Grid Cells Research Articles

Optimizing Urban Delimitations in Pakistan Through Exploratory Data Analytical Approach

In Pakistan, the lack of a standardized definition for urban areas hampers effective urban planning and policy development. Thus, this research study employs data science by integrating the Geographic Information System (GIS) employed to find the weightage of the urban threshold that can redefine the urban areas in Pakistan. With diverse socio-economic and demographic factors considered, exploratory data analysis (EDA) incorporates population density, infrastructure development, economic indicators, and land use patterns to demark urban boundaries. As there is no consensus on what an “urban area” in Pakistan should be, this study takes a data-focused approach to determine the criteria that best capture the complexities of urbanization. It has been done through spatial analytics and statistical modeling in a data science framework. The integration of a data science framework enables to set-up threshold for development of urban landscape. The proposed redefinition of urban areas has implications for urban planning, resource allocation, and policy development, fostering a more accurate representation of the urban fabric in Pakistan. This study contributes to filling the existing gap in defining urban areas in Pakistan. It identifies new urban percentages that more accurately reflect the socio-economic and demographic realities of Pakistan's urban landscape. The findings shows that there are 36 percent of urban grid cells retrieved from the EDA approach. The outcomes provide a foundation for informed decision-making and policy interventions, aligning urban definitions with Pakistan's urbanization process's complex and dynamic nature. This research will also ensure a comprehensive understanding of urban landscapes in the face of diverse challenges particularly in developing countries.

3D magnetotelluric inversion with arbitrary data orientation angles

The survey setup of 3D magnetotelluric inversion is typically chosen to best reflect the underlying geological structure. Typically, the individual observation azimuths are distinct from this preferred survey layout orientation; in fact, measured electric and magnetic fields are aligned with local geomagnetic North. When that happens, either data preprocessing (rotation) is required before the inversion may be performed, or the orientation of the modeling grid is aligned with the data orientations. None of these approaches is optimal, possibly resulting in a reduction of data quality, the loss of statistical accuracy of the error estimates, or in unnecessary grid cells, resulting in increased computation times. In situations when the data quality may be improved by allowing distinct individual site orientations, such as when cultural noise or faulty instrumentation cause a degradation of one of the modes, data loss due to rotation is especially detrimental. In this paper, we present a modified 3D magnetotelluric inversion algorithm, based on the ModEM software, that allows for direct interpretation of observations with arbitrary data orientation angles (ModEM ADORA). In ModEM ADORA, the modeling responses are rotated to the observation orientation angles after each forward calculation and before the data misfit is evaluated. This allows for direct interpretation of observation data with distinct orientations at each site and (as needed for some historical data) at each frequency. The derivative of the penalty functional is computed by pre-multiplying the derivatives of the data functionals (or the complete Jacobian matrix) by the relevant sparse block-diagonal rotation operators. We describe the principle and implementation of ModEM ADORA, and provide a collection of synthetic and real data tests for validation and an analysis of effectiveness of the ADORA option of ModEM. We show that by decoupling modeling grid and sensor orientations, ModEM ADORA supports both computational efficiency and ease of use, as well as allows for the maximum quality and quantity of inverted data.

A hierarchical model with hexagon grids for multi-objective route planning in large-scale off-road environments

Off-road route planning plays a vital role across various domains, including civil transportation, earthquake relief and civil production. Off-road route planning requires the consideration of a diverse range of environmental constraints, which is a considerable challenge compared with conventional route planning along existing networks. We aimed to present a multi-objective route planning method for off-road environments. Specifically, we designed a heuristic function incorporating multiple characteristics to generate reliable route planning solutions based on Pareto optimality. We also constructed a hierarchical hexagonal grid model by merging similar grid cells and reconstructing adjacency relationships with other grid cells to achieve high efficiency. The proposed method was compared with conventional route planning methods using single- and multi-objective approaches in traditional and hierarchical hexagonal grid models. The proposed method can effectively plan off-road routes, improve optimal route distance and trafficability, and provide more efficient route solutions. It reduced the planning time by 97.1% compared with multi-objective route planning based on a traditional hexagonal grid model.

