Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 69-73, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-69-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 69-73, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-69-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

ON THE USE OF CITSCI AND VGI IN NATURAL HAZARD ASSESSMENT

S. Kocaman1 and C. Gokceoglu2 S. Kocaman and C. Gokceoglu
  • 1Hacettepe Uni., Dept. of Geomatics Eng. 06800 Beytepe Ankara, Turkey
  • 2Hacettepe Uni., Dept. of Geological Engineering 06800 Beytepe Ankara, Turkey

Keywords: Geosciences, Citizen Science, CitSci, Volunteer Geographical Information, VGI

Abstract. The developments in the geospatially-enabled mobile communication technologies have opened new horizons in many fields of geosciences research, especially in those where data collection, processing and interpretation are time consuming and costly. Being one of these research fields, natural hazards also require high spatiotemporal data density and distribution, which is extremely difficult to obtain and also equally essential to secure the main assumptions of these researches and thus yield to proper conclusions. These problems can be solved with the help of citizen science (CitSci) methods and the volunteer geographical information (VGI). These two terms are complementary, or intertwined, and mutually benefit from each other for achieving their goals. This paper investigates the developments in CitSci and VGI with a specific focus of natural hazard researches and gives a brief overview of the literature. The importance of their use in natural hazards, open research areas and future aspects are also analysed. Based on the previous experiences and analyses, the authors foresee that such investigations would help researchers to utilize CitSci and VGI in their studies, and thus benefit the advantages of both approaches and improve the quality of their data. On the other hand, the growing interest of citizen scientists for supporting scientific processes could be steered to the fields where most help is needed. Specifically, detection of ground deformations after earthquakes is explained here and a simple mobile app developed for landslide data collection is briefly depicted as use case.