Investigating the Applicability of Cartosat-1 DEMs and Topographic Maps to Localize Large-Area Urban Mass Concentrations
Investigating the Applicability of Cartosat-1 DEMs and Topographic Maps to Localize Large-Area Urban Mass Concentrations
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DOI：
10.1109/jstars.2014.2346655
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发表时间：
2014-08
影响因子：
5.5
通讯作者：
M. Wurm;P. d’Angelo;P. Reinartz;H. Taubenböck
中科院分区：
文献类型：
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作者：
M. Wurm;P. d’Angelo;P. Reinartz;H. Taubenböck
建筑模型是城市研究的宝贵信息源，特别是对城市质量集中度(UMCS)的分析。最常见的是使用光探测和测距(LiDAR)来生成它们。这些数据的高几何细节的权衡是低空间覆盖率、相对较高的成本和低实现率。Cartosat-1的星载立体数据一方面能够覆盖大片区域，但另一方面几何分辨率较低。在这篇文章中，我们研究了在多大程度上可以克服Cartosat-1的几何缺陷，将地形图中的建筑物足迹结合在一起来推导大面积的建筑物模型。因此，我们描述了从Cartosat-1数据中提取数字表面模型(DSM)的方法，以及从1：25 000地形图(DTK25)中提取建筑物足迹的方法。将这两种数据融合在一起，生成德国面积约16000平方公里的四个大都市区的积木模型。将积木模型进一步聚合为1×1公里的网格单元，并计算体密度。体积密度被划分为不同级别的UMCS。对积木模型的性能评估表明，DTK-25的建筑占地面积较大，建筑高度较低，平均绝对误差为3.21m，这两个因素都影响建筑体积，建筑体积线性低于参考体积。然而，该错误不影响UMC的分类，UMC的分类精度可以在77%到97%之间。
Building models are a valuable information source for urban studies and in particular for analyses of urban mass concentrations (UMCS). Most commonly, light detection and ranging (LiDAR) is used for their generation. The trade-off for the high geometric detail of these data is the low spatial coverage, comparably high costs and low actualization rates. Spaceborne stereo data from Cartosat-1 are able to cover large areas on the one hand, but hold a lower geometric resolution on the other hand. In this paper, we investigate to which extent the geometric shortcomings of Cartosat-1 can be overcome integrating building footprints from topographic maps for the derivation of large-area building models. Therefore, we describe the methodology to derive digital surface models (DSMs) from Cartosat-1 data and the derivation of building footprints from topographic maps at 1:25 000 (DTK25). Both data are fused to generate building block models for four metropolitan regions in Germany with an area of ~ 16 000 km2. Building block models are further aggregated to 1 × 1 km grid cells and volume densities are computed. Volume densities are classified to various levels of UMCs. Performance evaluation of the building block models reveals that the building footprints are larger in the DTK-25, and building heights are lower with a mean absolute error of 3.21 m. Both factors influence the building volume, which is linearly lower than the reference. However, this error does not affect the classification of UMC, which can be classified with accuracies between 77% and 97%.