A Geocomputational Heuristic for Coverage Maximization in Service Facility Siting

Siting service facilities in order to maximize regional coverage is important when budget resources are limited. Various approaches exist for addressing this particular planning problem for discrete or continuous representations of potential facility sites and demand to be served. In cases where both candidate facility sites and service demand are continuous, approaches for maximizing regional coverage have only examined the siting of a single facility. In this article, a geocomputational approach is proposed for addressing multiple facility siting when demand is continuously distributed and facilities may be located anywhere in the region. Emergency warning siren location is used to highlight the developed approach.     

ROOF BOUNDARY EXTRACTION USING MULTIPLE IMAGES

Urban area building extraction is one of the most challenging problems in photogrammetry. Well-extracted buildings are needed for a variety of applications, such as cartography, building GIS databases for cities, and urban planning. This paper presents a new technique to extract 3D building wire-frames using a robust multi-image line-matching algorithm. Although one pair of images is adequate to find the 3D position of two visibly corresponding image features, it is not sufficient to solve the general building extraction problem due to obscured parts in the building. Four images are used in this research to extract the building wire-frames. First the images are segmented into regions. Regions are then classified into roof regions and non-roof regions based on their size, shape, and intensity values. The roof region boundary pixels are located and used to find the region perimeters. Region correspondence is solved in a pair-wise mode over all images using the epipolar constraint, region size, region shape, and region intensity values. Image lines within the corresponding regions are matched over all images simultaneously by first creating a plane for each region line. Planes are then intersected simultaneously and geometric consistency is used to determine acceptance or rejection. Results with high overlap and sidelap aerial images are presented and evaluated. The results show the completeness and accuracy that this method can provide for extracting complex urban buildings. The average coordinate accuracy is about 0·8 m using 1:4000 scale aerial photographs scanned at 30 μ m. Six buildings were examined; the line detection rate is 98%.     

TIN-based Tag Map Layout

With the widespread use of tag clouds, multiple map-based variations have been proposed. Like standard tag clouds (also called word clouds), these ‘tag maps’ all share the basic strategy of displaying words within a ‘geographic space’ and scaling the word size to depict frequency (or importance) of those words within some dataset. While some tag maps simply plot a standard tag cloud on top of a map, the subset of tag maps we focus on here are those in which the collection of words are displayed within bounded geographic regions (often of irregular shape) that the words are relevant for. For this form of tag map, map scale and polygon shape add constraints to word size and position that have not been considered in most prior approaches to tag map word layout. In this paper, we present a layout strategy for tag map generation that includes consideration of the shape and size of the geographical regions acting as containers for the tags. The method introduced here uses a triangulated irregular network (TIN) to subdivide the geographical region into many triangle subareas, with the centroid of each triangle being a potential location to centre a tag on. All the triangles are sorted by their area and all the tags are sorted by their weight value (e.g. frequency, importance or popularity). Positioning of tags is undertaken sequentially from most important (or frequent or popular) with potential locations being the TIN triangle centroids (tried from largest to smallest triangle). After each tag placement, the TIN is recalculated to integrate the tag centroid and bounding corners into the TIN creation. The limited whitespace in the geographical region, at any specific scale, is used fully by dynamically adjusting the font size along with the number and the direction of tags. The method can be applied to add tags within geographic polygons that are convex, concave and other more complex regions containing holes or islands.     

Hybrid region merging method for segmentation of high-resolution remote sensing images

Image segmentation remains a challenging problem for object-based image analysis. In this paper, a hybrid region merging (HRM) method is proposed to segment high-resolution remote sensing images. HRM integrates the advantages of global-oriented and local-oriented region merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region, which provides an elegant way to avoid the problem of starting point assignment and to enhance the optimization ability for local-oriented region merging. During the region growing procedure, the merging iterations are constrained within the local vicinity, so that the segmentation is accelerated and can reflect the local context, as compared with the global-oriented method. A set of high-resolution remote sensing images is used to test the effectiveness of the HRM method, and three region-based remote sensing image segmentation methods are adopted for comparison, including the hierarchical stepwise optimization (HSWO) method, the local-mutual best region merging (LMM) method, and the multiresolution segmentation (MRS) method embedded in eCognition Developer software. Both the supervised evaluation and visual assessment show that HRM performs better than HSWO and LMM by combining both their advantages. The segmentation results of HRM and MRS are visually comparable, but HRM can describe objects as single regions better than MRS, and the supervised and unsupervised evaluation results further prove the superiority of HRM.     

