: firstly, RTM models, such as PROSAIL, exhibit limitations in producing realistic spectra across diverse ecosystems; secondly, real-world data repurposed from various sources exhibit superior retrieval success compared to simulated data. As a result, we advocate to emphasize the importance of active data sharing over secrecy and overreliance on modeling to address data limitations.

After more than twenty years of commercial use, laser scanners have reached technical maturity and consequently became a standard tool for 3D-data acquisition across various fields of application. Yet, meaningful stochastic information regarding the achieved metric quality of recorded points remains an open research question. Recent research demonstrated that raw intensity values can be deployed to derive stochastic models for reflectorless rangefinders. Yet, all existing studies focused on single instances of particular laser scanners while the derivation of the stochastic models required significant efforts.Motivated by the aforementioned shortcomings, the focus of this study is set on the comparison of stochastic models for a series of eight identical phase-based scanners that differ in age, working hours and date of last calibration. In order to achieve this, a standardised methodological workflow is suggested to derive the unknown parameters of the individual stochastic models. Based on the generated outcome, a comparison is conducted which clarifies if a universally applicable stochastic model (type calibration) can be used for a particular scanner model or if individual parameter sets are still required for every scanner (instance calibration) to validate the practical benefit and usability of those models. The generated results successfully demonstrate that the computed stochastic model is transferable to all individual scanners of the series.

The goal for the next generation of terrestrial reference frames (TRF) is to achieve a 1-mm- and 0.1-mm/yr-accurate frame realization through the combination of reference station solutions by multi-technique geodetic observatories. A potentially significant source of error in TRF realizations is the inter-system ties between the instruments at multi-technique stations, usually independently determined through ground-based local surveying. The quality of local tie surveys is varied and inconsistent, largely due to differences in measurement techniques, surveying instruments, site conditions/geometries, and processing methods. The Global Geodetic Observing System (GGOS) has tried to address these problems by issuing guidelines for the construction and layout of future multi-technique observatories, promoting uniformity and quality while minimizing existing problems with local surveying that are exacerbated over longer baseline distances. However, not every observatory is going to be able to completely satisfy these guidelines, and in this work, a successful endeavor to satisfy the accuracy goals while exceeding the GGOS baseline guideline is detailed for the McDonald Geodetic Observatory (MGO) in the Davis Mountains of Texas, USA. MGO consists of a VLBI Geodetic Observing System (VGOS), infrastructure in place for a Space Geodesy Satellite Laser Ranging (SGSLR) telescope, and several Global Navigation Satellite Systems (GNSS) stations spanning a 900 m baseline and a 120 m elevation change. The results of the local ties between the GNSS stations across the near-kilometer baseline, as measured from their antenna reference points, show sub-mm precision and 1 mm accuracy validated through repeatability across several surveys conducted in 2021as well as 1 mm consistency with the monthly averaged daily solutions of the GNSS-based positioning. In this paper, we report these results as well as the framework of the surveys with sufficient detail and rigor in order to give confidence to the quality claims and to present the novel design and techniques employed in the procedure, processing, and error-budget analysis, which were determined through iterative research methods across repeated survey campaigns. [ABSTRACT FROM AUTHOR]

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a geospatial approach to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mitigating floods and preventing overtopping failure at Wivenhoe Dam. Using QGIS software and a 25 m resolution DEM from the Queensland Spatial Catalogue 'QSpatial' website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied on several dam wall options to identify the cost-effective and optimal one based on the dam wall dimensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria yielding the optimal choice. The study provides insights into the feasibility and effectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation plans for the asset owners. [ABSTRACT FROM AUTHOR]

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Accurate and precise measures of channel morphology are important when monitoring a stream post-restoration to determine changes in stability, water quality, and aquatic habitat availability. Practitioners often rely on traditional surveying methods such as a total station for measuring channel metrics (e.g., cross-sectional area, width, depth, and slope). However, these methods have limitations in terms of coarse sampling densities and time-intensive field efforts. Drone-based lidar or drone laser scanning (DLS) provides much higher resolution point clouds and has the potential to improve post-restoration monitoring efforts. For this study, a 1.3-km reach of Stroubles Creek (Blacksburg, VA, USA), which underwent a restoration in 2010, was surveyed twice with a total station (2010 and 2021) and twice with DLS (2017 and 2021). The initial restoration was divided into three treatment reaches: T1 (livestock exclusion), T2 (livestock exclusion and bank treatment), and T3 (livestock exclusion, bank treatment, and inset floodplain). Cross-sectional channel morphology metrics were extracted from the 2021 DLS scan and compared to metrics calculated from the 2021 total station survey. DLS produced 6.5 times the number of cross sections over the study reach and 8.8 times the number of points per cross section compared to the total station. There was good agreement between the metrics derived from both surveying methods, such as channel width (R2 = 0.672) and cross-sectional area (R2 = 0.597). As a proof of concept to demonstrate the advantage of DLS over traditional surveying, 0.1 m digital terrain models (DTMs) were generated from the DLS data. Based on the drone lidar data, from 2017 to 2021, treatment reach T3 showed the most stability, in terms of the least change and variability in cross-sectional metrics as well as the least erosion area and volume per length of reach. [ABSTRACT FROM AUTHOR]