A non-uniform lattice design method for lightweight structures in 3D printing

Lightweight design is an essential topic in aerospace, automotive and other fields. In automobile manufacturing, the engine connecting rod is one of the main components; its lightweight design has a high reference value. And 3D printing provides a feasible solution for designing and manufacturing lightweight structures. Unlike the traditional geometrically homogeneous point design, this study offers a non-homogeneous point design method based on the spatial stress state of additively manufactured components. After numerical simulation of quasi-static stresses on a model of an engine connecting rod, finite element grid cells with different stress values are replaced by lattice cells with different specific stiffnesses at similar local stress levels. The overall specific stiffness of the structure is further improved by continuing the optimized design with the corresponding gradient-type reinforcement of the non-uniform lattice structure. The basic idea of this design is to perform non-uniform adaptive filling of solid parts under localized loading by employing different types of unit cells. Stereolithography 3D printing technology prepares the engine lattice structural parts for quasi-static compression comparison experiments and fracture analysis after failure. The conclusions show that the engine connecting rod members with non-homogeneous lattice have more excellent overall mechanical properties than homogeneous lattice members. This work demonstrates the feasibility of such design methods for 3D printing lightweight structures and optimization.

Visual boundary cues suffice to anchor place and grid cells in virtual reality

The hippocampal formation contains neurons responsive to an animal's current location and orientation, which together provide the organism with a neural map of space.1,2,3 Spatially tuned neurons rely on external landmark cues and internally generated movement information to estimate position.4,5 An important class of landmark cue are the boundaries delimiting an environment, which can define place cell field position6,7 and stabilize grid cell firing.8 However, the precise nature of the sensory information used to detect boundaries remains unknown. We used 2-dimensional virtual reality (VR)9 to show that visual cues from elevated walls surrounding the environment are both sufficient and necessary to stabilize place and grid cell responses in VR, when only visual and self-motion cues are available. By contrast, flat boundaries formed by the edges of a textured floor did not stabilize place and grid cells, indicating only specific forms of visual boundary stabilize hippocampal spatial firing. Unstable grid cells retain internally coherent, hexagonally arranged firing fields, but these fields "drift" with respect to the virtual environment over periods >5 s. Optic flow from a virtual floor does not slow drift dynamics, emphasizing the importance of boundary-related visual information. Surprisingly, place fields are more stable close to boundaries even with floor and wall cues removed, suggesting invisible boundaries are inferred using the motion of a discrete, separate cue (a beacon signaling reward location). Subsets of place cells show allocentric directional tuning toward the beacon, with strength of tuning correlating with place field stability when boundaries are removed.

Points, cells, or polygons? On the choice of spatial units in forest conservation policy impact evaluation

A fast-growing literature uses remotely sensed land-cover data along with quasi-experimental statistical methods to assess the efficacy of forest conservation interventions. A critical modeling choice is the spatial unit of analysis—points, grid cells, and polygons are all commonly used. Yet little is known about the implications of this choice for treatment effect estimates and for their interpretation. We demonstrate that point-level data can generate treatment effect estimates substantially different from those based on polygon-level data when (i) a disproportionate share of sample points is drawn from relatively large, treated polygons as a result of random or quasi-random spatial sampling, and (ii) the intervention analyzed has heterogeneous effects that depend on treatment polygon size. Our paper has four parts. First, using real-world data (on the award of timber extraction permits to forest management units in Mexico) that meet the two aforementioned criteria, we demonstrate that point- and polygon-level data generate qualitatively different results, and we propose a simple method for weighting the point-level data to recover the polygon-level results. Second, we conduct a Monte Carlo simulation to clarify the mechanism that causes this phenomenon and to provide reassurance that it is not driven by unobserved confounding factors. Third, we present new evidence (on Mesoamerican and Dominican protected areas) suggesting this phenomenon is not uncommon. Finally, we discuss the implications of our findings for the design and interpretation of spatial evaluations of forest conservation interventions. Although our analysis focuses on point- versus polygon-level data, the mechanism we describe also applies to grid cell- versus polygon-level data.