Automatic Determination of Number of Homogenous Regions in SAR Images Utilizing Splitting and Merging Based on a Reversible Jump MCMC Algorithm

This paper presents an algorithm dealing with initial segmentation of speckled Synthetic Aperture Radar (SAR) intensity images in order to automatically determine the number of homogeneous regions. Taking this problem into account, segmentation procedure utilizing splitting and merging is designed, iteratively. The proposed approach is based upon Bayesian inference, a maximum likelihood gamma distribution parameter estimator, and a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using of image splitting operation, SAR image is partitioned into finite regions iteratively, until all individual regions are coherent. Then each region is assigned a unique label to indicate the class to which the homogeneous region belongs. The intensities of pixels in each coherent region are assumed to satisfy identical and independent gamma distribution. Then an RJMCMC scheme is designed to simulate the posterior distribution in order to estimate the number of components and delineate an initial segmentation. Thus, the main purpose of this research is to define the number of homogeneous regions rather than a perfect segmentation, i.e. model outputs can be served for unsupervised segmentation methodologies as prior information. The results obtained from Radarsat-1/2 of SAR intensity images show that the proposed algorithm is both capable and reliable in defining the accurate number of homogeneous regions in a wide variety of SAR intensity images, comprising a high level of speckle noise.     

一种顾及空间关系的遥感影像阴影检测算法

以城市区域内高大建筑阴影为研究对象,针对现有的阴影检测算法在复杂地物环境下检测精度和可靠性不高的问题,提出了一种结合颜色空间特征和空间关系的遥感影像阴影检测方法。首先,采用SLIC超像素算法对影像进行分割;然后基于Lab和HSI颜色空间构建初步检测条件,将阴影划分为阴影主体区域和待检测区域;最后,借助Canny边缘检测信息合并待判别区域内的超像素块,并利用阴影区域与造成干扰区域间的空间位置关系构建的检测条件进行判别。实验结果表明,该方法可以有效提高复杂地物环境下遥感影像阴影的检测精度和算法可靠性。     

Spatial representation in the social interaction potential metric: an analysis of scale and parameter sensitivity

The social interaction potential (SIP) metric measures urban structural constraints on social interaction opportunities of a metropolitan region based on the time geographic concept of joint accessibility. Previous implementations of the metric used an interaction surface based on census tracts and the locations of their centroids. This has been shown to be a shortcoming, as the metric strongly depends on the scale of the zoning system in the region, making it difficult to compare the SIP metric between metropolitan regions. This research explores the role of spatial representation in the SIP metric and identifies a suitable grid-based representation that allows for comparison between regions while retaining cost-effectiveness with respect to computational burden. We also report on findings from an extensive sensitivity analysis investigating the SIP metric’s input parameters such as a travel flow congestion factor and the length of the allowable time budget for social activities. The results provide new insights on the role of the modifiable areal unit problem in the computation of time geographic measures of accessibility.     

Capturing Raster Data From Scanned Thematic Maps Using Desktop Graphics Software

Data capture from paper maps remain a slow and costly part of many Geographic Information System projects. In this paper a simple method is described which makes use of the facilities of desktop graphics software to capture raster data from scanned thematic maps. The first stage is to use the flood fill tool to identify the regions of the map that represent the data values and set them to a separate colour for each thematic category. This edited image is then imported into a GIS, where a specially written majority filter is used to classify any remaining pixels to produce a raster GIS layer. The technique is tested on samples from three maps, giving accuracy levels of over 90% in all cases when compared with digitising the same maps by hand. The technique could be useful for situations where a rapid and simple means of data capture is needed and could be developed further by drawing on techniques for contextual classification rather than relying on a general purpose flood fill algorithm.     

Study of spectral reflectance characteristics of an urban environment — Case study of Kanpur city

Spectral reflectance Studies have been carried out on various features (man-made as well as natural) with reference to urban environment using a portable spectro-radiometer in wavelength regions ranging from 0.45 to 1.0 μm of Kanpur city and its surrounding areas. The signature values, thus collected, were used to draw spectral reflectance curves of each feature separately and to determine/select the optimum wavelength regions suitable for urban area studies. It has been observed that the best band width suitable for urban feature discrimination are between 0.45 to 0.55 μm (blue region) & 0.69 to 0.80 μm (near-infrared region) of the spectrum.     

DSSRM级联分割的SAR图像变化检测

SRM(Statistical Region Merging)分割算法具有快速、稳定和抗噪强的优点,基于此,本文提出一种基于DSSRM(Dynamic Sorting Statistical Region Merging)级联分割的SAR图像变化检测方法。首先,针对SRM算法基于单特征静态排序导致的过分割问题,提出一种动态排序模式的DSSRM算法以减少差异图像分割错误,该算法建立基于合并区域的多特征马氏距离排序准则,在每次合并之后更新区域邻接矩阵并重新排序;然后,基于互信息最小化准则构造多通道差异数据集以提高算法对区域合并的约束能力;最后,提出一种级联分割变化检测框架,第1级利用SRM算法将差异图像映射到超像素空间,第2级采用DSSRM算法对超像素进行动态合并获得收敛的分割结果,第3级采用简化SRM方法进行三次合并获得最终的变化检测图。实验结果表明,该方法可以获得比SRM方法和目前流行方法更好的检测性能。     