Several industrial and commercial bulk material management applications rely on accurate, current stockpile volume estimation. Proximal imaging and LiDAR sensing modalities can be used to derive stockpile volume estimates in outdoor and indoor storage facilities. Among available imaging and LiDAR sensing modalities, the latter is more advantageous for indoor storage facilities due to its ability to capture scans under poor lighting conditions. Evaluating volumes from such sensing modalities requires the pose (i.e., position and orientation) parameters of the used sensors relative to a common reference frame. For outdoor facilities, a Global Navigation Satellite System (GNSS) combined with an Inertial Navigation System (INS) can be used to derive the sensors’ pose relative to a global reference frame. For indoor facilities, GNSS signal outages will not allow for such capability. Prior research has developed strategies for establishing the sensor position and orientation for stockpile volume estimation while relying on multi-beam spinning LiDAR units. These approaches are feasible due to the large range and Field of View (FOV) of such systems that can capture the internal surfaces of indoor storage facilities.The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, wide range of temperature variation, dust, and corrosive environment in deicing salt storage facilities) limit the use of such systems. With the increasing availability of solid-state LiDAR units, there is an interest in exploring their potential for stockpile volume estimation. Despite their higher robustness to harsh conditions, solid-state LiDAR units have shorter distance measurement range and limited FOV when compared with multi-beam spinning LiDAR. This research presents a strategy for the extrinsic calibration (i.e., estimating the relative pose parameters) of installed solid-state LiDAR units inside stockpile storage facilities. The extrinsic calibration is made possible using deployed spherical targets and a complete, reference scan of the facility from another LiDAR sensing modality. The proposed research introduces strategies for: 1) automated extraction of the spherical targets; 2) automated matching of these targets in the solid-state LiDAR and reference scans using invariant relationships among them; and 3) coarse-to-fine estimation of the calibration parameters. Experimental results in several facilities have shown the feasibility of using the proposed methodology to conduct the extrinsic calibration and volume evaluation with an error percentage less than 3.5% even with occlusion percentages reaching up to 50%.

Terrestrial Radar Interferometry (TRI) is widely adopted in geomonitoring applications due to its capability to precisely observe surface displacements along the line of sight, among other key characteristics. As its deployment grows, TRI is also increasingly used to monitor smaller and more dispersed geological phenomena, where the challenge is their precise localization in 3d space if the pose of the radar interferometer is not known beforehand. To tackle this challenge, we introduce a semi-automatic target-based georeferencing method for precisely aligning TRI data with 3d point clouds obtained using long-range Terrestrial Laser Scanning (TLS). To facilitate this, we developed a multi-modal corner reflector (mmCR) that serves as a common reference point recognizable by both technologies, and we accompanied it with a semi-automatic data-processing pipeline, including the algorithms for precise center estimation. Experimental validation demonstrated that the corner reflector can be localized within the TLS data with a precision of 3–5 cm and within the TRI data with 1–2 dm. The targets were deployed in a realistic geomonitoring scenario to evaluate the implemented workflow and the achievable quality of georeferencing. The post-georeferencing mapping uncertainty was found to be on a decimeter level, matching the state-of-the-art results using dedicated targets and achieving more than an order of magnitude lower uncertainty than the existing data-driven approaches. In contrast to the existing target-based approaches, our results were achieved without laborious visual data inspection and manual target detection and on significantly larger distances, surpassing 2 km. The use of the developed mmCR and its associated data-processing pipeline extends beyond precise georeferencing of TRI imagery to TLS point clouds, allowing for alternatively georeferencing using total stations, mapping quality evaluation as well as on-site testing and calibrating TRI systems within the application environment.

