Uniform Grid Cells Research Articles

Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets

Scene classification is a means to interpret high-resolution remote sensing (HRS) imagery, to obtain the high-level semantic information, which can provide a reliable reference for urban planning and monitoring. The traditional scene classification methods based on HRS imagery take uniform grid cells as the scene units, thereby missing the geographic boundaries and leading to the mosaicking effect. Therefore, in this paper, the urban scene is defined as a geographic unit bordered by the road network. The task of urban scene understanding is to recognize the socioeconomic or natural semantics of the urban scene. However, due to the complexity of the urban environment, the effectiveness of the traditional scene classification methods is limited on account of three problems when applied in urban scenes: 1) The lack of socioeconomic information in HRS images, with which it is difficult to discriminate diverse urban scenes with similar exteriors. 2) The large discrepancy in the sizes and shapes of urban land parcels affects the scene feature extraction and representation. 3) Urban scene understanding frameworks that can embed various scene classification models have rarely been studied. In this paper, to solve these problems, a universal urban scene understanding framework based on multi-source geographic data (USUMG) is proposed. In the USUMG framework, road network and water channel data from OpenStreetMap (OSM) are used for generating the urban scene units. For each irregular unit, a scene decomposition method based on a morphological skeleton is employed to represent the urban scene unit by unified processing patches. To integrate the different data sources, the high-level semantic features extracted from the HRS imagery and the socioeconomic features extracted from point of interest (POI) data are fused to determine the urban scene category. Finally, the USUMG framework with various scene classification methods was tested in urban districts of Wuhan and Macao in China to verify the universality and feasibility of the proposed framework. The experimental performances are provided in this paper as a benchmark for urban scene understanding based on multi-source geographic data.

Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach

As direct prospecting data, geochemical data play an important role in modelling prospect potential. Geochemical element assemblage anomalies are usually reflected by the correlation between elements. Correlation coefficients are computed from the values of two elements, which reflect only the correlation at a global level. Thus, the spatial details of the correlation structure are ignored. In fact, an element combination anomaly often exists in geological backgrounds, such as on a fault zone or within a lithological unit. This anomaly may cause some combination of anomalies that are submerged inside the overall area and thus cannot be effectively extracted. To address this problem, we propose a local correlation coefficient based on spatial neighbourhoods to reflect the global distribution of elements. In this method, the sampling area is first divided into a set of uniform grid cells. A moving window with a size of 3×3 is defined with an integer of 3 to represent the sampling unit. The local correlation in each unit is expressed by the Pearson correlation coefficient. The whole area is scanned by the moving window, which produces a correlation coefficient matrix, and the result is portrayed with a thermal diagram. The local correlation approach was tested on two selected geochemical soil survey sites in Xiao Mountain, Henan Province. The results show that the areas of high correlation are mainly distributed in the fault zone or the known mineral spots. Therefore, the local correlation method is effective in extracting geochemical element combination anomalies.

Effects of spatial decomposition on the efficiency of kNN search in spatial interpolations

ABSTRACT Spatial interpolations are commonly used in geometric modelling in life science applications such as medical image processing. In large-scale spatial interpolations, it is always needed to find a local set of data points for each interpolated point using the k Nearest Neighbor (kNN) search. To improve the efficiency of kNN, the uniform grid is commonly employed to fastly locate neighbours, and the size of grid cell could strongly affect the efficiency of kNN search. In this paper, we evaluate effects of the size of uniform grid cell on the efficiency of kNN search which is implemented on the CPU and GPU. We employ the Standard Deviation of points’ coordinates to measure the spatial distribution of scattered points. For irregularly distributed scattered points, we perform several series of kNN search in two- and three-dimensions. Benchmark results indicate that: for both the sequential version implemented on the CPU and the parallel version implemented on the GPU, with the increase of the Standard Deviation of points’ coordinates, the relatively optimal size of the grid cell decreases and eventually converges. Moreover, relationships between the Standard Deviation of scattered points’ coordinates and the relatively optimal size of grid cell are fitted.

