ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 17–24, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-17-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 17–24, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-17-2022
 
18 May 2022
18 May 2022

A COGNITIVE APPROACH FOR LANDSYSTEM IDENTIFICATION USING A GRAPH DATABASE – TOWARDS THE IDENTIFICATION OF LANDFORMS IN CONTEXT

H. Ramiaramanana1, E. Guilbert1, and B. Moulin2 H. Ramiaramanana et al.
  • 1Dept. of Geomatics Sciences, Laval University, Québec, G1V 0A6 (QC), Canada
  • 2Professor Emeritus, Dept. of Computer Science and Software Engineering, Laval University, Québec, G1V 0A6 (QC), Canada

Keywords: landsystem, landform identification, knowledge modelling, graph database, knowledge-based system

Abstract. A landform is any physical feature of the earth's surface having a characteristic, recognizable shape. Most landform identification methods rely on OBIA (Object-Based Image Analysis) techniques to segment the terrain data and classify segments into objects that are assumed to compose the landform. However, geomorphologists can visually recognize any landform, considering the characteristics of the surrounding environment that plays the role of context. This notion of context was not considered in previous landform identification methods. We propose to model it using the notion of landsystem. Landsystems are geomorphologic elements that result from a set of natural geomorphological processes. They are also easily recognized by geomorphologists. In this paper, we present a new knowledge-based method to automatically identify landsystems as the context for landform identification. We first present a conceptual model as a core ontology of geomorphologic elements including landsystems and landforms, capturing relevant geomorphologists’ knowledge. Then, we present how this model is extended to create a domain ontology for a chosen domain in geomorphology. We illustrate such an extension for the case of mountainous glacial valleys. We used the graph database engine Neo4J to implement the domain ontology and to develop a knowledge-based system (a framework) to automatically identify landsystems from spatial datasets. We present the architecture of our framework and discuss how it is used to support: 1) the knowledge acquisition tasks; 2) the spatial data preparation task; 3) the processing of the user’s request seeking landsystems in a chosen study area.