A modular graph transformation rule set for IFC‐to‐CityGML conversion

Conversion of Industry Foundation Classes (IFC) building models into CityGML city models is one of the operational scenarios for BIM–GIS integration, with a variety of applications producing and consuming data on either side. Given the in‐depth cross‐domain knowledge required to specify such conversions, the heterogeneity of the IFC input data and the use cases for the resulting CityGML, flexible and configurable solutions are needed that make conversion details accessible to domain specialists. Graph transformation as a conversion method fulfils these requirements. We propose to extend the modularity given by single transformation rules at a more coarse‐grained level and identify four layers with modules of associated rules. We describe a self‐contained set of rules across these modules and demonstrate its application to a range of building models.     

Estimation of parameters in a split functional model of geodetic observations (<Emphasis Type="Italic">M</Emphasis><Subscript>split</Subscript> estimation)

The paper presents a method of estimating parameters in two competitive functional models. The models considered here are concerned with the same observation set and are based on the assumption that an observation may result from a realization of either of two different random variables. These variables differ from one another at least in the main characteristic (for example, outliers can be realizations of one variable). A quantity that describes the opportunity of identifying a single observation with one random variable is assumed to be known. That quantity, called the elementary split potential, is strictly referred to the amount of information that an observation can provide about two competitive assumptions concerning the observation distribution. Parameter assessments that maximize the global elementary split potential (concerning all observations), are called M _split estimators. A generalization of M _split estimation presented in the paper refers to the theoretical foundation of M-estimation. An erratum to this article can be found at     

A Temporal Variant‐Invariant Validation Approach for Agent‐based Models of Landscape Dynamics

Agent‐based modeling provides a means for addressing the way human and natural systems interact to change landscapes over time. Until recently, evaluation of simulation models has focused on map comparison techniques that evaluate the degree to which predictions match real‐world observations. However, methods that change the focus of evaluation from patterns to processes have begun to surface; that is, rather than asking if a model simulates a correct pattern, models are evaluated on their ability to simulate a process of interest. We build on an existing agent‐based modeling validation method in order to present a temporal variant‐invariant analysis (TVIA). The enhanced method, which focuses on analyzing the uncertainty in simulation results, examines the degree to which outcomes from multiple model runs match some reference to how land use parcels make the transition from one land use class to another over time. We apply TVIA to results from an agent‐based model that simulates the relationships between landowner decisions and wildfire risk in the wildland‐urban interface of the southern Willamette Valley, Oregon, USA. The TVIA approach demonstrates a novel ability to examine uncertainty across time to provide an understanding of how the model emulates the system of interest.     

<Emphasis Type="Italic">3D</Emphasis> Modelling of Urban Area Using Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor.     

Three‐dimensional terrain modeling with multiple‐source illumination

Three‐dimensional (3D) terrain modeling based on digital elevation models (DEMs) with the use of orthographic and perspective projections is a standard procedure implemented in many commercial and open‐source geoinformation systems. However, standard tools may be insufficient for 3D scientific visualization. In particular, single‐source illumination of 3D models may be deficient for topographically complex terrains. We present an approach for 3D terrain modeling with multiple‐source illumination in the virtual environment of the Blender free and open‐source software. The approach includes the following key stages: (1) automatic creation of a polygonal object; (2) selecting an algorithm to model the 3D geometry; (3) selecting a vertical exaggeration scale; (4) selecting types, parameters, a number, and positions of light sources; (5) selecting methods for generating shadows; (6) selecting a shading method for the 3D model; (7) selecting a material for the 3D model surface; (8) overlaying a texture on the 3D model; (9) setting a virtual camera; and (10) rendering the 3D model. To illustrate the approach, we processed a test DEM extracted from the International Bathymetric Chart of the Arctic Ocean version 3.0 (IBCAO 3.0). The approach is currently being used to develop a system for geomorphometric modeling of the Arctic Ocean floor.     

