Passionate about programming, especially in the fields of geospatial artificial intelligence and web app development.
Experience in coding, debugging and unit testing.Highly motivated researcher and quick learner.
Strong problem solving skills.Good at time management.Desire to learn and develop new programming skills.
Geometric calibration of spaceborne linear array CCDs, developing sensor model and algorithms
R&D Team Member
I am responsible for data quality control and visualization of the data on the web map and developing new ideas for the future of GeoCitSci.
Project Team Member
I worked for solving web development, 3D city modelling and 3D city visualization related problems.
Long Term Intern
3D City Modelling, 3D GIS
Producing Topographic Photogrammetric Maps, Orthophoto Generation, LiDAR Processing
LANDSLIDE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST MODEL FOR ORDU, TURKEY
Gizem Karakaş, Recep Can, Hakan A. Nefeslioğlu, Sultan Kocaman Gökçeoğlu, Candan Gökçeoğlu
Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.
DEVELOPMENT OF A CITSCI AND ARTIFICIAL INTELLIGENCE SUPPORTED GIS PLATFORM FOR LANDSLIDE DATA COLLECTION
Recep Can, Sultan Kocaman Gökçeoğlu, Candan Gökçeoğlu
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.
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 Gökçeoğlu, Candan Gökçeoğlu
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.
Creating web map that shows photographs obtained from LAMA mobile app with their correct position. Working for to automate controlling data quality using deep learning and creating web GIS application.
Geocitsci Project Page
Gaziantep Bizim Şehir
Transforming models and data that comes from architectural team to georeferenced 3D city models, visualization of that models in CityGRID Scout, technical support for software related problems.Bizim Şehir Gaziantep Web Sayfası