Zoogeographical regions in the Atlantic Forest: patterns and potential drivers

AbstractAimTo delineate present‐day zoogeographical regions of terrestrial vertebrates (frogs, lizards, snakes, birds and non‐volant mammals) in the Atlantic Forest. Within each taxonomic group, we examine the relative importance of abrupt climatic transitions, orographic barriers, past climate change and rivers in shaping zoogeographical boundaries.LocationSouth America's Atlantic Forest.MethodsWe applied a network‐based method to delineate zoogeographical regions, using distribution data (range maps) for 455 species of frogs, 103 lizards, 220 snakes, 917 birds and 202 non‐volant mammals, in 50 × 50 km grid cells. We used hierarchical generalized linear mixed‐effects models to test environmental predictors associated with zoogeographical boundaries. Finally, we intersected the bioregion maps delineated for each group to identify general patterns across all vertebrates.ResultsWe identified four zoogeographical regions for birds and snakes, and five for frogs, lizards and non‐volant mammals. Depending on the group, contemporary and past climate conditions, elevation variation and/or rivers were associated with zoogeographical boundaries. The combined maps indicate that the Atlantic Forest retains four spatially cohesive zoogeographical regions based on present‐day distribution of vertebrates.Main conclusionsCross‐taxon congruence indicates that the geographical and environmental characteristics of the Atlantic Forest have a strong influence on the location of zoogeographical regions for vertebrates. In contrast, transition zones appear to be associated with the spatial distribution of life history traits of each group, potentially explaining the observed differences in the number of bioregions across groups and the position of zoogeographical boundaries. This work paves the way for further research into the evolutionary assembly of the Atlantic Forest's zoogeographical regions and may help inform conservation priorities for maintaining their distinctive faunas.

The Impacts of Changing Winter Warm Spells on Snow Ablation Over Western North America

AbstractAn increase in winter air temperature can amplify snowmelt and sublimation in mountain regions with implications to water resources and ecological systems. Winter Warm Spells (WWS) are defined as a winter period (December to February, DJF) of at least 3 consecutive days with daily maximum temperature anomaly above the 90th percentile (using a moving‐average of 15 days between 2001 and 2013). We calculate WWS for every 4‐km grid cell within an atmospheric model over western North America to characterize WWS and analyze snow ablation and their changes in a warmer climate. We find that days with ablation during WWS represent a small fraction of winter days (0.6 days), however, 49% of total winter ablation (33.4 mm/DJF) occurs during WWS. Greater extreme ablation rates (99th percentile) occur 18% more frequently during WWS than during non‐WWS days. Ablation rates during WWS in humid regions are larger (9 mm d−1) than in dry regions (7 mm d−1) in a warmer climate, which can be explained by differences in the energy balance and the snowpack's cold content. We find that warmer (0.8°C), longer (1.8 days) and more frequent (3.7 more events) WWS increase total winter ablation (on average 109% or 18 mm/DJF) in a warmer climate. Winter melt during WWS in warm and humid places is expected to increase about 3 times more than in the cold and dry region. This study provides a comprehensive description of WWS and their impact on snowpack dynamics, which is relevant to reservoir operations and water security.

A comparison and development of methods for estimating shrub volume using drone‐imagery‐derived point clouds

AbstractShrub volume is used to calculate numerous, essential ecological indicators in rangeland ecosystems such as biomass, fuel loading, wildlife habitat, site productivity, and ecosystem structure. Field techniques for biomass estimation, including destructive sampling, ocular estimates, and allometric techniques, use shrub height and canopy widths to estimate volume and translate it to biomass with species‐specific allometric equations. These techniques are time‐consuming and pose challenges, including removal of plant material and training of observers. We compared canopy volume estimates from field‐based measurements with drone‐collected canopy volume estimates for seven dominant shrub species within mountain big sagebrush (Artemisia tridentata subsp. vaseyana) plant communities in southern Idaho, USA. Canopy height and two perpendicular width measurements were taken from 103 shrubs of varying sizes, and volume was estimated using a traditional allometric equation. Overlapping aerial images captured with a DJI Mavic 2 Professional drone were used to create a 3D representation of the study area using structure‐from‐motion photogrammetry. Each shrub was extracted from the point cloud, and volume was estimated using allometric and volumetric methods. The volumetric method, which involved converting point clouds to raster canopy height models with 2.5‐ and 5‐cm grid cells, outperformed the allometric method (R2 > 0.7) and was more reproducible and robust to user‐related variability. Drone‐estimated volume best‐matched field‐estimated volume (R2 > 0.9) for three larger species: A. tridentata subsp. tridentata, A. tridentata subsp. vaseyana, and Purshia tridentata. The volume of smaller shrubs (canopy widths <1 m) was slightly overestimated from drone‐based models. We argue that drone‐based models provide a suitable alternative to field methods, while having the added benefit of being less time‐consuming, with fewer limitations, and more easily scaled to larger study areas than traditional field techniques. Finally, we demonstrate a proof of concept for automating canopy volume estimates using point‐cloud‐based automatic shrub detection algorithms. These findings demonstrate that drone‐collected images can be used to assess shrub canopy volume for at least five upland sagebrush steppe shrub species and support the integration of drone data collection into rangeland vegetation monitoring.

