A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

Recep Can,Sultan Kocaman,Candan Gokceoglu

doi:10.3390/ijgi8070300

Abstract

Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers.

Highlights

IntroductionWith the recent developments in mobile and geospatial technologies, it has become possible to incorporate human efforts in several scientific processes
With the recent developments in mobile and geospatial technologies, it has become possible to incorporate human efforts in several scientific processes. This approach has brought the term Citizen Science (CitSci) into the sight of researchers and is especially useful for geoscience studies, where every human can act as a powerful sensor and interpreter
The growing public demand in CitSci [5] can be confirmed by the establishment of several regional and international citizen science associations (e.g., CSA [4], ECSA [6], etc.) in recent years

Summary

Introduction

With the recent developments in mobile and geospatial technologies, it has become possible to incorporate human efforts in several scientific processes This approach has brought the term Citizen Science (CitSci) into the sight of researchers and is especially useful for geoscience studies, where every human can act as a powerful sensor and interpreter. The trends in open data and open science mentalities strongly support these processes These changes are fostering rapid scientific development, but are forming science-oriented societies. According to Haklay [9], CitSci stands out as a class of VGI activities that require special attention and analysis It has been seen in the literature that the terms “CitSci” and “VGI” are used alternatively for many geographical data-related projects, and they benefit from each other [10]. The development of data quality assessment and validation strategies both for VGI and CitSci data has been an emerging research topic and is crucial, as emphasized by several studies [11,12,13,14]

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