Human-Centered Relational Feature Classification for VGI

以人为中心的 VGI 关系特征分类

• 批准号: 314697956
• 负责人: Professor Christian Freksa, Ph.D. (†)
• 金额: --
• 依托单位: GeoVISTA Center
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/314697956?language=en
• 关键词: Human; Centered; Relational; Feature; Classification

Volunteered Geographic Information (VGI) emphasizes the power of crowdsourcing as a means of data production and as a platform for exchanging geographic knowledge and providing services. In VGI, ordinary untrained citizens are involved in collecting, sharing, using, and maintaining spatial data - tasks for which previously trained experts in mapping agencies and national organizations were responsible. In this project, we will investigate data quality issues of collaborative mapping in VGI from an artificial intelligence / cognitive systems perspective. In the scope of our project, we focus on VGI as a potential source of land use and land cover mapping in which a potentially large number of citizens independently participate in mapping geographic features. For example, a piece of land covered by grass may be classified as "grassland" or more specifically as "park". From a cognitive perspective, the involvement of ordinary humans and the availability of a multitude of ways to describe geographic features pose a great challenge, as the diversity of backgrounds of the contributors may lead to incommensurable and/or faulty data. In principle, there are two general ways to deal with this situation: (1) Normative or definition-based approach: Canonical feature categories are predefined; there is one and only one correct way to classify observations. Advantages: no ambiguities on the data level; data interpretation can be performed by application of the predefined norms. Disadvantage: Data may be erroneous, as it may be hard or impossible to correctly map the observations to the predefined categories due to incomplete information and / or incompatible granularity levels. (2) Human-centered approach: Volunteers use concepts they are most familiar with and are certain about to describe geographic features. Advantages: Granularity of description can be adapted to the level of observation; the information provided is more likely to be correct. Disadvantage: There is no obvious way to relate and compare observations of different contributors and to integrate the observations in a joint map. Currently, we have no techniques to combine the advantages of both approaches and to compensate for their drawbacks. We will develop an approach to bridge this gap between definition-based and human-centered VGI. More specifically, we will make use of inherent ordering structures of space that are employed by the definition-based approach as well as by the human-centered approach. These structures will serve to mediate between the two types of descriptions. This will enable us to translate certain kinds of human-centered descriptions to definition-based descriptions on the canonical level, maintaining the advantage of preserving the correctness of the information, and vice versa. The proposed approach is based on artificial intelligence techniques of qualitative spatial representation and reasoning and exploits conceptual neighborhood structures for feature matching.

众包地理信息（VGI）强调众包作为一种数据生产手段以及作为交换地理知识和提供服务的平台的力量。在虚拟地理信息系统中，未受过训练的普通公民参与收集、共享、使用和维护空间数据-这些任务由以前受过训练的制图机构和国家组织的专家负责。在这个项目中，我们将从人工智能/认知系统的角度研究VGI中协作映射的数据质量问题。在我们的项目范围内，我们专注于VGI作为土地利用和土地覆盖测绘的潜在来源，其中可能有大量公民独立参与测绘地理特征。例如，一块被草覆盖的土地可以被分类为“草地”，或者更具体地被分类为“公园”。从认知的角度来看，普通人的参与和描述地理特征的多种方式的可用性构成了巨大的挑战，因为贡献者的背景多样性可能导致无法解释和/或错误的数据。原则上，有两种一般的方法来处理这种情况：（1）规范或基于定义的方法：规范的特征类别是预定义的;有一个且只有一个正确的方法来分类观察。优点：在数据层面上没有歧义;可以通过应用预定义的规范来进行数据解释。缺点：数据可能是错误的，因为由于不完整的信息和/或不兼容的粒度级别，可能很难或不可能将观察结果正确地映射到预定义的类别。(2)以人为本的方法：志愿者使用他们最熟悉和确定的概念来描述地理特征。优点：描述的粒度可以适应观察的水平;提供的信息更有可能是正确的。缺点：没有明显的方法来联系和比较不同贡献者的观测结果，并将观测结果整合到一个联合地图中。目前，我们还没有技术来联合收割机结合这两种方法的优点，并弥补其缺点。我们将开发一种方法来弥合基于定义和以人为本的VGI之间的差距。更具体地说，我们将利用基于定义的方法以及以人为中心的方法所采用的空间固有的有序结构。这些结构将在两种类型的描述之间起中介作用。这将使我们能够将某些类型的以人为中心的描述转换为规范级别上的基于定义的描述，保持保持信息正确性的优势，反之亦然。所提出的方法是基于人工智能技术的定性空间表示和推理，并利用概念邻域结构的特征匹配。