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Derivation of population distribution for vulnerability assessment in flood-prone German cities using multisensoral remote sensing data

机译:使用多传感器遥感数据推导易发洪水的德国城市中的脆弱性评估中​​的人口分布

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摘要

Against the background of massive urban development, area-wide and up-to-date spatial information is in demand. However, for many reasons this detailed information on the entire urban area is often not available or just not valid anymore. In the event of a natural hazard - e.g. a river flood - it is a crucial piece of information for relief units to have knowledge about the quantity and the distribution of the affected population. In this paper we demonstrate the abilities of remotely sensed data towards vulnerability assessment or disaster management in case of such an event. By means of very high resolution optical satellite imagery and surface information derived by airborne laser scanning, we generate a precise, three-dimensional representation of the landcover and the urban morphology. An automatic, object-oriented approach detects single buildings and derives morphological information - e.g. building size, height and shape - for a further classification of each building into various building types. Subsequently, a top-down approach is applied to distribute the total population of the city or the district on each individual building. In combination with information of potentially affected areas, the methodology is applied on two German cities to estimate potentially affected population with a high level of accuracy.
机译:在大规模城市发展的背景下,需要区域范围和最新的空间信息。然而,由于许多原因,有关整个市区的详细信息通常不可用或不再有效。如果发生自然灾害-例如河流洪水-这是救济单位了解受影响人口的数量和分布的关键信息。在本文中,我们演示了在发生此类事件时,遥感数据可用于脆弱性评估或灾难管理的能力。通过非常高分辨率的光学卫星图像和机载激光扫描得出的表面信息,我们生成了土地覆被和城市形态的精确三维表示。一种自动的,面向对象的方法可以检测单个建筑物并得出形态信息-例如建筑物的大小,高度和形状-用于将每座建筑物进一步分类为各种建筑物类型。随后,采用自上而下的方法将城市或区域的总人口分配到每个单独的建筑物上。结合潜在受影响地区的信息,该方法已应用于两个德国城市,以较高的准确性估算了潜在受影响的人口。

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