CREATE4D Newsletter, August 2016 
View this email in your browser

Free Point Clouds 

This online benchmark provides the largest known labelled 3D point cloud data set of natural scenes with over 3 billion points in total.

Download benchmarks >>


Upcoming Events    

1. Object Extraction for 3D City Models
2. JURSE 2017, Call for Papers  
3. IEEE JSTARS Special Issue  
4. IEEE Transactions on Computational Imaging


Guest Researcher: Elena Nikolaeva  

I am a physicist with both a bachelor’s and master’s degree. The research project for my master’s degree involved working with Motorola at Tbilisi State University, in the Republic of Georgia. The goal of the project was to study and simulate the interaction between radio waves and the human head. Later on, my experience with radio waves helped me to understand a work of satellites.

Upon completing my master’s degree, I started work at the Seismic Monitoring Centre, where my research focused on the process of optical satellite imaging and the physical processes of earthquakes. I found the processing and analysis of remote sensing data and the study of earthquakes to be fascinating. Spurred by this interest, I decided to learn more about remote sensing, particularly remote sensing application to the understanding of natural hazards.

I therefore moved to Germany and to pursue my PhD in the Helmholtz Centre Potsdam, German Research Centre for Geoscience (GFZ). My PhD research involved the detection, study, modeling and monitoring of landslides and earthquakes using Synthetic Aperture Radar (SAR). It was a first research where SAR data was applied to the study of landslides and earthquakes in Georgia. After obtaining my PhD, I was employed at the National Observatory of Athens, Greece, I improved my expertise in SAR data by processing amplitude of SAR data.

Now, I am a researcher at the Ilia State University and National Environment Agency in Georgia. I have several scientific projects covering wide topics, such as the identification of wetlands from optical data using classification methods; adaption of the revised universal soil loss equation to Tusheti region and the detection of landslides using SAR (Sentinel-1) data. Here I would like to share my approach to fast estimate a complex landslide volume based on remote sensing data:

Here is one of the key publications of Elena:
"Nikolaeva E., Walter T.R., Shirzaei M., and Zschau J.: Landslide observation and volume estimation in central Georgia based on L-band InSAR, Nat. Hazards Earth Syst. Sci., 14, 675-688, doi: 10.5194/nhess-14- 675-2014, 2014.


   Send an email ( and be our guest


Freshly Published    

"A supervoxel-based spectro-spatial approach for 3D urban point cloud labelling"  >>

My article on LIDAR News >> 

Want to share your publications? Send an email at


Q & A (New page)   

In order to answer to the questions that I commonly receive by emails, I have started a Q&A page.

Please email your suggestions to improve the page for a better service. 


Online Group For Discussing Pattern Recognition, Computer Vision, Machine Learning, AR/VR     

Join to group. Ask questions and support others.


Create Time for Creativity


Check the freshest artwork on Instagram...
               ...also on Facebook



I hope this letter serves you.
Please give me feedback. Thank you very much for reading.
With my best wishes,

Copyright © Beril Sirmacek, All rights reserved.