Manual Digitization Research Articles

Video-Based System for Automatic Measurement of Barbell Velocity in Back Squat.

Velocity-based training is a contemporary method used by sports coaches to prescribe the optimal loading based on the velocity of movement of a load lifted. The most employed and accurate instruments to monitor velocity are linear position transducers. Alternatively, smartphone apps compute mean velocity after each execution by manual on-screen digitizing, introducing human error. In this paper, a video-based instrument delivering unattended, real-time measures of barbell velocity with a smartphone high-speed camera has been developed. A custom image-processing algorithm allows for the detection of reference points of a multipower machine to autocalibrate and automatically track barbell markers to give real-time kinematic-derived parameters. Validity and reliability were studied by comparing the simultaneous measurement of 160 repetitions of back squat lifts executed by 20 athletes with the proposed instrument and a validated linear position transducer, used as a criterion. The video system produced practically identical range, velocity, force, and power outcomes to the criterion with low and proportional systematic bias and random errors. Our results suggest that the developed video system is a valid, reliable, and trustworthy instrument for measuring velocity and derived variables accurately with practical implications for use by coaches and practitioners.

A Comparative Study of the different remote sensing techniques for evaluating land use/ cover in Basra city, Iraq.(Dept. C)

Basra governorate represents one of the most important governorates in Iraq, where it is the only seaport for exporting oil and agriculture products. However, this governorate is facing serious land degradation problems results in major changes in land use/ cover (LULC) within the area. Accordingly, the main objective of this study was to evaluate LULC in Basra city using different techniques on remotely sensed data. For that purpose, Landsat 8, Sentinel 2A images were used in 2018 to study extent of urban areas, agricultural lands, water bodies and bare lands areas in Basra city center. The studied techniques include: the supervised classification in three methods (minimum distance, maximum likelihood, and Mahalanobis distance), spectral indices, manual digitizing of features and land surface temperature (LST). These methods were applied on both Sentinel 2A and Landsat data at three spatial resolutions 10, 15 and 30 m, respectively. The obtained results indicated that the minimum distance technique has the highest accuracy in identifying LULC when compared with the other classification methods. It was found that the higher the spatial resolution the higher the accuracy of the results. The spectral indices were more accurate than the classification methods in identifying agricultural areas and water bodies. There was a higher inclusion between urban areas and bare lands due to the similarity in their spectral reflectance. Accordingly, it is recommended to manually digitize urban areas than classifying it. The LST can be used as an indirect and fairly accurate method for evaluating LULC in Al-Basra city. In conclusion, remote sensing data and techniques could help in providing more accurate information about LULC in Basra City to be used in its future planning and sustainable development

Convergent Evolution of Locomotory Modes in Euarchontoglires

The research of phenotypic convergence is of increasing importance in adaptive evolution. Locomotory modes play important roles in the adaptive evolution of species in the Euarchontoglires, however, the investigation of convergent evolution of the locomotory modes across diverse Euarchontoglire orders is incomplete. We collected measurements of three phalangeal indices of manual digit III, including metacarpal of digit III (MC3), manus proximal phalanx of digit III (MPP3), and manus intermediate phalanx of digit III (MIP3), from 203 individuals of 122 Euarchontoglires species representing arboreal (orders Scandentia, Rodentia, and Primates), terrestrial (orders Scandentia and Rodentia), and gliding (orders Dermoptera and Rodentia) locomotory modes. This data can be separated into seven groups defined by order and locomotory mode. Based on combination of the three phalangeal indices, the Principle component analyses (PCA), phylomorphospace plot, and C-metrics analyses clustered the arboreal species of Scandentia, Rodentia, and Primates together and the terrestrial species of Scandentia and Rodentia together, showing the convergent signal in evolution of the arboreal (C1 = 0.424,P< 0.05) and terrestrial (C1 = 0.560,P< 0.05) locomotory modes in Euarchontoglires. Although the gliding species from Dermoptera and Rodentia did not cluster together, they also showed the convergent signal (C1 = 0.563,P< 0.05). Our work provides insight into the convergent evolution of locomotory modes in Euarchontoglires, and reveals that these three indices contribute valuable information to identify convergent evolution in Euarchontoglires.