An appearance‐preserving simplification method for complex 3D building models

The simplification of 3D building models to effectively reduce model complexity and improve rendering efficiency is an important component of 3D GIS. To reduce the data volume while preserving the model appearance, this article proposes a novel simplification method for complex 3D building models. Texture discontinuities are addressed by developing a new data model that records the mapping relation between the texture coordinates of each vertex and its neighboring triangles. The surface mesh of the building model is then segmented into regions, guided by topology and appearance. Finally, the mesh segmentation information is used to derive an improved error metric that considers both geometric and texture errors, and the texture coordinates are adjusted after each simplification operation. A series of comparative experiments alongside traditional methods demonstrates that our approach achieves a good balance between geometric fidelity and texture preservation, and produces simplified 3D building models with better visual quality.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

Knowledge Transfer for Large‐Scale Urban Growth Modeling Based on Formal Concept Analysis

Cellular automata (CA) are useful for studies on urban growth and land‐use changes. Although various methods have been developed to define transition rules, modeling urban growth of large areas remains a tough challenge owing to heterogeneous geographical features. To address the problem, we present a novel method based on the combination of Formal Concept Analysis (FCA) and knowledge transfer techniques. FCA is used to solicit association rules among cities within a large area. This method can provide a theoretical basis for the knowledge transfer process. A cutting‐edge algorithm called TrAdaBoost is then integrated with the commonly‐used Logistic‐CA as the modeling framework. The proposed method is applied to the urban growth modeling of Guangdong Province, a large region with 21 cities in China, from 2005 to 2008. Compared with traditional methods, this method can achieve better results at the provincial and local levels, according to the experiments. The combination of FCA and knowledge transfer is expected to provide a useful tool for calibrating large‐scale urban CA models.     

Street‐segment‐based salt and abrasive prediction for winter maintenance using machine learning and GIS

Salting operations are essential but expensive in northern winters. We aim to predict the quantity of salt and abrasive needed for a specific road segment for each hour. An estimate of the quantity allows managers to better manage the loads in the trucks and to propose optimal vehicle routes based on the forecast, which will allow them to optimize costs. This article uses machine‐learning techniques based on truck telemetry data, weather conditions, and segment attributes. Geographic information systems (GIS) allow us to exploit the street‐network characteristics, which were ignored by previous prediction models. The results show that the XGBoost method performs better than other techniques (R² = 0.83).     

非线性模型线性近似的容许曲率

由于不同的非线性模型具有不同的非线性强度，使得一些非线性模型可以线性近似，而另一些则不能。本文介绍度量非线性强度的方法，提出判断非线性模型能否线性近似的数值标准——容许曲率     

Spatially explicit models for exploring COVID‐19 lockdown strategies

This article describes two spatially explicit models created to allow experimentation with different societal responses to the COVID‐19 pandemic. We outline the work to date on modeling spatially explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters, and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalized models for mitigation provide significant advantages over a “one‐size‐fits‐all” approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.     

Similarity Weighted Instance‐based Learning for the Generation of Transition Potentials in Land Use Change Modeling

Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user‐friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance‐based learning algorithm based on the logic of the K‐Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi‐Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform.     

An enhanced approach for surface flow routing over drainage‐constrained triangulated irregular networks

The accuracy and efficiency of the simulations in distributed hydrological models must depend on the proper estimation of flow directions and paths. Numerous studies have been carried out to delineate the drainage patterns based on gridded digital elevation models (DEMs). The triangulated irregular network (TIN) has been increasingly applied in hydrological applications due to the advantages of high storage efficiency and multi‐scale adaptive performance. Much of the previous literature focuses mainly on filling the depressions on gridded DEMs rather than treating the special cases in TIN structures, which has hampered its applications to hydrological models. This study proposes a triangulation‐based solution for the removal of flat areas and pits to enhance the simulation of flow routing on triangulated facet networks. Based on the drainage‐constrained TIN generated from only a gridded DEM by the compound point extraction (CPE) method, the inconsistent situations including flat triangles, V‐shape flat edges and sink nodes are respectively identified and rectified. The optimization algorithm is an iterative process of TIN reconstruction, in which the flat areas are generalized into their center points and the pits are rectified by embedding break lines. To verify the proposed algorithm and investigate the potential for flow routing, flow paths of steepest descent are derived by the vector‐based tracking algorithm based on the optimized TIN. A case study of TIN optimization and flow path tracking was performed on a real‐world DEM. The outcomes indicate that the proposed approach can effectively solve the problem of inconsistencies without a significant loss in accuracy of the terrain model.     

