Classification and clustering of buildings for understanding urban dynamics

IF 0.2 Q4 REMOTE SENSING
Joan Perez, Giovanni Fusco, Y. Sadahiro
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Abstract

This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and for each period is computed, as well as the differences between each period. Mappings are then performed, showing that building types have specific locations and dynamics. In some extreme cases, a specific building type can even gradually replace a type on a declining dynamic. Questions of data preparation, and clustering validation are also dealt with, underlining the interest of assessing the spatial distribution of clusters.
建筑的分类和聚类,以了解城市动态
本文提出了在建筑层面研究城市动力学的不同方法。建筑类型是在一个基于矢量的综合建筑清单中确定的,至少跨越两个时间点。首先,计算每栋建筑的基本形态指标:面积、建筑面积、邻居数量、延伸率和凸度。基于专家知识的可用性,进行了不同类型的分类和聚类:监督树状分类模型、专家约束k-均值和组合SOM-HCA。将网格叠加在大阪(日本)的测试区域上,计算每个单元和每个周期的建筑类型数量,以及每个周期之间的差异。然后执行映射,显示建筑类型具有特定的位置和动力学。在某些极端情况下,特定的建筑类型甚至可以逐渐取代动态下降的类型。还讨论了数据准备和聚类验证问题,强调了评估聚类空间分布的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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