Using object-based image analysis to map commercial poultry operations from high resolution imagery to support animal health outbreaks and events.

Precise locations of commercial poultry operations are important to planning and response for animal health outbreaks and events. These data are not available nationally or uniformly in the United States. This project uses machine learning capabilities to identify and map commercial poultry operations from aerial imagery in seven south-eastern states in the United States. The output protocol uses an Object-Based Image Analysis (OBIA) approach, which identifies objects based on spectral signatures combined with spatial, contextual, and textural information. The protocol is a semi-automated and user-assisted process, meaning that the object identification routines require minimal user inputs or expertise. Using the protocol, we produced locations of likely commercial poultry operations in up to two counties in one workday, about two times faster than manual digitisation. The resulting datasets provide an estimate of the number and geographic distribution of commercial poultry operations to assist outbreak response by augmenting available knowledge in affected areas.

Exploiting a texture framework and high spatial resolution properties of panchromatic images to generate enhanced multi-layer products: Examples of Pleiades and historical CORONA space photographs

ABSTRACT Remotely sensed high spatio-temporal resolution panchromatic images have been extensively used globally to visually detect and interpret changes in landscape components, create land cover maps via the on-screen manual digitization, and to pan-sharpen multi-spectral images among other uses. Despite this attractive array of uses, lack of distinct spectral signatures for panchromatic images from surface elements, e.g. landscape cover types, creates a drawback in their exploitation during any automated classification process, hence limiting their use in the field of remote sensing for land use/land cover change studies. Moreover, the complexities of some panchromatic data (e.g. CORONA) on the one hand, and the traditional texture computation approach on the other hand present additional hurdles in utilizing panchromatic images. This contribution looks at the possibility of exploiting panchromatic images (e.g. Pleiades and historical CORONA products) for remote sensing applications by (i) proposing a new approach that optimizes and generates new multi-layer datasets from panchromatic images that could be useful, e.g. in image classification analysis, (ii)exploiting the combinatorial texture approach to enhance the products generated by the framework in (i) above, and (iii) assessing the capability of the proposed method to handle complex datasets exemplified, e.g. by CORONA. To evaluate the approach, Kurdistan, Iran and Syria regions are selected for study employing the maximum likelihood classification (MLC) scheme. The MLC results indicate an increase in overall accuracy and Kappa coefficient by 32% and 0.42 (compared to raw CORONA image), and 21% and 0.28 (compared to raw Pleiades image). For Iran and Syria, compared to the raw CORONA image, the MLC results show increase by 35% and 0.47, and 42% and 0.56, respectively. Furthermore, based on the results of the accuracy assessment that show an overall accuracy of 85% and Kappa coefficient of 0.80 for Kurdistan, 94% and 0.92 for Iran, and 96% and 0.95 for Syria, the proposed method can be said to have the potential of handling complex panchromatic datasets such as CORONA.

Deep learning-based digitization of prostate brachytherapy needles in ultrasound images.

PurposeTo develop, and evaluate the performance of, a deep learning‐based three‐dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) algorithm aimed at finding needles in ultrasound images used in prostate brachytherapy.MethodsTransrectal ultrasound (TRUS) image volumes from 1102 treatments were used to create a clinical ground truth (CGT) including 24422 individual needles that had been manually digitized by medical physicists during brachytherapy procedures. A 3D CNN U‐net with 128 × 128 × 128 TRUS image volumes as input was trained using 17215 needle examples. Predictions of voxels constituting a needle were combined to yield a 3D linear function describing the localization of each needle in a TRUS volume. Manual and AI digitizations were compared in terms of the root‐mean‐square distance (RMSD) along each needle, expressed as median and interquartile range (IQR). The method was evaluated on a data set including 7207 needle examples. A subgroup of the evaluation data set (n = 188) was created, where the needles were digitized once more by a medical physicist (G1) trained in brachytherapy. The digitization procedure was timed.ResultsThe RMSD between the AI and CGT was 0.55 (IQR: 0.35–0.86) mm. In the smaller subset, the RMSD between AI and CGT was similar (0.52 [IQR: 0.33–0.79] mm) but significantly smaller (P < 0.001) than the difference of 0.75 (IQR: 0.49–1.20) mm between AI and G1. The difference between CGT and G1 was 0.80 (IQR: 0.48–1.18) mm, implying that the AI performed as well as the CGT in relation to G1. The mean time needed for human digitization was 10 min 11 sec, while the time needed for the AI was negligible.ConclusionsA 3D CNN can be trained to identify needles in TRUS images. The performance of the network was similar to that of a medical physicist trained in brachytherapy. Incorporating a CNN for needle identification can shorten brachytherapy treatment procedures substantially.