局部表面参数化的实景三角网模型语义增强方法

倾斜摄影测量方法已可自动获取城市规模的实景三角网模型,然而散乱的三角网缺乏精细的几何结构和功能语义信息。为克服上述问题,提出一种局部表面参数化的实景三角网模型语义增强方法:将具有语义信息的独立三维部件与实景三角网模型的无缝融合问题,通过定义三维表面结构树,转换为局部区域的三角网替换操作;在待融合区域附近,将原实景三角网模型和替换的三维语义部件,通过局部参数化表达,UV展开为二维平面三角网;在二维平面上构建约束Delaunay三角网(CDT),实现两模型的无缝拼接,逆映射至三维空间并自动重建语义部件。通过深圳某区域的倾斜影像进行的试验证明,本文方法能有效实现具有开放边界和语义信息的部件模型与表面模型的无缝融合。与商业软件Maya对比,这种基于插入、融合的手段对提高建模效率具有实用价值。     

Spatial Approaches to Modeling Dispersion of Communicable Diseases – A Review

The dispersion of communicable diseases in a population is intrinsically spatial. In the last several decades, a range of spatial approaches has been devised to model epidemiological processes; and they differ significantly from each other. A review of spatially oriented epidemiological models is necessary to assess advances in spatial approaches to modeling disease dispersion and to help identify those most appropriate for specific research goals. The most notable difference in the design of these spatially oriented models is the scale and mobility of the modeling unit. Using two criteria, this review identifies six types of spatially oriented models. These include: (1) population‐based wave models, (2) sub‐population models, (3) individual‐based cellular automata models, (4) mobile sub‐population models, (5) individual‐based spatially implicit models, and (6) individual‐based mobile models. Each model type is evaluated in terms of its design principles, assumptions, and intended applications. For the evaluation of design, four aspects of design principles are discussed: the modeling unit, the interaction between the modeling units, the spatial process, and the temporal process utilized in a design. Insights gained from this review can be useful for devising much‐needed spatially and temporally oriented strategies to forecast, prevent, and control communicable diseases.     

SCOPED‐W: SCalable Online Platform for extracting Environmental Data and Water‐related model outputs

This article presents SCOPED, an innovative approach for extracting environmental data using OGC services. In the field of water resource management, SCOPED‐W (‘W’ for ‘Water’) is a method that was developed in the framework of EU/FP7 IASON and EOPOWER projects. This platform supports the collection of data required to build a Soil and Water Assessment Tool (SWAT) model and the uptake, spatialization and dissemination of raw data generated from the outputs of different SWAT models for the Black Sea region. Scientists are documenting the data served by the platform in ISO standardized metadata to support informed use. SCOPED‐W primarily targets the community of SWAT users in the Black Sea region but it can easily be replicated in other geographical areas. Additionally, the SCOPED approach is based on data interoperability that makes it fully compatible with other domains of application as demonstrated here with three original use cases. The article also highlights the benefits of the approach for the GEO community and discusses future improvements for supporting integration with other platforms such as UNEP Live.     

Remote Sensing-Based Yield Forecasting for Sugarcane (<Emphasis Type="Italic">Saccharum officinarum</Emphasis> L.) Crop in India

Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. This current study explored a remote sensing-based approach of predicting sugarcane yield, at district level, using Vegetation Condition Index (VCI), under the FASAL programme of the Ministry of Agriculture & Farmers’ Welfare. 13-years’ historical database (2003–2015) of NDVI was used to derive the VCI. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of June to second fortnight of October (peak growing period) were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of sugarcane over 52 major sugarcane-growing districts in five states of India. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level sugarcane yield. Though there was variation in model performance in different states, overall, the study showed the usefulness of VCI, which can be used as an input for operational sugarcane yield forecasting.     

Georelief Transfiguration in Areas Affected by Open‐cast Mining

Open‐cast mining activity causes the largest georelief transfigurations all over the world. Large localities in north‐west Bohemia (the Czech Republic) are affected. In this area, we focus on a very significant case: the royal town Most, which has been turned into a lake. The main aim of this article is the reconstruction of the original georelief in different time periods together with analysis showing the process of landscape devastation. The workflow and analysis is based on precise elevation data obtained from aerial photographs and old maps. The georelief development is reconstructed using the digitized contour lines contained in Derived state‐map 1:5,000 (SMO5) from the years 1953, 1972, and 1980; maps of the 3^rd Military Survey (year 1936); and digital surface models (DSM) extracted, using the pixel correlation method, from aerial images (year 1953 and 2008). The most important results of the analysis are digital terrain models showing the evolution of the landscape which may be used for many purposes in landscape development analysis, historical applications, visualization or landscape reclamation.     

非线性模型参数估计的直接解法

提出了非线性模型参数估计的直接解法。该方法不仅不需迭代,而且由于顾及了二次项和三次项的影响,参数估值的精度优于传统的线性近似时参数估值的精度,且易应用传统的精度评定理论进行精度评定。