The progressing industrialization of oceans mandates reliable, accurate and automatable subsea survey methods. Close-range photogrammetry is a promising discipline, which is frequently applied by archaeologists, fish-farmers, and the offshore energy industry. This paper presents a robust approach for the reliable detection and identification of photogrammetric markers in subsea images. The proposed method is robust to severe image degradation, which is frequently observed in underwater images due to turbidity, light absorption, and optical aberrations. This is the first step towards a highly automated work-flow for single-camera underwater photogrammetry. The newly developed approach comprises several machine learning models, which are trained by 10,122 real-world subsea images, showing a total of 338,301 photogrammetric markers. The performance is evaluated using an object detection metrics, and through a comparison with the commercially available software Metashape by Agisoft. Metashape delivers satisfactory results when the image quality is good. In images with strong noise, haze or little light, only the novel approach retrieves sufficient information for a high degree of automation of the subsequent bundle adjustment. While the need for offshore personnel and the time-to-results decreases, the robustness of the survey increases.

Forest diebacks pose a major threat to global ecosystems. Identifying and mapping both living and dead trees is crucial for understanding the causes and implementing effective management strategies. This study explores the efficacy of Mask R–CNN for automated forest dieback monitoring. The method detects individual trees, delineates their crowns, and classifies them as alive or dead. We evaluated the approach using aerial imagery and canopy height models in the Harz Mountains, Germany, a region severely affected by forest dieback. To assess the model's ability to track changes over time, we applied it to images from three separate flight campaigns (2009, 2016, and 2022). This evaluation considered variations in acquisition dates, cameras, post-processing techniques, and image tilting. Forest changes were analyzed based on the detected trees' number, spatial distribution, and height. A comprehensive accuracy assessment demonstrated the Mask R–CNN's robust performance, with precision scores ranging from 0.80 to 0.88 and F1-scores from 0.88 to 0.91. These results confirm the model's ability to generalize across diverse image acquisition conditions. While minor changes were observed between 2009 and 2016, the period between 2016 and 2022 witnessed substantial dieback, with a 64.57% loss of living trees. Notably, taller trees appeared to be particularly affected. This study highlights Mask R–CNN's potential as a valuable tool for automated forest dieback monitoring. It enables efficient detection, delineation, and classification of both living and dead trees, providing crucial data for informed forest management practices.

3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities; although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, including image-based 3D reconstruction. State-of-the-art monocular and stereo algorithms leverage deep learning techniques and achieve increased performance in depth estimation and 3D reconstruction. However, one of the limitations of such methods is that they highly rely on large training sets that are often tedious to obtain; even when available, they typically refer to indoor, close-range scenarios and low-resolution images. Especially while considering UAV (Unmanned Aerial Vehicle) scenarios, such data are not available and domain adaptation is not a trivial challenge. To fill this gap, the UAV-based multi-sensor dataset for geospatial research (UseGeo - https://usegeo.fbk.eu/home) is introduced in this paper. It contains both image and LiDAR data and aims to support relevant research in photogrammetry and computer vision with a useful training set for both stereo and monocular 3D reconstruction algorithms. In this regard, the dataset provides ground truth data for both point clouds and depth maps. In addition, UseGeo can be also a valuable dataset for other tasks such as feature extraction and matching, aerial triangulation, or image and LiDAR co-registration. The paper introduces the UseGeo dataset and validates some state-of-the-art algorithms to assess their usability for both monocular and multi-view 3D reconstruction.

Real-time object detection and tracking is an active area of aerial remote sensing research that enables many environmental and ecological monitoring and preservation applications. Despite the development of several solutions tailored for these specific applications, trade-offs between cost efficiency and feature richness persist. This paper proposes a lightweight, low-cost, and modular approach to real-time object detection and instance tracking, enabling a wide gamut of use cases. By integrating real-time object detection models with affordable embedded hardware, we present a system that uses image metadata to perform geolocation on detected objects, enabling real-time applications due to minimal computational overhead. This algorithm generates cleaner ’areas of interest’ based on geolocated detections filtered by a clustering algorithm to remove false positives. In our findings, this proved a viable solution with real-time processing speeds and GPS positioning accuracy within a meter. While there is room for improvement, our proposed pipeline represents a significant step forward in lowering the costs involved with applying computer vision to conservation applications.