Robust river boundaries extraction of dammed lakes in mountain areas after Wenchuan Earthquake from high resolution SAR images combining local connectivity and ACM

River boundaries extraction from SAR imagery is valuable for flood monitoring and damage assessment. Several rivers, parts of which include dammed lakes caused by landslides and rock avalanches triggered by the 2008 Wenchuan Earthquake, were taken as a case study for robust extraction. In this paper, a novel state-of-the-art approach for automated river boundaries extraction using high resolution synthetic aperture radar (SAR) intensity imagery is presented. The key of our approach lies in the combined usage of local connectivity feature of the river and a region-based active contours model (ACM) in a variational level set framework to differentiate between river and the background. First, sub-patched intensity thresholding segmentation is applied to SAR imagery. Pixels with intensities below the threshold are selected as potential river pixels while the others are potential background pixels. Second, potential river pixels are divided into several connected regions, considering that the river is a big connected region, only relatively bigger regions with similar contrast value are retained as the regions of interest (ROI) while others are noise due to pixel-level decision approach in the first step or shadows due to mountains terrain. Third, the ROI and their contours are regarded as local region and the initial contours to refine the river boundaries, which are used to reduce the scene complexity of ACM and its sensitivity to initial situation, respectively. A novel ACM driven by local image fitting (LIF) energy is presented and used for river boundaries extraction for the first time, which is not only robust against inhomogeneity widely spread in SAR imagery but also can work with efficiency without the need of re-initialization during iteration compared to traditional ACM. The proposed approach was tested on numerous high resolution airborne SAR images containing connected rivers or dammed lakes obtained by Chinese domestic radar system after Wenchuan Earthquake. For the overall dataset, the average commission error, omission error and root mean squared error were 6.5%, 3.3%, and 0.51, respectively. The average computational time for 4000 by 4000 image size was 21 min using a PC-based MATLAB platform. Our experimental results demonstrate that the proposed approach is robust and effective.     

Confidence regions for GPS baselines by Bayesian statistics

 The global positioning system (GPS) model is distinctive in the way that the unknown parameters are not only real-valued, the baseline coordinates, but also integers, the phase ambiguities. The GPS model therefore leads to a mixed integer–real-valued estimation problem. Common solutions are the float solution, which ignores the ambiguities being integers, or the fixed solution, where the ambiguities are estimated as integers and then are fixed. Confidence regions, so-called HPD (highest posterior density) regions, for the GPS baselines are derived by Bayesian statistics. They take care of the integer character of the phase ambiguities but still consider them as unknown parameters. Estimating these confidence regions leads to a numerical integration problem which is solved by Monte Carlo methods. This is computationally expensive so that approximations of the confidence regions are also developed. In an example it is shown that for a high confidence level the confidence region consists of more than one region. Received: 1 February 2001 / Accepted: 18 July 2001     

Re-examining urban region and inferring regional function based on spatial–temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents’ needs. Understanding the changes of regional structure and dynamics of city function based on the residents’ movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T)?×?week (W)?×?origin (O)?×?destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.     

A site selection framework for urban power substation at micro-scale using spatial optimization strategy and geospatial big data

The world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations and optimizing the location of existing ones are necessary to address the energy crisis. The current site selection lacks consideration of spatial and temporal heterogeneity in urban power demand, which results in unreasonable energy transfer and waste, leading to power outages in some areas. Aiming to maximize the grid coverage and transformer utilization, we propose a multi-scene micro-scale urban substation siting framework (UrbanPS): (1) The framework uses multi-source big data and the machine learning model to estimate fine-scale power consumption for different scenarios; (2) the region growing algorithm is used to divide the power supply area of substations; and the (3) location set coverage problem and genetic algorithm are introduced to optimize the substation location. The UrbanPS was used to perform siting optimization of 110 kV terminal substations in Pingxiang City, Jiangxi Province. Results show that the coverage and utilization rate of the optimization results under different power consumption scenarios are close to 99%. We also found that the power can be saved by dynamic regulation of substation operation.     

基于时刻独立脉冲耦合神经网络的高空间分辨率摇感影像分割

高空间分辨率遥感影像中地物目标内部光谱信息复杂性的增强,使得传统基于光谱特征值的数据处理方法效果不再显著,影像分割为解决这一问题提供了一种思路,成为当前高空间分辨率遥感影像处理的研究焦点.时刻独立脉冲耦合神经网络具有状态相近、空间相邻神经元相互耦合同步脉冲激发和区域之间神经元脉冲激发时刻独立两大特点,已被应用于非遥感影像分割中,并取得较好效果.本文结合高空间分辨率遥感影像特点,通过对网络参数进行实验和分析,提出一个基于时刻独立脉冲耦合神经网络的高空间分辨率遥感影像分割方法,并利用空间分辨率0.3m的航空影像进行了数据试验,将分割结果进行讨论并与现有时刻独立脉冲耦合神经网络方法和ISODATA方法分割结果进行对比分析.结果表明:时刻独立脉冲耦合神经网络在高空间分辨率遥感影像分割处理中具有很好的应用前景.     