Using gridded population and quadtree sampling units to support survey sample design in low-income settings

BackgroundHousehold surveys are the main source of demographic, health and socio-economic data in low- and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames.MethodsPreviously this process was done by either sampling from a set of the uniform grid cells (UGC) which are then manually subdivided to achieve the desired population size, or by sampling very small grid cells then aggregating cells into larger units to achieve a minimum population per survey cluster. The former approach is time and resource-intensive as well as results in substantial heterogeneity in the output sampling units, while the latter can complicate the calculation of unbiased sampling weights. Using the context of Somalia, which has not had a full census since 1987, we implemented a quadtree algorithm for the first time to create a population sampling frame. The approach uses gridded population estimates and it is based on the idea of a quadtree decomposition in which an area successively subdivided into four equal size quadrants, until the content of each quadrant is homogenous.ResultsThe quadtree approach used here produced much more homogeneous sampling units than the UGC (1 × 1 km and 3 × 3 km) approach. At the national and pre-war regional scale, the standard deviation and coefficient of variation, as indications of homogeneity, were calculated for the output sampling units using quadtree and UGC 1 × 1 km and 3 × 3 km approaches to create the sampling frame and the results showed outstanding performance for quadtree approach.ConclusionOur approach reduces the manual burden of manually subdividing UGC into highly populated areas, while allowing for correct calculation of sampling weights. The algorithm produces a relatively homogenous population counts within the sampling units, reducing the variation in the weights and improving the precision of the resulting estimates. Furthermore, a protocol of creating approximately equal-sized blocks and using tablets for randomized selection of a household in each block mitigated potential selection bias by enumerators. The approach shows labour, time and cost-saving and points to the potential use in wider contexts.

Divide and Count: Generic Object Counting by Image Divisions.

We propose a general object counting method that does not use any prior category information. We learn from local image divisions to predict global image-level counts without using any form of local annotations. Our method separates the input image into a sets of image divisions - each fully covering the image. Each image division is composed of a set of region proposals or uniform grid cells. Our approach learns in an endto- end deep learning architecture to predict global image-level counts from local image divisions. The method incorporates a counting layer which predicts object counts in the complete image, by enforcing consistency in counts when dealing with overlapping image regions. Our counting layer is based on the inclusion-exclusion principle from set theory. We analyze the individual building blocks of our proposed approach on Pascal- VOC2007 and evaluate our method on the MS-COCO large scale generic object dataset as well as on three class-specific counting datasets: UCSD pedestrian dataset, and CARPK and PUCPR+ car datasets.

A fast mass spring model solver for high-resolution elastic objects

Real-time simulation of elastic objects is of great importance for computer graphics and virtual reality applications. The fast mass spring model solver can achieve visually realistic simulation in an efficient way. Unfortunately, this method suffers from resolution limitations and lack of mechanical realism for a surface geometry model, which greatly restricts its application. To tackle these problems, in this paper we propose a fast mass spring model solver for high-resolution elastic objects. First, we project the complex surface geometry model into a set of uniform grid cells as cages through *cages mean value coordinate method to reflect its internal structure and mechanics properties. Then, we replace the original Cholesky decomposition method in the fast mass spring model solver with a conjugate gradient method, which can make the fast mass spring model solver more efficient for detailed surface geometry models. Finally, we propose a graphics processing unit accelerated parallel algorithm for the conjugate gradient method. Experimental results show that our method can realize efficient deformation simulation of 3D elastic objects with visual reality and physical fidelity, which has a great potential for applications in computer animation.

Evacuation planning for plausible worst case inundation scenarios in Honolulu, Hawaii.