Impact of Planetary Parameters on Water Clouds Microphysics

Potentially habitable exoplanets are targets of great interest for the James Webb Space Telescope and upcoming mission concepts such as the Habitable Worlds Observatory. Clouds strongly affect climate and habitability, but predicting their properties is difficult. In Global Climate Models (GCMs), especially those aiming at simulating Earth, cloud microphysics is often crudely approximated by assuming that all cloud particles have a single, constant size or a prescribed size distribution and that all clouds in a grid cell are identical. For exoplanets that range over a large phase space of planetary properties, this method could result in large errors. In this work, our goal is to determine how cloud microphysics on terrestrial exoplanets, whose condensable is mainly water vapor, depend on aerosol properties and planetary parameters such as surface pressure, surface gravity, and incident stellar radiation. We use the Community Aerosol and Radiation Model for Atmospheres as a 1D microphysical model to simulate the formation and evolution of clouds including the processes of nucleation, condensation, evaporation, coagulation, and vertical transfer. In these 1D idealized experiments, we find that the parameters that determine the macrophysical thermal structure of the atmospheres, including surface pressure and stellar flux, impact cloud radiative effect (CRE) most significantly. Parameters such as gravity and number density of aerosols working as cloud condensation nuclei affect the microphysical processes of cloud formation, including activation and vertical transfer. They also have a significant, though weaker effect on CRE. This work motivates the development of more accurate GCM cloud schemes and should aid in the interpretation of future observations.

ВЛИЯНИЕ ВНЕШНИХ ПАРАМЕТРОВ НА ВЕЛИЧИНУ ЭВАПОТРАНСПИРАЦИИ В МОДЕЛИ ДЕЯТЕЛЬНОГО СЛОЯ СУШИ ИВМ РАН - МГУ

The paper investigates the sensitivity of evapotranspiration in the INM RAS-MSU land surface model to the changes in the weights of land surface classes in the cells of the latitude-longitude grid and the leaf area index LAI. It is demonstrated that the refinement of the values of the mentioned parameters based on modern observational data significantly reduces an error in evapotranspiration. The annual sum of evapotranspiration averaged over 10 years (2002-2012) from the surface of medium-sized (2-50 × 103 km2) watersheds of northern European Russia is used for the analysis. The model error has been calculated against the empirical estimates of evapotranspiration obtained from the watershed water balance equation. As an intermediate task, the accuracy of satellite data on terrestrial water storage used in the calculations is assessed by the comparison with the data of snow route surveys.

Nowcasting of High-Intensity Rainfall for Urban Applications in the Netherlands

Abstract Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains (e.g., river basins), although rainfall nowcasting in small urban areas is expected to be more challenging. Here, we selected 80 events with high rainfall intensities (at least one 1-km2 grid cell experiences precipitation >15 mm h−1 for 1-h events or 30 mm day−1 for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9060 probabilistic nowcasts with 20 ensemble members by applying the short-term ensemble prediction system (STEPS) from Pysteps to every 10-min issue time for the selected events. We found that nowcast errors increased with decreasing (urban) areas especially when below 100 km2. In addition, at 30-min lead time, the underestimation of nowcasts was 38% larger and the discrimination ability was 11% lower for 1-h events than for 24-h events. A set of gridded correction factors for the Netherlands, CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) could adjust the bias in real-time QPE and nowcasts by 70%. Yet, nowcasts were still found to underestimate rainfall more than 50% above 40-min lead time relative to the reference, which indicates that this error originates from the nowcasting model itself. Also, CARROTS did not adjust the rainfall spatial distribution in urban areas much. In summary, radar-based nowcasting for urban areas (between 67 and 213 km2) in the Netherlands exhibits a short skillful lead time of about 20 min, which can only be used for last-minute warning and preparation.

