Meet the first-ever Dun & Bradstreet Geospatial Data Science Scholarship recipient
With more than 15 years of experience in Geographic Information Science (GIS), Yaneev Golombek aims to utilize geospatial technologies to render street corridors and their encroaching built environments, also known as streetscapes, into 3D pixels to accurately measure features in their 3D space.
Golombek is the first-ever recipient of the Dun & Bradstreet Geospatial Data Science Scholarship, which, in collaboration with USGIF, provides $15,000 to one graduate or doctorate student focused on solving a data-intensive, large-scale, location-based problem using engineering, computer science, math, and/or spatial science.
“[My career] began in geospatial solutions. I went down the path of utilizing GIS for engineering-based applications for [Merrick & Company]. Geospatial solutions used to solve real-world problems are commonly tied to GEOINT,” Golombek said.
Golombek always had an interest in geography with a focus on software and technology. He received a Bachelor of Science in environmental science from the University of Michigan and a Master’s of Science in GIS from the University of Denver. Thereafter, Golombek went to work for Merrick & Company, a multi-discipline professional engineering, architecture, surveying, and geospatial solutions firm serving different entities in the energy and chemicals, national security, life sciences, and infrastructure markets. Today he is a GIS projects and applications lead, managing GIS-based projects.
In 2014, Golombek enrolled at the University of Colorado to pursue a Ph.D. in civil/geospatial engineering, where he will use the funding from the Dun & Bradstreet scholarship to enhance his research on measuring and quantifying 3D features in streetscapes.
Through his study, Golombek found that methods to accurately measure and conduct location-based research are very limited. Golombek, therefore, is devising a way to quantify 3D models of streetscapes en masse specifically to measure and quantify features within them. Ultimately, his processes are used to generate location-based descriptive statistics.
“My research is focused on the actual 3D measuring of features within a streetscape. As of now, most measuring and spatial analysis is done on a 2D plane. Something you measure in 2D, like a tree or a building, [looks like] a simple polygon on a map. However, incorporating a feature’s 3D characteristics provides an alternative and more objective representation,” Golombek said.
To accomplish his research, Golombek utilizes LiDAR as his primary mapping technology, combined with GIS software applications. Golombek also incorporates publicly available data from the U.S. Geological Survey’s 3D Elevation Program into his research.
“My [research] builds upon how things have been done in the past to enhance how things are done now, moving from subjective methods to objective methods to quantify and map features utilizing LiDAR technology,” Golombek said.
Trajectory spoke with Rendered.ai's CEO about the company's recent partnership with Orbital Insight to contribute synthetic data for a project that is building automated detection technology for the National Geospatial-Intelligence Agency