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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >AN OBJECT-BASED LAND-USE CELLULAR AUTOMATA MODEL TO OVERCOME CELL SIZE AND NEIGHBORHOOD SENSITIVITY
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AN OBJECT-BASED LAND-USE CELLULAR AUTOMATA MODEL TO OVERCOME CELL SIZE AND NEIGHBORHOOD SENSITIVITY

机译：基于对象的土地利用蜂窝自动机模型，用于克服细胞大小和邻域敏感性

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Cellular automata (CA) are individual-based spatial models increasingly used to simulate the dynamics of natural and human systems and forecast their evolution. Despite their simplicity, they can exhibit extraordinary rich behavior and are remarkably effective at generating realistic simulations of land-use patterns and other spatial structures. However, recent studies have demonstrated that the standard raster-based CA models are sensitive to spatial scale, more specifically to the cell size and neighborhood configuration used for the simulation. To overcome spatial scale dependency, a novel object-based CA model has been developed where space is represented using a vector structure in which the polygons correspond to meaningful geographical entities composing the landscape under study. The proposed object-based CA model allows the geometric transformation of each polygon, expressed as a change of state in part or in totality of its surface, based on the influence of its respective neighbors. In addition, the concept of dynamic neighborhood has been implemented where the neighborhood relationships among objects are defined semantically, that is two objects are neighbors if they are separated by 0, 1 or more objects whose states favor the state transition between them. This flexible neighborhood definition removes any restriction of distance to identify neighborhood relationships among objects, therefore overcoming the neighborhood configuration sensitivity present in the traditional raster CA models. The model was tested to simulate the land-use/land-cover changes in a sub-area of the Elbow river watershed, located in Southwest Alberta, Canada. The results reveal that the object-based CA model generates an adequate evolution of the geographic objects and a spatial configuration of the landscape patches that is more realistic than the one produced by a conventional raster-based CA model. The model also produces land-use patterns that are very similar to the reference maps.
机译：元胞自动机（CA）是越来越多地用于模拟自然系统和人类系统的动态基于个体的空间模型，并预测其发展。尽管它们的简单性，它们可以表现出非凡的丰富的行为，并且在产生的土地使用模式和其它空间结构现实的模拟显着有效。然而，最近的研究表明，标准的基于光栅-CA模型是空间尺度，更具体地用于模拟细胞的大小和邻里配置敏感。为了克服空间尺度依赖性，一种新颖的基于对象的CA模型已开发在空间使用其中的多边形对应于构成所研究的景观有意义地理实体的载体结构表示。所提出的基于对象的CA模型允许每个多边形的几何变换，表示为状态的部分或在其表面的全部的变化，是根据其各自的邻居的影响。此外，动态邻域的概念已被实现，其中对象之间的邻里关系在语义上定义的，即两个对象是邻居，如果他们被0,1或多个对象，其状态有利于它们之间的状态转变分离。这种灵活的附近定义消除距离的任何限制，以确定对象间相邻关系，因此克服附近配置灵敏度存在于传统的光栅CA模型。该模型进行了测试，以模拟在弯头河流域，地处鲁西南加拿大阿尔伯塔省的一个子区域的土地利用/土地覆盖变化。结果表明，基于对象的CA模型生成地理对象的足够演变和景观斑块比由常规的基于光栅的CA模型所产生的一个更为现实的的空间配置。该模型还产生非常类似于参考地图土地利用模式。

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