Grid-based Sampling Research Articles

Spatial assessment of soil resources on different land uses and slope gradient as a planning tool, in Ferenjua watershed, Ethiopia

Soil erosion, land degradation and climate change are the major environmental threats attributed to low agricultural productivity in Ethiopia. To reduce soil erosion and restore degraded lands, integrated soil and water conservation interventions have been implemented. However, the impacts of the intervention measures were not sufficiently studied due to the lack of baseline information. Therefore, this study was carried out to evaluate and understand the current conditions of biophysical resources in Ferenjua watershed, which is located in Gondar zuria district. In this study, more emphasis was given to the levels of total nitrogen (TN), available phosphorous (P), organic matter (OM), bulk density (BD), pH, moisture, texture and soil erosion. To collect the biophysical data, the watershed was divided into 144 grid-based sampling points with a resolution of 250 m by 250 m. In addition, the data were collected in 8 representative profile pits. Three primary land uses were identified from supervised land use and land cover classification and described as cultivated land (55.08 %), forestland (30.80 %), and grassland (14.12 %). Erosion hotspot map shows that >40 % of the watershed is moderately and highly susceptible to soil erosion. The highest BD (1.36 gm cm−3) was observed at Vertisols-dominated cultivated lands located in the flatlands, whereas, the lowest BD (1.28 gm cm−3) was observed under forestland. Regarding the results of TN, P, and OM, >60 % of the watershed was classified as having low to very low soil nutrient levels. Consequently, development interventions should be considered to restore degraded lands for the sustained use of resources in the watershed.

Effect of scatter suppression with 2D antiscatter grids in photon counting compact CBCT.

Energy sensitive and photon counting detectors can provide improved tissue visualization and material quantification capabilities in Cone Beam Computed Tomography (CBCT) systems. However, their implementation in CBCT systems is more challenging, which is in part due to high fluence of scattered X-rays in wide cone angle CBCT geometry. Specifically, high scatter contamination in lower energy spectrum challenges reconstruction of high fidelity CBCT images by using lower energy X-rays. To address this problem, we investigated a robust scatter rejection with 2D antiscatter grids in a benchtop photon counting and compact CBCT system. The benchtop system employs a 35 cm wide CdTe photon counting detector with two energy thresholds. To reject scatter, a dedicated 2D antiscatter grid (2D grid) prototype made from tungsten was developed and mounted directly on the detector. To correct residual scatter not stopped by the 2D grid, a measurement-based scatter correction method, referred to as Grid-based Scatter Sampling (GSS), was utilized. Without 2D grid, scatter to primary ratio (SPR) reached 2.3 in the 15-40 keV energy bin. SPR was factor of 3 higher in the lowest energy bin when compared to the highest energy bin (90-120 keV). With the 2D grid, SPR was reduced below 0.14, and SPR values were more homogenous across the energy spectrum. CT number nonuniformity was factor of 3 lower in both low and high energy bin CBCT reconstructions. Improvement in contrast to noise ratio and contrast was more pronounced in the low energy bin CBCT images. This work indicates that 2D grids can significantly reduce spectral contamination caused by scatter in photon counting compact CBCT, and potentially enable higher fidelity CBCT image reconstructions.

GA-Net: Accurate and Efficient Object Detection on UAV Images Based on Grid Activations

Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle these issues, we propose GA-Net, a novel approach tailored for UAV images. The key innovation includes the Grid Activation Module (GAM), which efficiently calculates grid activations, the probability of foreground presence at grid scale. With grid activations, the GAM helps filter out patches without objects, minimize redundant computations, and improve inference speeds. Additionally, the Grid-based Dynamic Sample Selection (GDSS) focuses the model on discriminating positive samples and hard negatives, addressing background bias during training. Further enhancements involve GhostFPN, which refines Feature Pyramid Network (FPN) using Ghost module and depth-wise separable convolution. This not only expands the receptive field for improved accuracy, but also reduces computational complexity. We conducted comprehensive evaluations on DGTA-Cattle-v2, a synthetic dataset with added background images, and three public datasets (VisDrone, SeaDronesSee, DOTA) from diverse domains. The results prove the effectiveness and practical applicability of GA-Net. Despite the common accuracy and speed trade-off challenge, our GA-Net successfully achieves a mutually beneficial scenario through the strategic use of grid activations.

Status of Soil Quality in the Vicinity of Artisanal Refining Sites in Rivers State, Nigeria

The environmental quality and sustainability in the Niger Delta region are severely undermined by the increasing practice of artisanal crude oil refining. This study aimed to assess the impact artisanal crude oil refining on soil nutrients stability vis-à-vis plant/vegetal resources of farmlands. Farmlands in Elele-Alimini and Ibaa in Emohua Local Government Area (LGA), Ogbodo in Ikwerre LGA and Umuanyagu (control) in Etche LGA, Rivers State (Nigeria) were sampled in this study. A total of fifty (50) sampling points in both test and control locations, were randomly selected using a standard spatial (grid-based) sampling technique. Soil and plant samples within the farmlands were collected to determine the physicochemical parameters and macronutrient contents. During the dry and wet seasons, mean values of pH, EC and moisture content in test soil were in the ranges 4.60-4.85 and 4.55-4.79; 66.67-130.0 µS/cm and 31.3-33 µS/cm, and 7.21-11.49 % and 11.71-66 %, respectively. The values of pH, EC and moisture content in the control soil ranged from 4.78-4.84 and 4.81-5.14; 130-152 µS/cm and 31.5-33.0 µS/cm, 11.86-11.88% and 63-66% respectively. Electrical conductivity and pH of soil showed almost a similar trend (Control > Ogbodo > Ibaa > Elele Alimini) for both top and sub-soils and in both seasons. During the dry and wet seasons, mean values of nitrogen (N), phosphorus (P), potassium (K), Total Organic Carbon (TOC) and Soil Organic Matter (SOM) in test soils were in the ranges 0.11-0.17% and 0.11-0.18 %; 0.13-0.23 mg/kg and 0.04-0.06 mg/kg; 22.82-51.87 mg/kg and 14.23-35.60 mg/kg; 1.30-2.00% and 1.36-2.0%, and 2.22-3.45% and 2.24-3.16, respectively. Mean values of N, P, K, TOC and SOM in control soils during the dry and wet seasons were in the ranges 0.12-0.19% and 0.14%; 0.29-0.33 mg/kg and 0.08-0.09 mg/kg; 50.33-52.18 mg/kg and 42.75-50.24 mg/kg; 1.35-2.14 and 1.83-2.08%, and 2.53-3.70% and 2.12-2.85%, respectively. The levels of N, P, K, TOC and SOM in the farmlands were low, and could result in poor crop growth yield.

A quantitative CBCT pipeline based on 2D antiscatter grid and grid-based scatter sampling for image-guided radiation therapy.

Quantitative accuracy is critical for expanding the role of cone beam CT (CBCT) imaging from target localization to quantitative treatment monitoring and plan adaptations in radiation therapy. Despite advances in CBCT image quality improvement methods, quantitative accuracy gap between CBCT and multi-detector CT (MDCT) remains. In this work, a physics-driven approach was investigated that combined robust scatter rejection, raw data correction and iterative image reconstruction to further improve CBCT image quality and quantitative accuracy, referred to as quantitative CBCT (qCBCT). QCBCT approach includes tungsten 2D antiscatter grid hardware, residual scatter correction with grid-based scatter sampling, image lag, and beam hardening correction for offset detector geometry linac-mounted CBCT. Images were reconstructed with iterative image reconstruction to reduce image noise. qCBCT was evaluated using a variety of phantoms to investigate the effect of object size and its composition on image quality, and image quality was benchmarked against clinical CBCT and gold standard MDCT images used for treatment planning. QCBCT provided statistically significant improvement in CT number accuracy and reduced image artifacts when compared to clinical CBCT images. When compared to gold standard MDCT, mean HU errors in qCBCT and clinical CBCT were 17±9 and 38±29 HU, respectively. Magnitude of phantom size dependent HU variations were comparable between MDCT and qCBCT images. With iterative reconstruction, contrast-to-noise ratio improved by 25% when compared to clinical CBCT protocols. Combination of novel scatter suppression techniques and other data correction methods in qCBCT provided CT number accuracy comparable to gold standard MDCT used for treatment planning. This approach may potentially improve CBCT's promise in fulfilling the tasks that demand high quantitative accuracy, such as online dose calculations and treatment response assessment, in image guided radiation therapy.

Heavy metal and metalloid concentrations in agricultural communities around steel and iron industries in Uganda: implications for future food systems

ABSTRACT Poor management of effluents from steel and iron industries could increase element concentrations in the environment and threaten the health of consumers of food products from these areas. The current study assessed element concentration and physicochemical properties of soils, water, and vegetation from within 200 m around three steel and iron industries. A workable grid-based sampling design guided soil and plant sampling; upstream-downstream water quality comparisons were adapted with upstream as a control sample. Analyses were conducted following procedures by USEPA method 3051A. Element concentration was in the order Mn>As>Zn>Cr>Pb. The concentrations reduced with increase in distance from the industry. There was moderate-to-strong pollution of soils for As, and transfer factor for all elements was >1. In conclusion, industrial activity might have contributed to increased element concentrations in the soils, vegetation, and water sources around the industries. Agrarian activities may only be safely carried out 151m away from the industries.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites.

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

Reconsidering Urban Densification for Microclimatic Improvement: Planning and Design Strategies for Istanbul

One of the key issues of the urban planning agenda is how urban density be decided in the spatial configurations of future neighbourhoods to overcome complex challenges such as urban warming. This paper aims to reconsider urban density as a spatial planning instrument to develop effective densification policies, planning and design strategies in terms of surface urban heat island (SUHI) mitigation in Istanbul. The quantitative research embraced a four-stage methodology including grid-based sampling design, decoding the taxonomy of urban density-matrix (UDM), land surface temperature mapping, and ANOVA tests. Tests were conducted on the UDM consisting of nine building typologies representing the horizontal and vertical urban density. The research indicated that the impact of urbanisation on SUHI can be mitigated by controlling densities and urban forms based on quantitative findings. The highest temperatures were recorded in areas with high-coverage-mid-rise and mid-coverage-mid-rise development. The different levels of SUHI in different building typologies having the same density indicated the mitigation potentials of the built-form in Istanbul's local urban warming. Low coverage and high-rise building forms were an optimal solution for mitigating SUHI in densely populated urban areas. The research gives insight into an ongoing debate among urban professionals in Istanbul concerning the impacts of density and the urban form for climate adaptation. It enables professionals to understand the impact of urban planning decisions on microclimate and integrate them into the operational processes. Considering quantitative research findings as a strong foundation for developing policy recommendations and using them as a guideline may create new opportunities for researchers, practitioners, and policymakers. The study has an original value for exploring design strategies to improve microclimate and promoting sustainable urban development.

Automated segmentation of five different body tissues on computed tomography using deep learning.

To develop and validate a computer tool for automatic and simultaneous segmentation of five body tissues depicted on computed tomography (CT) scans: visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. A cohort of 100 CT scans acquired on different subjects were collected from The Cancer Imaging Archive-50 whole-body positron emission tomography-CTs, 25 chest, and 25 abdominal. Five different body tissues (i.e., VAT, SAT, IMAT, SM, and bone) were manually annotated. A training-while-annotating strategy was used to improve the annotation efficiency. The 10-fold cross-validation method was used to develop and validate the performance of several convolutional neural networks (CNNs), including UNet, Recurrent Residual UNet (R2Unet), and UNet++. A grid-based three-dimensional patch sampling operation was used to train the CNN models. The CNN models were also trained and tested separately for each body tissue to see if they could achieve a better performance than segmenting them jointly. The paired sample t-test was used to statistically assess the performance differences among the involved CNN models RESULTS: When segmenting the five body tissues simultaneously, the Dice coefficients ranged from 0.826 to 0.840 for VAT, from 0.901 to 0.908 for SAT, from 0.574 to 0.611 for IMAT, from 0.874 to 0.889 for SM, and from 0.870 to 0.884 for bone, which were significantly higher than the Dice coefficients when segmenting the body tissues separately (p<0.05), namely, from 0.744 to 0.819 for VAT, from 0.856 to 0.896 for SAT, from 0.433 to 0.590 for IMAT, from 0.838 to 0.871 for SM, and from 0.803 to 0.870 for bone. There were no significant differences among the CNN models in segmenting body tissues, but jointly segmenting body tissues achieved a better performance than segmenting them separately.

Concurrent kilovoltage CBCT imaging and megavoltage beam delivery: suppression of cross-scatter with 2D antiscatter grids and grid-based scatter sampling

Objective. The concept of using kilovoltage (kV) and megavoltage (MV) beams concurrently has potential applications in cone beam computed tomography (CBCT) guided radiation therapy, such as single breath hold scans, metal artifact reduction, and simultaneous imaging during MV treatment delivery. However, MV cross-scatter generated during MV beam delivery degrades CBCT image quality. To address this, a 2D antiscatter grid and a cross-scatter correction method were investigated in the context of high dose MV treatment delivery. Approach. A 3D printed, tungsten 2D antiscatter grid prototype was utilized in kV CBCT scans to reduce MV cross-scatter fluence during concurrent MV beam delivery. Remaining cross-scatter in projections was corrected by using the 2D grid itself as a cross-scatter intensity sampling device, referred to as grid-based scatter sampling (GSS). To test this approach, kV CBCT acquisitions were performed while delivering 6 and 10 MV beams, mimicking high dose rate treatment delivery scenarios. kV and MV beam deliveries were not synchronized to eliminate MV beam delivery interruption. MV cross-scatter suppression performance of the proposed approach was evaluated in projections and CBCT images of phantoms. Main results. 2D grid reduced the intensity of MV cross-scatter in kV projections by a factor of 3 on the average, when compared to conventional antiscatter grid. Remaining cross scatter as measured by the GSS method was within 7% of measured reference intensity values, and subsequently corrected. CBCT image quality was improved substantially during concurrent kV–MV beam delivery. Median Hounsfield Unit (HU) inaccuracy was up to 182 HU without our methods, and it was reduced to a median 6.5 HU with our 2D grid and scatter correction approach. Our methods provided a factor of 2–6 improvement in contrast-to-noise ratio. Significance. This investigation demonstrates the utility of 2D antiscatter grids and grid-based scatter sampling in suppressing MV cross-scatter. Our approach successfully minimized the effects of MV cross-scatter in concurrent kV CBCT imaging and high dose MV treatment delivery scenarios. Hence, robust MV cross-scatter suppression is potentially feasible without MV beam delivery interruption or compromising kV image acquisition rate.

Multi-granularity scenarios understanding network for trajectory prediction

Understanding agents’ motion behaviors under complex scenes is crucial for intelligent autonomous moving systems (like delivery robots and self-driving cars). It is challenging duo to the inherent uncertain of future trajectories and the large variation in the scene layout. However, most recent approaches ignored or underutilized the scenario information. In this work, a Multi-Granularity Scenarios Understanding framework, MGSU, is proposed to explore the scene layout from different granularity. MGSU can be divided into three modules: (1) A coarse-grained fusion module uses the cross-attention to fuse the observed trajectory with the semantic information of the scene. (2) The inverse reinforcement learning module generates optimal path strategy through grid-based policy sampling and outputs multiple scene paths. (3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.

Avian brood parasites, species assemblage, and bird diversity: A case study using a grid-based survey in South Korea

Identifying proper surrogate species or groups is a challenging but critical step in the application of surrogate species approaches in biodiversity conservation because their effectiveness can vary according to biological communities. Recently, the brood parasitic common cuckoo, Cuculus canorus, has been suggested as a surrogate for describing biodiversity, primarily based on its strong parasitic relationship with other host species. However, the efficiency of cuckoo surrogates has been challenged because of the potential limitations of the methodological approaches. Furthermore, its generality rarely has been explicitly tested in other parasitic species with local biodiversity data collected through conventional schemes such as grid-based sampling. Using bird survey data obtained from a grid-based sampling scheme in South Korea, we evaluated the surrogate efficiency of five cuckoo species for bird diversity (species richness and phylogenetic diversity) and generated surrogate species groups. We found that among the five parasitic cuckoo species breeding in South Korea, the common cuckoo and the Indian cuckoo C. micropterus were effective surrogate species. Among these, only the common cuckoo belonged to the best surrogate species group together with its major host species; the Indian cuckoo showed less effective surrogate efficiency than the common cuckoo. In conclusion, our study shows that the common cuckoo, as a single species and a member of a species assemblage, is an effective indicator species for bird diversity in grid-based survey data. However, the result that not all parasitic cuckoo species performed a surrogate role effectively implies that multiple functions may be associated with the surrogate efficiency of brood parasitic cuckoos, along with the importance of host-parasite relationships.

UAV Remote Sensing for Detecting within-Field Spatial Variation of Winter Wheat Growth and Links to Soil Properties and Historical Management Practices. A Case Study on Belgian Loamy Soil

Intra-field heterogeneity of soil properties, such as soil organic carbon (SOC), nitrogen (N), phosphorous (P), exchangeable cations, pH, or soil texture, is a function of complex interactions between biological factors, physical factors, and historic agricultural management. Mapping the crop growth and final yield heterogeneity and quantifying their link with soil properties can contribute to an optimization of amendment/fertilizer application and crop yield in a management variable zones (MVZ) approach. To this end, we studied a field of 17 ha consisting of four former fields that were merged in early 2017 and cropped with winter wheat in 2018. Historical management practices data were collected. The topsoil characteristics were analyzed by grid-based sampling and kriged to create maps. We tested the capacity of a multispectral MicaSense® RedEdge-MTM camera sensor embedded on an unmanned aerial vehicle (UAV) to map in-season growth of winter wheat. Relating several vegetation indices (VIs) to the plant area index (PAI) measured in the field highlighted the red-edge NDVI (RENDVI) as the most suitable to follow the crop growth throughout the growing season. The georeferenced final grain yield of the winter wheat was measured by a combine harvester. The spatial patterns in RENDVI at three phenological stages were mapped and analyzed together with the yield map. For each of these images a conditional inference forest (CI-forest) algorithm was used to identify the soil properties significantly influencing these spatial patterns. Historical management practices of the four former fields have induced significant heterogeneity in soil properties and crop growth. The spatial patterns of RENDVI are rather constant over time and their Spearman rank correlation with yield is similar along the growing season (r ≃ 0.7). Soil properties explain between 87% (mid-March) to 78% (mid-May) of the variance in RENDVI throughout the growing season, as well as 66% of the variance in yield. The pH and exchangeable K are the most significant factors explaining from 15 to 26% of the variance in crop growth. The methodology proposed in this paper to quantify the importance of soil parameters based on the CI-forest algorithm can contribute to a better management of amendment/fertilizer inputs by stressing the most important parameters to take into consideration for site-specific management. We also showed that heterogeneity induced by the soil properties can be described by a crop map early in the season and that this crop map can be used to optimize soil sampling and thus amendment/fertilizer management.

Geoinformatics in the Preparation of Forest Working Plans for Odisha State

The Working Plan Code 2014 recommends grid based sampling design for the sample point distribution and using stratification at the estimation stage. Such a grid-based sampling would have required 52169 sample points and the requirements of sample point location at grid centre placed 3508 points outside the forested area. We have successfully demonstrated that using remote sensing based type and density stratification and spatially optimal stratified random sampling we can reduce the sample size with 92% estimation accuracies. Remote sensing based stratified random sampling with a two phase single stage sampling design reduced the sample size to one third (48661 to 17704) compared to grid based sampling while ensuring an estimation accuracy of 92%. This very significant reduction in sample size reduces the sampling cost and project completion time while achieving desired levels of accuracies. This SRS design ensured higher sampling intensities in the areas of potential working and high canopy density regions (8010 sample plots in 29% Selection Working Circle area while Rehabilitations Working Circles comprising 71% area receiving 9694 sample plots). A web-based Forest Inventory Design and Analysis System (web-based FIDAS) is developed on BHUVAN-NRSC's Geo-Visualization platform, which can be readily adapted for any new states working plan input preparation.

Making the Invisible Stratigraphy Visible: A Grid-Based, Multi-Proxy Geoarchaeological Study of Umhlatuzana Rockshelter, South Africa

Umhlatuzana rockshelter is an archaeological site with an occupational record covering the Middle Stone Age, Later Stone Age, and Iron Age. The presence of both Middle and Later Stone Age assemblages makes Umhlatuzana the ideal location for the study of the MSA–LSA transition (20–40 ka) in southern Africa. This transitional period is characterized by important modifications in stone tool technology, from prepared core technology to a toolkit based on microlith production. These changes are argued to have occurred in response to changes in climate and environment leading up to the Last Glacial Maximum. The deposits bearing the transitional assemblages at Umhlatuzana rockshelter appear homogeneous with no visible stratigraphic boundaries. This study integrates geoarchaeological techniques in order to explore fine-resolution geochemical differentiations of the sediments that are macroscopically invisible, and that will provide insight into (post-)depositional processes over time. Samples were systematically retrieved from the western profile of the site following a grid-based sampling strategy and analyzed for pH, elemental composition (XRF), and Magnetic Susceptibility. Additionally, the results were integrated with preliminary micromorphological observations. Our study reveals a steady, gradual change in the geochemistry of the deposits throughout the Pleistocene, related to a combination of environmental change and occupation intensity. We suggest that the part of the sequence reported to bear Middle to Later Stone Age transitional industries is characterized by wetter environmental conditions compared to the underlying deposits. Additionally, we support results from previous studies that excluded large scale post-depositional movement of the sedimentary sequence. Our study offers a successful multi-proxy approach to systematically sample and study archaeological deposits at the macro and micro scale, integrating a variety of geoarchaeological techniques. The approach provides insight into the depositional and post-depositional history of the site, and allows for questions of stratigraphic integrity, anthropogenic input, preservation, and environmental change to be addressed.

Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography.