Honolulu is susceptible to coastal flooding hazards. Like other coastal cities, Honolulu&s long-term economic viability and sustainability depends on how well it can adapt to changes in the natural and built environment. While there is a disagreement over the magnitude and extent of localized impacts associated with climate change, it is widely accepted that by 2100 there will be at least a meter in sea level rise (SLR) and an increase in extreme weather events. Increased exposure and vulnerabilities associated with urbanization and location of human activities in coastal areas warrants serious consideration by planners and policy makers. This article has three objectives. First, flooding due to the combined effects of SLR and episodic hydro-meteorological and geophysical events in Honolulu are investigated and the risks to the community are quantified. Second, the risks and vulnerabilities of critical infrastructure and the surface transportation system are described. Third, using the travel demand software, travel distances and travel times for evacuation from inundated areas are modeled. Data from three inundation models were used. The first model simulated storm surge from a category 4 hurricane similar to Hurricane Iniki which devastated the island of Kauai in 1992. The second model estimates inundation based on five tsunamis that struck Hawaii. A 1-m increase in sea level was included in both the hurricane storm surge and tsunami flooding models. The third model used in this article generated a 500-year flood event due to riverine flooding. Using a uniform grid cell structure, the three inundation maps were used to assess the worst case flooding scenario. Based on the flood depths, the ruling hazard (hurricane, tsunami, or riverine flooding) for each grid cell was determined. The hazard layer was analyzed with socioeconomic data layers to determine the impact on vulnerable populations, economic activity, and critical infrastructure. The analysis focused both on evacuation needs and the critical elements of the infrastructure system that are needed to ensure effective response and recovery in the advent of flooding. This study shows that the coastal flooding will seriously affect the economy and employment. Extreme flooding events could affect 38 percent of the freeways, 44 percent of the highways, 69 percent of the arterial roads, and 40 percent of the local streets in the area examined. Approximately 80 percent of the economy and 76 percent of the total employment in the urban core of Honolulu is exposed to flooding. Evacuation modeling, shelter accessibility, and travel time to shelter analyses revealed that there is a significant shortage in sheltering options, as well as increases in travel times and distances as inundation depth increases. The findings are useful for evacuation and shelter planning for extreme coastal events, as well as for climate change adaptation planning in Honolulu. Recommendations for emergency responders as well as those interested in the integration of long-term SLR and low probability, high consequence coastal hazards are included. The study shows how to integrate travel demand modeling across multiple hazards and threats related to evacuating, sheltering, and disaster risk reduction.

Improvement of the semi-Lagrangian advection scheme in the GRAPES model: Theoretical analysis and idealized tests

The Global/Regional Assimilation and PrEdiction System (GRAPES) is the new-generation numerical weather prediction (NWP) system developed by the China Meteorological Administration. It is a fully compressible non-hydrostatical global/regional unified model that uses a traditional semi-Lagrangian advection scheme with cubic Lagrangian interpolation (referred to as the SL_CL scheme). The SL_CL scheme has been used in many operational NWP models, but there are still some deficiencies, such as the damping effects due to the interpolation and the relatively low accuracy. Based on Reich’s semi-Lagrangian advection scheme (referred to as the R2007 scheme), the Re_R2007 scheme that uses the low- and high-order B-spline function for interpolation at the departure point, is developed in this paper. One- and two-dimensional idealized tests in the rectangular coordinate system with uniform grid cells were conducted to compare the Re_R2007 scheme and the SL_CL scheme. The numerical results showed that: (1) the damping effects were remarkably reduced with the Re_R2007 scheme; and (2) the normalized errors of the Re_R2007 scheme were about 7.5 and 3 times smaller than those of the SL_CL scheme in one- and two-dimensional tests, respectively, indicating the higher accuracy of the Re_R2007 scheme. Furthermore, two solid-body rotation tests were conducted in the latitude-longitude spherical coordinate system with nonuniform grid cells, which also verified the Re_R2007 scheme’s advantages. Finally, in comparison with other global advection schemes, the Re_R2007 scheme was competitive in terms of accuracy and flow independence. An encouraging possibility for the application of the Re_R2007 scheme to the GRAPES model is provided.

