Automatic Gully Detection: Neural Networks and Computer Vision

Artur M Gafurov,Oleg P Yermolayev

doi:10.3390/rs12111743

Abstract

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

Highlights

Erosion is one of the most active geomorphic processes and one of the major causes of land degradation worldwide
In the eastern part of the Russian Plain, gully erosion is intense, forming what is known as the “erosion polis” [2,3]
This is due to the fact that when such maps were created for the territory of the former USSR and its separate regions, the gully dissection density of the gully network was determined by topographic maps of different scales (1:420,000, 1:100,000, 1:50,000, and 1:25,000) [4,5,6,7,8,9]

Summary

Introduction

Erosion is one of the most active geomorphic processes and one of the major causes of land degradation worldwide. There is still no unified database of gullies in Russia, especially reflecting their current state and identified by common methodological rules and mapping sources In many ways this is due to the exceptionally large territory of the country, the variety of natural conditions, and the difficulty of making continuous instrumental observations in the field due to their high cost. The solution could be to map gullies in office conditions (without fieldwork) based on remote sensing data, but such work is very time-consuming for a large territory. This approach raises a number of methodologically unsolved issues.

Objectives

Methods

Findings

Discussion

Conclusion