Spatial and Temporal Variation of Subseasonal-to-Seasonal (S2S) Precipitation Reforecast Skill across the CONUS

Abstract Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resource management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash–Sutcliffe efficiency (NSE) coefficient, has been analyzed toward understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components—correlation, conditional bias, and unconditional bias—by four seasons, three lead times (1–12, 1–22, and 1–32 days), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 in. per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists, and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during the fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Postprocessing using simple model output statistics could reduce both unconditional and conditional biases to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show that efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

Live Object Recognition Using YOLO

Abstract: Live object recognition refers to the real-time process of identifying and categorizing objects within a given visual input, such as images. This technology utilizes computer vision techniques and advanced algorithms to detect objects, determine their dimension, area and weight and often classify them into defined categories. Our system proposesR-CNN and YOLO to determine the dimensions of the objects in real time. YOLO takes a different approach by treating object detection as a single regression problem. A single neural network is trained to directly predict bounding boxes and class probabilities for multiple objects in an image. The input image is divided into a grid, and each grid cell is responsible for predicting the objects whose center fall within that cell. Live object recognition finds applications in various fields, including autonomous vehicles, surveillance systems, robotics,augmented reality, and more. By providing instantaneous and accurate insights into the surrounding environment, this technology contributes to enhanced decision-making,interaction, and automation across numerous domains. The objects which can be recognized are solid objects which we use daily such as electronic items, stationery items, culinary items and many more. Our system “Live Object Recognition using YOLO” is aimed to detect and determine the objects and dimensions in real time.

Groundwater decline has negatively affected the well-preserved amphibian community of Doñana National Park (SW Spain)

Abstract The success of amphibian conservation efforts is highly dependent on the preservation of amphibian breeding habitats. Doñana National Park contains an abundance of ponds with heterogeneous characteristics, which has historically favoured its amphibian community. However, most of the park’s ponds are groundwater dependent, and aquifer overexploitation outside the park is leading to shorter hydroperiods and even desiccation in the pond network. This problem has been exacerbated by the scarcity of rain over the last decade. In 2021-2022, we surveyed the occurrence of the park’s 11 amphibian species, with a view to comparing our findings with those from the last previous survey in 2003-2004. We mapped the species occurrence within the park for the two survey periods. While all 11 amphibian species could still be found in Doñana, their occurrence had decreased across the board. In 2003-2004, 6 species were present across more than 50% of the sampling area. In contrast, 18 years later, such was the case for only two species (Pelophylax perezi and Hyla meridionalis). Declines were greatest for Epidalea calamita, followed by Pelobates cultripes, Triturus pygmaeus, and Lissotriton boscai. The mean number of species per sampling unit (i.e., grid cell) dropped from 4.3 to 3.1. To preserve Doñana’s amphibian community, it is important to restore the park’s pond network, which implies reducing regional groundwater overexploitation.

Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion.

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera-LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster-Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles.

Underprediction of extirpation and colonisation following climate and land‐use change using species distribution models

AbstractAimTo evaluate the performance of species distribution models in predicting observed colonisations, persistences and extirpations in response to changes in climate and land use over a multi‐decadal period.LocationSweden.MethodsWe use historical (early 20th century) land use and climate data to build species distribution models for 84 plant species across three provinces of Sweden. Model performance was then evaluated internally using a subset of the historical data for cross‐validation, as well as by using the models to project occurrences to the modern day and validating them with observed occurrences from 1990 to 2020. We then analysed predicted and observed occurrences in the modern period in terms of persistence, extirpation (local extinction) and colonisation in relation to species' habitat and climate associations.ResultsWe found overall high agreement between evaluation methods, although internal evaluation gave consistently higher values for model performance (using true skill statistic, TSS). Overall, extirpations were worst predicted, with on average fewer than one‐third of each species' extirpations being foreseen by the models. Colonisations were better predicted, while persistences were relatively well‐predicted. Predictive accuracy of colonisations was higher for species with relatively warmer temperature associations (climate‐driven expansion), while extirpations were better predicted in cool‐related species (retractions at cool edges). Colonisations of forest‐associated species were more common than predicted (underpredicted), despite widespread patterns of afforestation. Assessing grid‐cell level turnover, we found that in grid cells that experienced the largest changes in terms of climate and land use, predicted extirpations were less likely to have happened.Main ConclusionsWe found that commonly applied modelling approaches have limited ability to predict observed changes in species occurrences, especially extirpations. This suggests that we should take predictions of future biodiversity loss very seriously. However, the ability for species to (at least temporarily) persist in unsuitable conditions could be an opportunity for biodiversity conservation.

