Knowledge-Based Approach for Contextual Landsystem Identification: A Conceptual Model and Graph-Based Software, with an Application to Mountain Glacial Valleys

A landform is a physical feature of the Earth’s surface with its own recognizable shape. Most current automated landform identification methods use Object-Based Image Analysis (OBIA) techniques. Such methods segment the terrain into landform elements and assemble them into topographic objects and landforms. Usually, these methods are specific to the landform to be identified. However, geomorphologist experts can contextually recognize any landform on the Earth’s surface in relation to its environment. They have a holistic view of the landscape, adopting a physiographic approach for the interpretation of the observed regions, the objects that they contain and their relationships. Moreover, geomorphological processes leave marks on the Earth’s surface that enable geomorphologists to identify homogeneous regions by recognizing features known as structural elements. In this paper, we show that the physiographic approach can be formalized and that the context of appearance of a landform and its association with other types of landforms can be represented as a landsystem. We propose a conceptual model that organizes the main concepts and relationships characterizing the physiographic approach: they are used to formalize landsystems, landforms and structural elements. The approach is illustrated using a case study of the identification of landsystems characteristic of mountainous glacial valleys. We developed a software to automatically identify landsystems, in a way that is compatible with the geomorphologists’ physiographic approach. The core of this system is a knowledge base implemented as a Neo4j graph database. We also provide details about the logical transformation of the conceptual model and the corresponding ontologies in Noe4j structures. The tool automates the identification of landsystems in accordance with geomorphological practices, facilitating the integration of expert knowledge in the computational workflows.