Calibration of a transient transport model to tritium data in streams and simulation of groundwater ages in the western Lake Taupo catchment, New Zealand

Abstract. Here we present a general approach of calibrating transient transport models to tritium concentrations in river waters developed for the MT3DMS/MODFLOW model of the western Lake Taupo catchment, New Zealand. Tritium has a known pulse-shaped input to groundwater systems due to the bomb tritium in the early 1960s and, with its radioactive half-life of 12.32 yr, allows for the determination of the groundwater age. In the transport model, the tritium input (measured in rainfall) passes through the groundwater system, and the simulated tritium concentrations are matched to the measured tritium concentrations in the river and stream outlets for the Waihaha, Whanganui, Whareroa, Kuratau and Omori catchments from 2000–2007. For the Kuratau River, tritium was also measured between 1960 and 1970, which allowed us to fine-tune the transport model for the simulated bomb-peak tritium concentrations. In order to incorporate small surface water features in detail, an 80 m uniform grid cell size was selected in the steady-state MODFLOW model for the model area of 1072 km2. The groundwater flow model was first calibrated to groundwater levels and stream baseflow observations. Then, the transient tritium transport MT3DMS model was matched to the measured tritium concentrations in streams and rivers, which are the natural discharge of the groundwater system. The tritium concentrations in the rivers and streams correspond to the residence time of the water in the groundwater system (groundwater age) and mixing of water with different age. The transport model output showed a good agreement with the measured tritium values. Finally, the tritium-calibrated MT3DMS model is applied to simulate groundwater ages, which are used to obtain groundwater age distributions with mean residence times (MRTs) in streams and rivers for the five catchments. The effect of regional and local hydrogeology on the simulated groundwater ages is investigated by demonstrating groundwater ages at five model cross-sections to better understand MRTs simulated with tritium-calibrated MT3DMS and lumped parameter models.

A second-order virtual node algorithm for nearly incompressible linear elasticity in irregular domains

We present a cut cell method in R2 for enforcing Dirichlet and Neumann boundary conditions with nearly incompressible linear elastic materials in irregular domains. Virtual nodes on cut uniform grid cells are used to provide geometric flexibility in the domain boundary shape without sacrificing accuracy. We use a mixed formulation utilizing a MAC-type staggered grid with piecewise bilinear displacements centered at cell faces and piecewise constant pressures at cell centers. These discretization choices provide the necessary stability in the incompressible limit and the necessary accuracy in cut cells. Numerical experiments suggest second order accuracy in L∞. We target high-resolution problems and present a class of geometric multigrid methods for solving the discrete equations for displacements and pressures that achieves nearly optimal convergence rates independent of grid resolution.

Fuzzy optimization model for integrated management of total nitrogen loads from distributed point and nonpoint sources in watershed

A fuzzy optimization model is developed to allocate allowable total nitrogen (T-N) loads to distributed nonpoint sources (NPSs) and point sources (PSs) in a watershed for river water quality management using the linear programing technique. The watershed is divided into uniform grid cells on which T-N loads issuing from NPSs such as paddy fields, upland crop fields and cities are controlled. A geographic information system integrated with the digital elevation model facilitates computation of route lengths of surface and subsurface flows from cells to a river running through the watershed. The T-N loads discharged from their sources are assumed to decay, subject to distance-related first-order kinetics. As management goals, maximizations of total allowable NPS loads, total allowable PS loads and total yield of rice are considered from environmental and economic viewpoints. A prime constraint is an effluent limitation standard for the aggregate amount of loads that arrive at the downstream end of the river. The fuzzy sets theory helps appropriately describe vague attitudes of decision-makers (i.e., stakeholders and management authorities) in terms of constraints and conflicting goals. An application of the fuzzy optimization model, developed as an improvement over our last nonfuzzy model, to a real watershed in Shiga prefecture, Japan, demonstrates that the fuzzy model embodies our last model, and is capable of creating management alternatives for T-N load allocation in a more practical and flexible manner.