Depth estimation and 3D model reconstruction from aerial imagery is an important task in photogrammetry, remote sensing, and computer vision. To compare the performance of different image-based approaches, this study presents a benchmark for UAV-based aerial imagery using the UseGeo dataset. The contributions include the release of various evaluation routines on GitHub, as well as a comprehensive comparison of baseline approaches, such as methods for offline multi-view 3D reconstruction resulting in point clouds and triangle meshes, online multi-view depth estimation, as well as single-image depth estimation using self-supervised deep learning. With the release of our evaluation routines, we aim to provide a universal protocol for the evaluation of depth estimation and 3D reconstruction methods on the UseGeo dataset. The conducted experiments and analyses show that each method excels in a different category: the depth estimation from COLMAP outperforms that of the other approaches, ACMMP achieves the lowest error and highest completeness for point clouds, while OpenMVS produces triangle meshes with the lowest error. Among the online methods for depth estimation, the approach from the Plane-Sweep Library outperforms the FaSS-MVS approach, while the latter achieves the lowest processing time. And even though the particularly challenging nature of the dataset and the small amount of training data leads to a significantly higher error in the results of the self-supervised single-image depth estimation approach, it outperforms all other approaches in terms of processing time and frame rate. In our evaluation, we have also considered modern learning-based approaches that can be used for image-based 3D reconstruction, such as NeRFs. However, due to the significantly lower quality of the resulting 3D models, we have only included a qualitative comparison between NeRF-based and conventional approaches in the scope of this work.

Abstract In the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite‐derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high‐resolution satellite imagery and in‐situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground‐truth data and imagery from Aster, Landsat‐8, and Sentinel‐2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K‐Neighbors Regressor, k‐fold cross‐validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat‐8 data set with the GPR algorithm achieves R2 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel‐2 data set with GPR and RF algorithms attains R2 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation.

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Recently, there has been a recognized need to use waste lime landfills for land reclamation projects. Waste lime earthwork is more challenging than general earthwork operations due to potential health and safety hazards and significant changes in earthwork volume. There has been substantial research undertaken on the role of advanced construction technologies in general earthwork; however, their potential in hazardous waste lime environments for earthwork management is yet to be explored. This research proposes a methodology that integrates drone and BIM construction technology for effective and efficient hazardous waste lime earthwork management for construction projects. Drones are used for surveying, providing as-built information, and BIM to model, process, and evaluate the progress of waste lime earthwork. Volume change analyses are conducted using BIM to calculate dehydration time due to the ripple effect of waste lime for cost-efficient earthwork haulage. The case study provides evidence that the proposed method using advanced construction technologies improves productivity, safety, cost control, and better project management for earthwork related to hazardous waste materials. [ABSTRACT FROM AUTHOR]

Predicting crop yield using deep learning (DL) and remote sensing is a promising technique in agriculture. In smallholder agriculture (

Various methods have been developed to assign pollen to its botanical origin. They range from technically complex approaches to the less precise but sophisticated chromatic assessment, in which the pollen colors are used for identification. However, a common challenge lies in the similarity of colors of pollen from different plant species. The advent of camera-based bee monitoring systems has sparked renewed interest in classifying pollen based on color and offers potential advances for honey bee biomonitoring. Despite the promise of improved sensor accuracy, a critical examination of whether color diversity within a single species may be the primary limiting factor has been lacking. Our comprehensive analysis, which includes over 85,000 corbicular pollen from 30 major pollen species, shows that the average color variation within each species is distinguishable to a human observer, similar to the difference between two dissimilar colors. From today's perspective, the considerable color variation within a single pollen source makes the use of color alone to classify pollen impractical. When picking a single pollen color from the entire dataset, we report a correct pollen type classification rate of 67 %. The accuracy was highly dependent on the type and ranged from 0 % for rare types with common colors to 99 % for distinct colors. The large color dispersion within species highlights the need for complementary methods to improve the accuracy and reliability of color-based pollen identification in biomonitoring applications.