Improving data acquisition speed and accuracy in sport using neural networks

ABSTRACT Video analysis is used in sport to derive kinematic variables of interest but often relies on time-consuming tracking operations. The purpose of this study was to determine speed, accuracy and reliability of 2D body landmark digitisation by a neural network (NN), compared with manual digitisation, for the glide phase in swimming. Glide variables including glide factor; instantaneous hip angles, trunk inclines and horizontal velocities were selected as they influence performance and are susceptible to digitisation propagation error. The NN was “trained” on 400 frames of 2D glide video from a sample of eight elite swimmers. Four glide trials of another swimmer were used to test agreement between the NN and a manual operator for body marker position data of the knee, hip and shoulder, and the effect of digitisation on glide variables. The NN digitised body landmarks 233 times faster than the manual operator, with digitising root-mean-square-error of ~4-5 mm. High accuracy and reliability was found between body position and glide variable data between the two methods with relative error ≤5.4% and correlation coefficients >0.95 for all variables. NNs could be applied to greatly reduce the time of kinematic analysis in sports and facilitate rapid feedback of performance measures.

Lift devices in the flight of Archaeopteryx

Archaeopteryx has played a central role in the debates on the origins of avian (and dinosaurian) flight, even though as a flier it probably represents a relatively late stage in the beginnings of flight. We report on aerodynamic tests using a life-sized model of Archaeopteryx performing in a low turbulence wind tunnel. Our results indicate that tail deflection significantly decreased take-off velocity and power consumption, and that the first manual digit could have functioned as the structural precursor of the alula. Such results demonstrate that Archaeopteryx had already evolved high-lift devices, which are functional analogues of those present in today’s birds

Automatic multi-needle localization in ultrasound images using large margin mask RCNN for ultrasound-guided prostate brachytherapy

Multi-needle localization in ultrasound (US) images is a crucial step of treatment planning for US-guided prostate brachytherapy. However, current computer-aided technologies are mostly focused on single-needle digitization, while manual digitization is labor intensive and time consuming. In this paper, we proposed a deep learning-based workflow for fast automatic multi-needle digitization, including needle shaft detection and needle tip detection. The major workflow is composed of two components: a large margin mask R-CNN model (LMMask R-CNN), which adopts the lager margin loss to reformulate Mask R-CNN for needle shaft localization, and a needle based density-based spatial clustering of application with noise algorithm which integrates priors to model a needle in an iteration for a needle shaft refinement and tip detections. Besides, we use the skipping connection in neural network architecture to improve the supervision in hidden layers. Our workflow was evaluated on 23 patients who underwent US-guided high-dose-rate (HDR) prostrate brachytherapy with 339 needles being tested in total. Our method detected 98% of the needles with 0.091 ± 0.043 mm shaft error and 0.330 ± 0.363 mm tip error. Compared with only using Mask R-CNN and only using LMMask R-CNN, the proposed method gains a significant improvement on both shaft error and tip error. The proposed method automatically digitizes needles per patient with in a second. It streamlines the workflow of transrectal ultrasound-guided HDR prostate brachytherapy and paves the way for the development of real-time treatment planning system that is expected to further elevate the quality and outcome of HDR prostate brachytherapy.

Three-dimensional architecture of rabbit M. soleus during growth

Muscle architecture has a significant influence on the mechanical properties of skeletal muscles. Important parameters include the fascicle length, the angle of pennation, the physiological cross-sectional area (PCSA) as well as aponeurosis and tendon dimensions. During growth, skeletal muscles have to react to an increasing body mass and size demanding adaptations in muscle dimensions. Investigations of muscle architectural changes during growth are sparse, and existing studies often confine their scope to specific parameters or regions of the muscle. For this cross-sectional study, we determined the entire three-dimensional fascicle architecture of rabbit M. soleus via manual digitization. To this end, the investigations covered nine rabbits in the age-range between 29 days and 109 days. Fascicle length, muscle belly length, and aponeurosis length increased by 40%, 107%, and 111%, respectively. As the pennation angle remained almost constant and the contribution of fascicle length growth to muscle belly growth was minor, the increase in muscle mass primarily led to an increase in PCSA (462%), which required a similar increase in aponeurosis area (434%). Results gain new insight into the build-up of rabbit M. soleus and reveal that increases in muscle belly length are primarily connected to increases in aponeurosis length (83%). Contributions from fascicle length increase (17%) only play a minor role.

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

VERIFICATION OF MANUAL DIGITIZATION METHODS DURING EXPERIMENTAL SIMULATION OF KNEE MOTION.

Cadaveric testing is a common approach for verifying mathematical models used in computational modeling work. In the case of a knee joint model for calculating ligament tension during total knee replacement (TKR) motion, model inputs include rigid body motions defined using the Grood-Suntay coordinate system as a spatial linkage between the tibial component orientation relative to the femoral component. Using this approach requires the definition of coordinate systems for each rigid TKR component (i.e. tibial and femoral) based on fiducial points, manual digitization of a point cloud within the experimental setup, and registration of the orientation relative to the relevant bone marker array. The purpose of this study was to compare the variability between two different manual point digitization methods (a hand-held stylus and pivot tool each calibrated in the optical tracking system), using a TKR femoral component in a simulated cadaver limb experimental setup as an example. This was accomplished by verifying the mathematical algorithm used to calculate the coordinate system from the digitized points, quantifying the variability of the manual digitization methods, and discussing how any error could affect the computational model. For the hand-held stylus method, the standard deviation of the origin and, x-, y-, and z-axis calculations were 0.50mm, 1.31 degrees, 0.51 degrees, and 0.62 degrees, respectively. It is important to note that there is an additional error created using the hand-held stylus from required manual digitization of each rigid marker array. This average additional error was 0.54mm for the origin and 1.70, 1.66, and 0.98 degrees for the x-, y-, and z-axes, respectively. For the pivot tool method, the standard deviation was 0.35mm, 0.37 degrees, 1.27 degrees, and 1.24 degrees for the origin, x-, y-, and z-axes, respectively. It is essential to minimize experimental error, as small errors in alignment can substantially alter model outputs. In this study of cadaver simulation of limb motion, the pivot tool is the better option for minimizing error. Careful definition of fiducial points and repeatable manual digitization of the point cloud is critical for meaningful computational models of TKR motion based on cadaver experimental work.

The Effectiveness of the Semi-Automatic Technique in Extracting Shoreline from Digital Elevation Models for Sustainable Development in Africa

The accurate delineation of Shorelines is required to determine the respective tidal heights and monitor coastal erosion. In some coastlines, the difference between the high tide and the low tide could be as small as one meter but the accurate extraction of the respective vertical surfaces is required to understand the pattern of the sea wave energy and to chart tidal heights relative to a specific datum. The manual digitizing of shoreline is usually with high accuracies and flexibilities but could be rigorous and hectic especially for longer shorelines. Automatic extraction is more feasible where the wavelength intensity gradient within a sub-pixel level is at maximum for instantaneous shorelines with small tidal range. This is due to topological inconsistencies that could hinder such accuracies. In this study, an improved mean shift segmentation model extracts shorelines from digital elevation models from LiDAR and a UAV survey. The key element of an accurate DTM is the contouring network capable of delineating between the grid pixels each representing water and land whose elevation is below or above tidal datum. The study locations are the Freshwater and the Perranporth bay in the United Kingdom. The third is the Nigeria Maritime Academy Oron shoreline. The choice for the different study areas is to ascertain the reliability of this technique on different geographies. Comparison between manual digitizing and the mean shift segmentation were both by visual interpretation and by volumetric change analysis for two different years. The results obtained indicate that the mean shift segmentation can delineate tide-coordinated shorelines accurately. The limitation of this methodology is on digital images with poor spectral resolution. Recommendations include the use of this technique on open source GIS software and a practical solution to developing a monitoring infrastructure for coastlines in Africa.

Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images

Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue and thermal imaging, as well as photogrammetric canopy height modelling (CHM) were applied. Images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. Image objects obtained were classified using random forest classification modelling based on objects’ attributes. The classification model was employed to create lupine distribution maps of test areas, and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89%. The maximum difference in lupine area between classified and digitised lupine maps was 5%. Moreover, the pixel-wise map comparison showed that 88% of all pixels matched between classified and digitised maps. Our results indicated that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

Supra-glacial debris cover is key to glacier ablation through increasing (thin debris layer) or decreasing (thick debris layer) melt rates, thereby regulating the mass balance of a glacier and its meltwater runoff. The thickening or lateral expansion of supra-glacial debris cover correlates with a reduction of glacier ablation and, consequently, runoff generation, which is also considered to be an influential factor on the rheology and dynamics of a glacierized system. Studies on supra-glacial debris cover have recently attracted wide attention especially for glaciers in the Himalayas and Karakoram, where the glaciers have heterogeneously responded to climate change. In this study, we used 32 images from the Landsat Thematic Mapper, Enhanced Thematic Mapper Plus and Operational Land Imager archive, going back to 1990, which are available on the Google Earth Engine cloud-computing platform, to map the supra-glacial debris cover in the Hunza Valley, Karakoram, Pakistan based on a band ratio segmentation method (normalized difference snow index [NDSI] < 0.4), Otsu thresholding and machine learning algorithms. Compared with manual digitization, the random forest (RF) model was found to have the greatest accuracy in identifying supra-glacial debris, with a Kappa coefficient of 0.94 ± 0.01 and an overall accuracy of 95.5 ± 0.9%. Overall, the supra-glacial debris cover in the study area showed an increasing trend, and the total area expanded by 8.1–21.3% for various glaciers from 1990 to 2019. The other two methods (Otsu thresholding and NDSI < 0.4) generally overestimated the supra-glacial debris covered area, by 36.3% and 18.8%, respectively, compared to that of the RF model. The supra-glacial debris cover has migrated upward on the glaciers, with intensive variation near the equilibrium-line altitude zone (4500–5500 m a.s.l.). The increase in ice or snow avalanche activity at high altitudes may be responsible for this upward expansion of supra-glacial debris cover in the Hunza Valley, which is attributed to the combined effect of temperature decrease and precipitation increase in the study area.

Validity of Using Automated Two-Dimensional Video Analysis to Measure Continuous Sagittal Plane Running Kinematics.

Two-dimensional video analysis is commonly used to assess kinematics when three-dimensional motion capture is unavailable. However, videos are often assessed using manual digitization, which limits the ability to extract outcomes that require continuous data. Here, we introduced a method to collect continuous kinematic data in 2D using an inexpensive camera and an open-source automated marker tracking program. We tested the validity of this method by comparing 2D video analysis to 3D motion capture for measuring sagittal-plane running kinematics. Twenty uninjured participants ran on a treadmill for 1-min while lower extremity kinematics were collected simultaneously in 3D using a motion capture system and in 2D using a single digital camera, both at 120Hz. Knee, ankle, and foot angle at contact, peak knee flexion, knee flexion excursion, and knee-ankle flexion vector coding variability were computed using both the 3D and 2D kinematic data, and were compared using intraclass correlation coefficients and Bland-Altman plots. The agreement between collection methods was excellent for foot angle at contact and knee flexion excursion, good for ankle and knee angle at contact and knee-ankle vector coding variability, and moderate for peak knee flexion. However, Bland-Altman plots revealed significant differences between the 2D and 3D collection methods, which varied across study participants. These low-cost methods could be useful for collecting continuous sagittal plane running kinematics in non-laboratory settings.

Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms

ABSTRACT LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field – often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8%. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3% and Sentinel-2 a mean overall accuracy of 83.6%. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4%. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.

