A novel approach for landform classification based on salience detection integrating expert knowledge and deep learning

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For several years, advances in data acquisition in geomatics have made it possible to acquire very high-resolution data over very large areas. In particular, we now have digital terrain models at unequaled levels of detail for the analysis of relief and the environment. However, this requires robust and automatic processing methods. Among other issues, the detection and analysis of landforms remains a difficult problem. A landform is the result of geomorphological processes in a specific context. Its definition is inherently vague and current methods are therefore specific to certain types of data and certain landforms. Lately, progress has been made thanks to deep learning methods coming from image processing, but they are difficult to generalize. We therefore propose a new approach based on salient elements of the terrain: a landform is not characterised by morphometric criteria but by saliences, following the cognitive approach performed by geomorphologists. These saliences are included in a network (graph) of points (peaks, pits) and lines (thalwegs, ridges) which describes the terrain. The objective of this research program is to propose new methods for landform detection based on deep learning techniques on graphs. We consider that this approach is more robust since it is less dependent on the terrain model resolution and that it makes it possible to more easily integrate the descriptions made by the experts. A limit to the learning methods is to have data where saliences have already been labelled. Such data are rarely available and their construction is tedious. Three objectives are therefore proposed for this program: 1- Provide a methodology to calculate the required salience properties automatically from expert knowledge and the database and integrates them into a deep neural network. 2- Design an application for automatic classification of saliences from a graph in a semi-supervised way. Only part of the data is labelled and the system must derive classification rules from it. 3- Design an application for automatic classification of saliences from a graph in an unsupervised way. Data are not labelled and the detection depends entirely on the quality of the definition provided by experts. Several case studies on various forms (moraines, glacial valleys, landslide zones) will be used to validate the methods. This project will train eight highly qualified persons. It will provide a new approach to landform analysis that can be used by geomorphologists and environmental experts and will allow them to analyse large datasets for a better understanding of environmental phenomena.