22 February 2017

Nikolay Bilev and Nikolay Nikiforov joined ESM & ASA

Nikolay Bilev and Nikolay Nikiforov, PhD students at the Moscow State University joined the Ecosystems Services and Management program and the Advanced Systems Analysis program for three months as the winners of best-poster award at the conference on Data Intensive Systems Analysis for Geohazard Studies, which took place in Sochi, Russia in 2016, where IIASA was a co-sponsor. Their research at IIASA focuses on Remote Sensing and Data Analysis for Geoscience Research.

Nikolay Bilev graduated from the Faculty of Economics, Lomonosov Moscow State University in 2015. Currently, he is a PhD student at the same university. In addition, he has two years of experience working in industry as a Data Scientist and Quantitative Researcher. His main fields of expertise lead to advanced data analysis and large scale data engineering. Currently, he is focusing on implementing Machine Learning techniques of data analysis for Geoscience studies. In particular, he uses the Computer Vision approach for object detection and classification in remote sensing images.

Nikolay Nikiforov graduated from the Faculty of Geology, Lomonosov Moscow State University in 2014. Currently, he is a PhD student at the same university. In addition, he has five years of wide ranging experience working in industry as a Geoscientist. His main field of interest is the application of Machine Learning techniques and advanced analysis to geoscience. Currently he is working on applying Supervised Classification for global scale issues, using remote sensing data.

Remote sensing and data analysis for geoscience research

Nowadays remote sensing and high-performance computing provide an opportunity to extract valuable information about landscape and terrain objects. The core element of our research is satellite imagery analysis. The first project is aimed to develop an approach for deforestation monitoring. We analyse how users and experts understand forest reduction using crowdsourcing campaign data and describe this process with remote sensing survey. In our second project the effort is undertaken to examine satellite images in various spectra in order to detect oil palm plantations worldwide. Our approach requires high-performance computations and machine learning techniques. In this case, we use cloud based Geographic Information System (GIS) platform and have a strong ability to power the study with Computer Vision methods of image data analysis. 

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Last edited: 08 March 2017

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