Learning to Find Hydrological Corrections

TL;DR

This paper introduces a machine learning approach using convolutional neural networks to automatically identify hydrological corrections in large digital elevation models, significantly reducing manual effort and improving correction quality.

Contribution

The paper presents a novel CNN-based algorithm for automatic detection of hydrological corrections in high-resolution terrain models, streamlining the correction process.

Findings

Model detects most known hydrological corrections in Danish terrain data.

The approach identifies additional corrections not previously included.

Automation reduces manual effort and improves correction consistency.

Abstract

High resolution Digital Elevation models, such as the (Big) grid terrain model of Denmark with more than 200 billion measurements, is a basic requirement for water flow modelling and flood risk analysis. However, a large number of modifications often need to be made to even very accurate terrain models, such as the Danish model, before they can be used in realistic flow modeling. These modifications include removal of bridges, which otherwise will act as dams in flow modeling, and inclusion of culverts that transport water underneath roads. In fact, the danish model is accompanied by a detailed set of hydrological corrections for the digital elevation model. However, producing these hydrological corrections is a very slow an expensive process, since it is to a large extent done manually and often with local input. This also means that corrections can be of varying quality. In this paper we propose a new algorithmic apporach based on machine learning and convolutional neural networks for automatically detecting hydrological corrections for such large terrain data. Our model is able to detect most hydrological corrections known for the danish model and quite a few more that should have been included in the original list.