In recent years, there has been a growing emphasis on assessing and ensuring the quality of horticultural and agricultural produce. Traditional methods involving field measurements, investigations, and statistical analyses are labour-intensive, time-consuming, and costly. As a solution, Hyperspectral Imaging (HSI) has emerged as a non-destructive and environmentally friendly technology. HSI has gained significant popularity as a new technology, particularly for its promising applications in remote sensing, notably in agriculture. However, classifying HSI data is highly complex because it involves several challenges, such as the excessive redundancy of spectral bands, scarcity of training samples, and the intricate non-linear relationship between spatial positions and spectral bands. Notably, Deep Learning (DL) techniques have demonstrated remarkable efficacy in various HSI analysis tasks, including those within agriculture. As interest continues to surge in leveraging HSI data for agricultural applications through DL approaches, a pressing need exists for a comprehensive survey that can effectively navigate researchers through the significant strides achieved and the future promising research directions in this domain. This literature review diligently compiles, analyzes, and discusses recent endeavours employing DL methodologies. These methodologies encompass a spectrum of approaches, ranging from Autoencoders (AE) to Convolutional Neural Networks (CNN) (in 1D, 2D, and 3D configurations), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), Transfer Learning (TL), Semi-Supervised Learning (SSL), Few-Shot Learning (FSL) and Active Learning (AL). These approaches are tailored to address the unique challenges posed by agricultural HSI analysis. This review evaluates and discusses the performance exhibited by these diverse approaches. To this end, the efficiency of these approaches has been rigorously analyzed and discussed based on the results of the state-of-the-art papers on widely recognized land cover datasets. Github repository.

Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile robotics. Current state-of-the-art point cloud semantic segmentation methods rely heavily on the availability of 3D laser scanning data. This is problematic in regards of low-latency, real-time applications that use data from high-precision mobile laser scanners, as those are typically 2D line scanning devices. In this study, we experiment with real-time semantic segmentation of high-density multispectral point clouds collected from 2D line scanners in urban environments using encoder - decoder convolutional neural network architectures. We introduce a rasterized multi-scan input format that can be constructed exclusively from the raw (non-georeferenced profiles) 2D laser scanner measurement stream without odometry information. In addition, we investigate the impact of multispectral data on the segmentation accuracy. The dataset used for training, validation and testing was collected with multispectral FGI AkhkaR4-DW backpack laser scanning system operating at the wavelengths of 905 nm and 1550 nm, and consists in total of 228 million points (39 583 scans). The data was divided into 13 classes that represent various targets in urban environments. The results show that the increased spatial context of the multi-scan format improves the segmentation performance on the single-wavelength lidar dataset from 45.4 mIoU (a single scan) to 62.1 mIoU (24 consecutive scans). In the multispectral point cloud experiments we achieved a 71 % and 28 % relative increase in the segmentation mIoU (43.5 mIoU) as compared to the purely single-wavelength reference experiments, in which we achieved 25.4 mIoU (905 nm) and 34.1 mIoU (1550 nm). Our findings show that it is possible to semantically segment 2D line scanner data with good results by combining consecutive scans without the need for odometry information. The results also serve as motivation for developing multispectral mobile laser scanning systems that can be used in challenging urban surveys.

CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cost, and instrument compactness constraints, CNES asked the French hyperspectral Mission Advisory Group (MAG), representing a broad French scientific community, to provide recommendations on spectral sampling, particularly in the Short Wave InfraRed (SWIR) for various applications.This paper presents the tests carried out with the aim of defining the optimal spectral sampling and spectral resolution in the SWIR domain for quantitative estimation of physical variables and classification purposes. The targeted applications are geosciences (mineralogy, soil moisture content), forestry (tree species classification, leaf functional traits), coastal and inland waters (bathymetry, water column, bottom classification in shallow water, coastal habitat classification), urban areas (land cover), industrial plumes (aerosols, methane and carbon dioxide), cryosphere (specific surface area, equivalent black carbon concentration), and atmosphere (water vapor, carbon dioxide and aerosols). All the products simulated in this exercise used the same CNES end-to-end processing chain, with realistic instrument parameters, enabling easy comparison between applications. 648 simulations were carried out with different spectral strategies, radiometric calibration performances and signal-to-noise Ratios (SNR): 24 instrument configurations × 25 datasets (22 images + 3 spectral libraries).The results show that spectral sampling up to 20 nm in the SWIR range is sufficient for most applications. However, 10 nm spectral sampling is recommended for applications based on specific absorption bands such as mineralogy, industrial plumes or atmospheric gases. In addition, a slight performance loss is generally observed when radiometric calibration accuracy decreases, with a few exceptions in bathymetry and in the cryosphere for which the observed performance is severely degraded. Finally, most applications can be achieved with a realistic SNR, with the exception of bathymetry, shallow water classification, as well as carbon dioxide and methane estimation, which require the optimistic SNR level tested. On the basis of these results, CNES is currently evaluating the best compromise for designing the future hyperspectral sensor to meet the objectives of priority applications.