The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B5-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 43–50, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-43-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 43–50, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-43-2020

  24 Aug 2020

24 Aug 2020

DEVELOPMENT OF A CITSCI AND ARTIFICIAL INTELLIGENCE SUPPORTED GIS PLATFORM FOR LANDSLIDE DATA COLLECTION

R. Can1, S. Kocaman1, and C. Gokceoglu2 R. Can et al.
  • 1Hacettepe University, Dept. of Geomatics Engineering, 06800 Beytepe Ankara, Turkey
  • 2Hacettepe University, Dept. of Geological Engineering, 06800 Beytepe Ankara, Turkey

Keywords: WebGIS, Citizen Science, Deep Learning, Landslide, Data Quality, CNN

Abstract. Geospatial data are fundamental to understand the relationship between the geographical events and the Earth dynamics. Although the geospatial technologies aid geodata collection, the increasing possibilities yield new application areas and cause even a greater demand. Considering the increment in data quantity and diversity, to be able to work with the data, they must be collected, stored, analysed and presented with the help of specifically designed platforms. Geographical Information Systems (GIS) with mobile and web support are the most suitable platforms for these purposes. On the other hand, the location-enabled mobile, web and geospatial technologies empowered the rise of the citizen science (CitSci) projects. With the CitSci, mobile GIS platforms enable the data to be collected from almost any location. As the size of the collected data increases, considering automatic control of the data quality has become a necessity. Integrating artificial intelligence (AI) with the CitSci based GIS designs allows automatic quality control of the data and helps eliminating data validation problem in CitSci. For this reason, the purpose of the present study is to develop a CitSci and AI supported GIS platform for landslide data collection because landslide hazard mitigation efforts require landslide susceptibility, hazard and risk assessments. Especially, landslide hazard assessments are necessary the time of occurrence of a landslide. Although this information is crucial, it is almost impossible to collect time of occurrence in regional hazard assessment efforts. Consequently, use of CitSci for this purpose may provide valuable information for landslide hazard assessments.