Uncertainty propagation analysis for distributed hydrological forecasting using a neural network

AbstractThe last few decades have presented a significant increase in hydrological disasters, such as floods. In some countries, most of the environmental, socioeconomic, and biodiversity losses are caused by floods. Thus, flood forecasting is crucial to support an efficient disaster warning system. This work proposes a model for hydrological forecasting based on a neural network with a geographically aligned input named GeoNN. It employs weather radar data to obtain accumulated rainfall in each grid cell of the watershed and make 15‐ and 120‐min predictions of the outlet river level. An uncertainty propagation analysis was performed for GeoNN from a collection of test cases obtained by either using different schemes of the dataset partitioning or introducing different additive‐noise rates to the input data to provide a probability of flood occurrence and also an ensemble prediction. Both this probability and the ensemble were able to detect occurrences of river levels exceeding a given flood threshold.

A Eulerian Monte Carlo method for the numerical solution of the multivariate population balance equation

Statistical descriptions of particulate phases and granular media based on the notion of a property distribution are particularly advantageous for dense, many-particle dispersions since they support the direct evaluation of Eulerian property statistics, permit a kinematically simple treatment of particle collisions and feature a model complexity that is independent of the number of physical particles. On the minus side, the property distribution is governed by a high-dimensional population balance equation (PBE) whose moment reduction is accompanied by a challenging closure problem and engenders intricate dynamical nonlinearities. Circumventing both the curse of dimensionality and the moment closure problem, we present a Eulerian Monte Carlo (EMC) solution method that harnesses a representation of the property distribution in terms of an ensemble of property samples and is based on a statistical enforcement of the spatially and temporally discretized PBE. After application of a kinetic finite volume scheme, the PBE is subjected to a fractional steps decomposition that separates spatial transport from transport in property space. Conceptually, spatial transport is emulated by assembling the ensemble in one spatial grid cell from samples drawn from the ensembles in neighbouring cells at the previous time point. In this regard, we propose a two-level sampling scheme that is sensitive to large-scale features of the marginal velocity distribution and effectively reduces the instantaneous sampling error. The property transport step, by contrast, is based on the method of characteristics and involves the propagation of individual samples as property characteristics in time. While the sampling error afflicting the property statistics needs to be controlled using a top-level time or ensemble averaging, the EMC method remains well-conditioned for an arbitrary number of samples, is easy to implement and involves a computational expense that scales only linearly with the number of particle properties. Based on two benchmark examples, we analyze the spatial and statistical convergence properties of the EMC method and demonstrate its capacity to capture multiple crossings of particle trajectories. A third example involves the two-way coupled transport of size-polydispersed inertial particles in an inhomogeneous flow field.

Multi-Agent Cooperative Camera-Based Semantic Grid Generation

The idea of cooperative perception for navigation assistance was introduced about a decade ago with the aim to increase safety on dangerous areas like intersections. In this context, roadside infrastructure appeared very recently to provide a new point of view of the scene. In this paper, we propose to combine the Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) approaches in order to take advantage of the elevated points of view offered by the infrastructure and the in-scene points of view offered by the vehicles to build a semantic grid map of the moving elements in the scene. To create this map, we chose to use camera information and 2-Dimentional (2D) bounding boxes in order to minimize the impact on the network and ignored possible depth information as opposed to all state-of-the art methods. We propose a framework based on two fusion methods: one based on the Bayesian theory and the other on the Dempster-Shafer Theory (DST) to merge the information and chose a label for each cell of the semantic grid in order to assess the best fusion method. Finally, we evaluate our approach on a set of datasets that we generated from the CARLA simulator varying the proportion of Connected Vehicle (CV) and the traffic density. We also show the superiority of the method based on the DST with a gain on the mean intersection over union between the two methods of up to 23.35%.