We have been investigating two-dimensional (2D) antiscatter grids (2D ASGs) to reduce scatter fluence and improve image quality in cone beam computed tomography (CBCT). In this work, two different aspects of 2D ASGs, their scatter rejection and correction capability, were investigated in CBCT experiments. To correct residual scatter transmitted through the 2D ASG, it was used as a scatter measurement device with a novel method: grid-based scatter sampling. Three focused 2D ASG prototypes with grid ratios of 8, 12, and 16 were developed for linac-mounted offset detector CBCT geometry. In the first phase, 2D ASGs were used as a scatter rejection device, and the effect of grid ratio on CT number accuracy and contrast-to-noise ratio (CNR) evaluated in CBCT images. In the second phase, a grid-based scatter sampling method which exploits the signal modulation characteristics of the 2D ASG's septal shadows to measure and correct residual scatter transmitted through the grid was implemented. To evaluate CT number accuracy, the percent change in CT numbers was measured by changing the phantom from head to pelvis size and configuration. When 2D ASG was used as a scatter rejection device, CT number accuracy increased and the CT number variation due to change in phantom dimensions was reduced from 23% to 2-6%. A grid ratio of 16 yielded the lowest CT number variation. All three 2D ASGs yielded improvement in CNR, up to a factor of two in pelvis-sized phantoms. When 2D ASG prototypes were used for both scatter rejection and correction, CT number variations were reduced further, to 1.3-2.6%. In comparisons with a clinical CBCT system and a high-performance radiographic ASG, 2D ASG provided higher CT number accuracy under the same imaging conditions. When 2D ASG is used solely as a scatter rejection device, substantial improvement in CT number accuracy can be achieved by increasing the grid ratio. Two-dimensional ASGs also provided significant CNR improvement even at lower grid ratios. Two-dimensional ASGs used in conjunction with the grid-based scatter sampling method provided further improvement in CT number accuracy, irrespective of the grid ratio, while preserving 2D ASGs' capacity to improve CNR. The combined effect of scatter rejection and residual scatter correction by 2D ASG may accelerate implementation of new techniques in CBCT that require high quantitative accuracy, such as radiotherapy dose calculation and dual energy CBCT.

A unified scatter rejection and correction method for cone beam computed tomography.

Scattered radiation is a major cause of image quality degradation in flat panel detector-based cone beam CT (CBCT). While recently introduced 2D antiscatter grids reject the majority of scatter fluence, the small percentage of scatter fluence still transmitted to the detector remains a major challenge for implementation of quantitative imaging techniques such as dual energy imaging in CBCT. Additionally, this residual scatter is also a major source of grid-induced artifacts, which impedes implementation of 2D grids in CBCT. We therefore present a new method to achieve both robust scatter rejection and residual scatter correction using a 2D antiscatter grid; in doing so, we expand the role of 2D grids from mere scatter rejection devices to scatter measurement devices. In our method, the radiopaque septa of the 2D grid emulate a micro array of beam-stops placed on the detector which introduce spatially periodic septal shadows. By selecting sufficiently thin grid septa, the primary intensity can be reduced while preserving the uniformity of scatter intensity. This enables us to correlate the modulated pixel signal intensity in septal shadows with local scatter intensity. Our method then exploits this correlation to measure and remove residual scatter intensity from projections. No assumptions are made about the object being imaged. We refer to this as Grid-based Scatter Sampling (GSS). In this work, we evaluate the principle of signal modulation with grid septa, the accuracy of scatter estimates, and the effect of the GSS method on image quality using simulations and measurements. We also implement the GSS method experimentally using a 2D grid prototype. Our results demonstrate that the GSS method increased CT number accuracy and reduced image artifacts associated with scatter. With 2D grid and residual scatter correction, HU nonuniformity was reduced from 65HU to 30HU in pelvis sized phantoms, and HU variations due to change in phantom size were reduced from 59HU to 20HU, when compared to use of only a 2D grid. With residual scatter correction via GSS method, grid-induced ring artifacts were suppressed, leading to a 41% reduction in noise. The shape of the modulation transfer function (MTF) was preserved before and after suppression of ring artifacts. Our grid-based scatter sampling method enables utilization of a 2D grid as a scatter measurement and correction device. This method significantly improves quantitative accuracy in CBCT, further reducing the image quality gap between CBCT and multi-detector CT. By correcting residual scatter with the proposed method, grid-induced line artifacts in projections and associated ring artifacts in CBCT images were also suppressed with no compromise of spatial resolution.