Trierarchuncus prairiensis gen. et sp. nov., the last alvarezsaurid: Hell Creek Formation (uppermost Maastrichtian), Montana

The enigmatic Alvarezsauria (Dinosauria: Theropoda) are characterized by extremely short forelimbs with a single functional digit bearing a large, robust ungual. Alvarezsauria are first recorded from the Jurassic of China, but are otherwise mostly known from the Cretaceous of South America and Asia, including a number of relatively complete skeletons. North America has yielded only a fragmentary skeleton from the lower Maastrichtian Horseshoe Canyon Formation, Alberta, and a pubis, partial ischium, and metatarsal from the upper Maastrichtian Hell Creek Formation, Montana, and Lance Formation, Wyoming. Here we describe new alvarezsaurid material (Trierarchuncus prairiensis gen. et sp. nov.) from the Hell Creek Formation, Montana, comprising a metatarsal III, distal end of a radius, and three manual digit (MD)-I unguals, which form a progressive size series. One MD-I ungual is the most complete yet described, and is much more curved than typically depicted for Alvarezsauridae. Manual D-I unguals are of particular interest as they undergo a number of changes within the clade, including enclosure of the ventral blood vessel groove, development of a ventral sulcus, and increased robusticity and rugosity. Comparison among the new specimens suggests that these features also develop ontogenetically, which may have taxonomic implications. Stratigraphic data shows that alvarezsaurids occur through most of the ~85 m thick Hell Creek Formation, with the uppermost specimen occurring ~10 m below the upper contact with the Fort Union Formation. As such these are the youngest known alvarezsaurid remains and demonstrate that the clade survived at least until ~1–200 kyrs before the Cretaceous–Paleogene mass extinction.

Mapping and spatial statistical analysis of Mars Yardangs

Mars yardang fields were mapped using the newly available high-resolution Context Camera (CTX) mosaics (30°N to 30°S) through visual interpretation and manual digitization. We classified the Martian yardangs into three types according to their shapes: long ridge, inverted hull and curvilinear shapes, and found that the number of long ridge yardang fields account for 77.97% of all 394 yardang fields. The mapping results show that the total area of yardang fields is about 734,115 km2, within which the area of the Medusae Fossae Formation (MFF) yardangs is about 562,640 km2. We also identified and digitized individual yardang bodies wider than 500 m in the CTX mosaics. The resultant 466 yardang bodies are subsequently used as samples for statistical analysis of the morphometric parameters. Among all of the orientations, N–S is the dominant orientation of the yardang samples, and the average length and width are 5626 m and 993 m respectively. The length to width ratios vary from 1.2:1 to 46.9:1 within (30°N to 30°S) and vary from 1.5:1 to 46.9:1 in the most studied MFF. The mapping and statistical results provide important information about the spatial distribution of Mars yardangs and their morphological characteristics, which will be valuable to further study of the formation and evolution of Mars yardangs, and the aeolian environment.

Coastal settlement patterns and exposure to sea-level rise in the Jaffna Peninsula, Sri Lanka

The Jaffna Peninsula in Sri Lanka has a generally flat topography with a median elevation of 2.72 m, and thus faces a high risk from sea-level rise that has the potential to have adverse impacts on the livelihoods of coastal communities. Understanding these risks and identifying the regions that could be adversely impacted is critical for planning future settlements and developing preventative protocols where possible. The aim of this study was to analyze the exposure of coastal settlements of the Jaffna Peninsula to climate risks, particularly to sea-level rise, and to identify the areas that are likely to be impacted under different sea-level rise scenarios. Raster-based sea-level rise modeling was performed with a digital elevation model produced with topographic contours and spot heights. The spatial distribution of individual residential houses for the entire Jaffna Peninsula was obtained through manual digitization using virtual globe platforms and high-resolution satellite images, and the houses exposed to inundation under various Representative Concentration Pathways from 2025 to 2100 were identified. The results showed that a majority (55.5%) of the residential buildings in the Jaffna Peninsula are located within 3 m above sea level. Approximately 5554 (5.6%) of the houses were projected to be inundated by 2100, and this projection increased to approximately 25,074 (25.4%) under high tide scenarios. This study highlights the coastal communities with a high level of exposure to coastal inundation where adaptation planning is essential. These results provide insights for coastal managers and policy makers for future planning of new settlements and urban expansion.