Next Article in Journal
HBIM Modeling from the Surface Mesh and Its Extended Capability of Knowledge Representation
Next Article in Special Issue
On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection
Previous Article in Journal
Collision Detection for UAVs Based on GeoSOT-3D Grids
Open AccessArticle

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

1
Hacettepe University, Department of Geomatics Engineering, 06800 Beytepe, Ankara, Turkey
2
Hacettepe University, Department of Geological Engineering, 06800 Beytepe, Ankara, Turkey
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(7), 300; https://doi.org/10.3390/ijgi8070300
Received: 1 June 2019 / Revised: 11 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
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. View Full-Text
Keywords: landslide; convolutional neural network; CitSci; VGI; data quality landslide; convolutional neural network; CitSci; VGI; data quality
Show Figures

Figure 1

MDPI and ACS Style

Can, R.; Kocaman, S.; Gokceoglu, C. A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality. ISPRS Int. J. Geo-Inf. 2019, 8, 300. https://doi.org/10.3390/ijgi8070300

AMA Style

Can R, Kocaman S, Gokceoglu C. A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality. ISPRS International Journal of Geo-Information. 2019; 8(7):300. https://doi.org/10.3390/ijgi8070300

Chicago/Turabian Style

Can, Recep; Kocaman, Sultan; Gokceoglu, Candan. 2019. "A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality" ISPRS Int. J. Geo-Inf. 8, no. 7: 300. https://doi.org/10.3390/ijgi8070300

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop