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

结合专家知识和深度学习的基于显着性检测的地貌分类新方法

• 批准号: RGPIN-2022-03885
• 负责人: Guilbert, Eric
• 金额: $ 1.89万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765451
• 关键词: novel; approach; landform; classification; based

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.

几年来，地理信息学数据采集方面的进展使人们有可能在非常大的区域内采集非常高分辨率的数据。特别是，我们现在有数字地形模型在无与伦比的细节水平的地形和环境的分析。然而，这需要稳健的自动处理方法。除其他问题外，地貌的探测和分析仍然是一个难题。地貌是特定环境下地貌过程的结果。它的定义本质上是模糊的，因此目前的方法是特定于某些类型的数据和某些地形。最近，由于来自图像处理的深度学习方法，已经取得了进展，但它们很难推广。因此，我们提出了一种新的方法的基础上显着的地形要素：地形的特点是不形态标准，但显着性，以下的认知方法进行的地貌学家。这些突出部分被包含在描述地形的点（峰、坑）和线（深线、山脊）的网络（图）中。该研究计划的目标是提出基于图上深度学习技术的地形检测新方法。我们认为，这种方法是更强大的，因为它是较少依赖于地形模型的分辨率，它使得有可能更容易地集成由专家的描述。学习方法的一个限制是具有已经标记了显着性的数据。这样的数据很少，其建设是乏味的。因此，该计划提出了三个目标：1-提供一种方法，从专家知识和数据库中自动计算所需的显着性属性，并将其集成到深度神经网络中。2-设计一个应用程序，以半监督的方式从图中自动分类显著性。只有部分数据被标记，系统必须从中导出分类规则。3-设计一个应用程序，以无监督的方式自动分类图中的显著性。数据没有标记，检测完全取决于专家提供的定义的质量。对各种形式（冰碛、冰川谷、滑坡带）的若干案例研究将用于验证这些方法。该项目将培养8名高素质人才。它将为地貌学家和环境专家提供一种新的地貌分析方法，使他们能够分析大型数据集，更好地了解环境现象。