Using GIS and K = 3 Central Place Lattices for Efficient Solutions to the Location Set‐Covering Problem in a Bounded Plane

One of the simplest location models in terms of its constraint structure in location‐allocation modeling is the location set‐covering problem (LSCP). Although there have been a variety of geographic applications of the set‐covering problem (SCP), the use of the SCP as a facility location model is one of the most common. In the early applications of the LSCP, both potential facility sites as well as demand were represented by points discretely located in geographic space. The advent of geographic information systems (GIS), however, has made possible a greater range of object representations that can reduce representation error. The purpose of this article is to outline a methodology using GIS and K = 3 central place lattices to solve the LSCP when demand is continuously distributed over a bounded area and potential facility sites have not been defined a priori. Although, demand is assumed to exist over an area, it is shown how area coverage can be accomplished by the coverage of a point pattern. Potential facility site distributions based on spacings that are powers of one‐third the coverage distance are also shown to provide more efficient coverage than arbitrarily chosen spacings. Using GIS to make interactive adjustments to an incomplete coverage also provides an efficient alternative to smaller spacings between potential facility sites for reducing the number of facilities necessary for complete coverage.     

边界约束最大p区域问题及其启发式算法

针对城市空间内的自动化分区,顾及空间域边界对于分区结果的约束效应,提出一种边界约束最大p区域问题。在最大化区域个数p前提下,针对单元与多个边界交叉产生的单元从属不确定性,设计一种顾及空间单元从属不确定度的单元差异性加权目标函数。并在满足阈值约束等最大p区域问题原有约束下,增加若干边界约束,保证形成的区域一般在某个边界之内,若需跨越多个边界,则需涵盖整个边界。针对该非确定性多项式难题设计并实现一种基于禁忌搜索的启发式算法,并在模拟数据和实际数据集上进行实验。实验结果表明,该方法可以使科研和实验人员能够将现实世界中的边界约束灵活地加入到分区问题的模型中,以对最大p区域问题的求解结果进行更为实际的控制。     

A Computational Method for Estimating the Demand of Retail Stores on a Street Network and its Implementation in GIS

This paper describes a computational method for estimating the demand of retail stores on a street network using GIS. First, the 'network Huff model' is formulated on a network with the shortest-path distance as an extension of the ordinary Huff model (which assumes a continuous plane with Euclidean distance). Second, using this model, a formula for estimating the demand is derived. This estimation formula is similar to that with the ordinary Huff model, but it has an advantage in that the formula exactly computes the demand on a network. Third, a practical method for computing the formula is developed. Finally, a method of implementing this computational method in a GIS environment is shown.     

Basin-wide hydrological system assessment under climate change scenarios through conceptual modelling

ABSTRACT

Mediterranean region is identified as a primary hot-spot for climate change due to the expected temperature and rainfall changes. Understanding the potential impacts of climate change on the hydrology in these regions is an important task to develop long-term water management strategies. The aim of this study was to quantify the potential impacts of the climate changes on local hydrological quantities at the Goksu Watershed at the Eastern Mediterranean, Turkey as a case study. A set of Representative Concentration Pathways (RCP) scenarios were used as drivers for the conceptual hydrological model J2000 to investigate how the hydrological system and the underlying processes would respond to projected future climate conditions. The model was implemented to simulate daily hydrological quantities including runoff generation, Actual Evapotranspiration (AET) and soil-water balance for present (2005–2015) and future (up to 2100). The results indicated an increase of both precipitation and runoff throughout the region from January to March. The region showed a strong seasonally dependent runoff regime with higher flows during winter and spring and lower flows in summer and fall. The study provides a comparative methodology to include meteorological-hydrological modelling integration that can be feasible to assess the climate change impacts in mountainous regions.     

多源遥感影像高精度自动配准的方法研究

提出并发展了一套对多源遥感影像(不同传感器、不同分辨率、不同时相的遥感影像)的高精度自动纠正与配准技术与方法。遥感影像首先通过一个多项式模型进行整体粗纠正,然后在待配准影像上提取均匀分布的特征点。以提取的特征点为引导,利用金字塔逐层模板匹配的方法获得配准用同名控制点,基此构建不规则三角网将影像分解为各三角形区域,在每个三角形范围内实现逐三角网的高精度影像纠正与配准。