Scope to map available water content using proximal sensed electromagnetic induction and gamma-ray spectrometry data

Dryland cotton farmers need to carefully manage the available water content (AWC) in the root-zone. To reduce cost of mapping, a digital soil mapping (DSM) approach might be useful. The purpose of this study was seeing if we could create DSM of the AWC at the field scale. In the first instance, digital data in the form of proximal sensed electromagnetic (i.e EM) and gamma-ray (γ-ray) spectrometry data were collected along with elevation. Using a grid-based sampling design, 52 soil sample locations were selected and sampled; topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m). In the laboratory, the field capacity (FC) and permanent wilting point (PWP) were determined using a pressure plate apparatus. The digital and soil (i.e. FC and PWP) data were then calibrated with multiple linear regression (MLR) and stepwise-MLR models compared for each depth (e.g. adjusted coefficient of determination [adj-R2]). The stepwise-MLR were shown to be superior. Subsequently, the DSM of FC and PWP were examined in terms of prediction agreement (Lin’s concordance). Leave-one-out cross validation results showed substantial 1:1 agreement between measured and predicted FC (0.80) and PWP (0.79) at all depths. The final DSMs of FC and PWP, and by difference AWC, were indicative of the two main geomorphological/geological units, with large AWC (> 0.3 m3 m−3) associated with the clay rich Vertosols and small AWC (< 0.15 m3 m−3) the loamy soil of the Pilliga Sandstone. Owing to the short scale variation and the fact that the local alluvium and Pilliga Sandstone were close together and the fact that the digital data varied in different ways, larger confidence interval (CI) values were evident where these areas were juxtaposed. Nevertheless, the final DSM allows the farmer to better manage AWC.

Use of Smartphones for Rapid Location Tracking in Mega Scale Soil Sampling

Conventional, grid-based sampling location tracking approach with GPS or topographic maps is time-consuming and inconvenient. Therefore, this study combined the freely available virtual/digital globe with recent advancements of smartphone geo-browsers to develop a new sampling location tracking approach. The sampling frame was developed with pre-uploaded point map formulated in GIS environment by combining land-use map and one-kilometer grid with center coordinates. The sampling location grid was uploaded to smartphone and each point was tracked with Google Map path finder using point ID (coded grid coordinate) when searching each sampling location. The suitability of the new approach was tested for soil sample collection from paddy lands distributed in 0.9 million hectares in Sri Lanka. The sampling locations could be reached conveniently with the help of Google path-finder voice direction guide and optional routes. The efficiency of new approach was found to be remarkably high, i.e., over 99% of the 9000 sampling points, which were spread across Sri Lanka having an area of 65,610 km2. All sampling points in the country could be covered with twelve member field investigation crew who were guided through smartphones uploaded with sampling point grid on six motorbikes within 60 days. The new sampling location tracking approach is effective in terms of cost, time, human resource requirements, thus can be adopted in large-scale soil/plant sampling frames with high accuracy.

GreedyCenters: Satellite imagery adaptive sampling method for artificial neural networks training

The one of many significant particularities of satellite imagery is large size of images within orders of magnitude exceeds capability of modern GPGPU to train neural networks on its full size. On the other hand satellite imagery tends to be limitedly available. Moreover, the objects of interest tends to constitute a small fraction of whole dataset. This leads to the demand of sample extraction and augmentation method specialized on satellite imagery. Yet this area is immensely underrated so almost all widely used method limited to grid-based sample extraction and augmentation via combinations of 90-degrees rotations and mirroring on vertical or horizontal axes. This paper proposes the domain-agnostic method of sample extraction and augmentation. Adoption of this method to specific subject area is based on domain-specific way to generate significance field of image. In contrast to trivial greedy solutions and more advanced stochastic optimization methods the design of proposed method is focused on maximizing per-step progress. This makes its performance reasonably good even without low-level optimizations without significant quality loss. It can be easily implemented using widely known and open source software libraries.