A wave-propagation based volume tracking method for compressible multicomponent flow in two space dimensions

We present a simple volume-of-fluid approach to interface tracking for inviscid compressible multicomponent flow problems in two space dimensions. The algorithm uses a uniform Cartesian grid with some grid cells subdivided by tracked interfaces, approximately aligned with the material interfaces in the flow field. A standard volume-moving procedure that consists of two basic steps: (1) the update of a discrete set of volume fractions from the current time to the next and (2) the reconstruction of interfaces from the resulting volume fractions, is employed to find the new location of the tracked interfaces in piecewise linear form at the end of a time step. As in the previous work by LeVeque and the author to front tracking based on a surface-moving procedure (R.J. LeVeque, K.-M. Shyue, Two-dimensional front tracking based on high-resolution wave propagation methods, J. Comput. Phys. 123 (1996) 354–368), a conservative high-resolution wave propagation method is applied on the resulting slightly non-uniform grid to update all the cell values, while the stability of the method is maintained by using a large time step idea even in the presence of small cells and the use of a time step with respect to the uniform grid cells. We validate our algorithm by performing the simulation of a Mach 1.22 shock wave in air over a circular R22 gas bubble, where sensible agreement of some key flow features of the computed solutions are observed when direct comparison of our results are made with the existing experimental and numerical ones that appear in the literature. Other computations are also presented that show the feasibility of the algorithm together with a mixture type of the model equations developed by the author (K.-M. Shyue, A fluid-mixture type algorithm for compressible multicomponent flow with Mie–Grüneisen equation of state, J. Comput. Phys. 171 (2001) 678–707) for practical multicomponent problems with general compressible materials characterized by a Mie–Grüneisen form of the equation of state.

A Simple Volume Tracking Method For Compressible Two-Phase Flow

Our goal is to present a simple volume-of-fluid type interface-tracking algorithm to compressible two-phase flow in two space dimensions. The algorithm uses a uniform underlying Cartesian grid with some cells cut by the tracked interfaces into two subcells. A volume-moving procedure that consists of two basic steps: (1) the update of volume fractions in each grid cell at the end of the time step, and (2) the reconstruction of interfaces from discrete set of volume fractions, is employed to follow the dynamical behavior of the interface motion. As in the previous work with a surface-tracking procedure for general front tracking (LeVeque & Shyue 1995, 1996), a high resolution finite volume method is then applied on the resulting slightly nonuniform grid to update all the cell values, while the stability of the method is maintained by using a large time step wave propagation approach even in the presence of small cells and the use of a time step with respect to the uniform grid cells. A sample preliminary numerical result for an underwater explosion problem is shown to demonstrate the feasibility of the algorithm for practical problems.

Two-Dimensional Front Tracking Based on High Resolution Wave Propagation Methods

We present a fully conservative, high resolution approach to front tracking for nonlinear systems of conservation laws in two space dimensions. An underlying uniform Cartesian grid is used, with some cells cut by the front into two subcells. The front is moved by solving a Riemann problem normal to each segment of the front and using the motion of the strongest wave to give an approximate location of the front at the end of the time step. A high resolution finite volume method is then applied on the resulting slightly irregular grid to update all cell values. A “large time step” wave propagation algorithm is used that remains stable in the small cut cells with a time step that is chosen with respect to the uniform grid cells. Numerical results on a radially symmetric problem show that pointwise convergence with order between 1 and 2 is obtained in both the cell values and location of the front. Other computations are also presented.

An aid to two-dimensional contouring using nonuniform orthogonal grids—a fortran algorithm

Data (Z) which are a function of two independent variables (e.g. x and y) may be presented in the form of “contour plots”. Most readily available and inexpensive contouring programs require an orthogonal grid with uniform grid cell size. This paper reviews a simple algorithm that increases the versatility of conventional contour programs by permitting the use of grids in which Δx and Δy can differ with x and y respectively. Contours obtained in this manner should be used with care if slopes or trends are to be calculated. This algorithm has applications for data presentation in several specialized fields, including geophysics and oceanography.