LOOM (landslide object-oriented model) is here presented as a data structure for landslide inventories based on the object-oriented paradigm. It aims at the effective storage, in a single dataset, of the complex spatial and temporal relations between landslides recorded and mapped in an area and at their manipulation. Spatial relations are handled through a hierarchical classification based on topological rules and two levels of aggregation are defined: (i) landslide complexes, grouping spatially connected landslides of the same type, and (ii) landslide systems, merging landslides of any type sharing a spatial connection. For the aggregation procedure, a minimal functional interaction between landslide objects has been defined as a spatial overlap between objects. Temporal characterization of landslides is achieved by assigning to each object an exact date or a time range for its occurrence, integrating both the time frame and the event-based approaches. The sum of spatial integrity and temporal characterization ensures the storage of vertical relations between landslides, so that the superimposition of events can be easily retrieved querying the temporal dataset. The here proposed methodology for landslides inventorying has been tested on selected case studies in the Cilento UNESCO Global Geopark (Italy). We demonstrate that the proposed LOOM model avoids data fragmentation or redundancy and topological inconsistency between the digital data and the real-world features. This application revealed to be powerful for the reconstruction of the gravity-induced deformation history of hillslopes, thus for the prediction of their evolution.

中文翻译：

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滑坡时空面向对象数据模型 (LOOM)

LOOM（滑坡面向对象模型）在此表示为基于面向对象范式的滑坡清单数据结构。它旨在在单个数据集中有效存储在一个区域内记录和绘制的滑坡之间的复杂空间和时间关系以及它们的操作。空间关系通过基于拓扑规则的分层分类进行处理，并定义了两个级别的聚合：(i) 滑坡复合体，将空间上连接的相同类型的滑坡分组，以及 (ii) 滑坡系统，合并共享空间的任何类型的滑坡联系。对于聚合过程，滑坡对象之间的最小功能相互作用被定义为对象之间的空间重叠。滑坡的时间特征是通过为每个对象分配其发生的确切日期或时间范围来实现的，整合时间框架和基于事件的方法。空间完整性和时间特征的总和确保了滑坡之间垂直关系的存储，从而可以通过查询时间数据集轻松检索事件的叠加。此处提议的滑坡清查方法已在 Cilento UNESCO 世界地质公园（意大利）的选定案例研究中进行了测试。我们证明了所提出的 LOOM 模型避免了数字数据与现实世界特征之间的数据碎片或冗余和拓扑不一致。该应用程序显示出对于山坡重力诱导变形历史的重建具有强大的作用，