Author: Brittney Kim O'Neill

Posted on

Long Distance Network Facilitation Seed Grant Awarded to Prof. Laefer

The Connecting Women Faculty in Geotechnical Engineering project, a partnership between Syracuse University, Drexel University, and the University of Michigan, has awarded Prof. Debra Laefer a seed grant for long distance network facilitation amongst women in geotechnical engineering. The grant will enable Prof. Laefer to plan and build an on-line database for the engineering community and the general public to identify women with expertise in geotechnical engineering. Similar to “Women Also Know Stuff”, in the political science community, this new tool will help everyone from conference planners to contractors to identify highly skilled women to fulfill their needs for geotechnical expertise.

Prof. Laefer is delighted to collaborate with the Deep Foundations Institute’s Mary Ellen Bruce Large on this exciting project, combining their many years of involvement in the geotechnical community to support and elevate the many talented women working in geotechnical engineering today.

Posted on

Capstone Group Ties for Top Honors

CUSP Urban Science Intensive Capstone group, Piercing the Landlord Veil, co-supervised by Prof. Laefer and Prof. Vo, tied for top honors at their final project presentation. The project focussed on developing a system to help representatives of the New York State Attorney General to better understand the landscape of rental property ownership in New York and to combat harmful landlord practices. After months of hard work, we are delighted to see all their efforts pay off.

//platform.twitter.com/widgets.js

Posted on

City-Scale Modeling at the Alan Turing Institute

Prof. Debra Laefer presented a workshop in London today by invitation from the Alan Turing Institute. She brought over a decade of knowledge in urban modeling to share with the Institute’s innovative data science researchers. We hope that this will inspire increasing research in city-scale urban modeling and seed exciting new cross-Atlantic collaborations.

Posted on

Prof. Laefer Introduces ieSoSc Students to LiDAR Point Clouds

As a part of NYU Tandon’s Innovation, Entrepreneurship and the Science of Smart Cities (ieSoSC) program for K-12 students, Prof. Debra Laefer introduced this summer’s cohort to the wide world of aerial laser scanning. In addition to receiving an introduction to laser scanning, the students also had the opportunity to work with real world laser scanning point clouds collected in Dublin, Ireland. Students also learned how to create video fly-throughs of the data using the open source software CloudCompare.

Prof. Laefer was delighted to have the opportunity to share her research with the next generation of emerging urban scientists and looks forward to continued collaboration with the ieSoSC program.

Posted on

Public Release of the World’s Densest Urban Laser Scanning Dataset

We are delighted to announce that our 2015 aerial laser scanning data are now freely and publicly available through NYU’s Spatial Data Repository.

From the press release:

“At over 300 points per square meter, this is more than 30 times denser than typical LiDAR data and is an order of magnitude denser than any other aerial LiDAR dataset. The dataset also includes the first ever urban scan with the fullwave form version of the data, as well as affiliated imagery and video. The unprecedented comprehensiveness of this multi-layered dataset enables new opportunities in exploration and modeling. It also sets a new standard for what can be collected and used by cities around the world”

To read more, check out the full release on the Center for Urban Science + Progress website.

Posted on

OGC-TC: Combining 2D And 3D Indexing for Efficient Airborne LiDAR Data Management

Urban Modeling Group Assistant Research Scientist, Dr. Anh Vu Vo presented a talk entitled “Combining 2D And 3D Indexing for Efficient Airborne LiDAR Data Management” at the Open Geospatial Consortium (OGC) Technical Committee Meeting in St. Johns, Newfoundland. The OGC is a key player in the creation of standards for geospatial data across the globe and the Urban Modeling Group is delighted to have the opportunity to help shape those standards.

Abstract

This presentation introduced a hybrid spatial index solution for efficient management of large aerial, point cloud data. The proposed approach uses multiple data indexing layers:  a top layer with a two-dimensional, Hilbert-coded, rectangular grid and a bottom layer with multiple, separate, in-memory, three-dimensional octrees. This approach is designed to push more of the workload into the main memory, thereby reducing disk scanning and improving the query resolving speed. Scalability is addressed by using the lightweight Hilbert-coded grid to rapidly localize the searching domain to only a few blocks, which can then be processed independently, irrespective of the dataset’s overall size. When tested on datasets from 90 million to 1.15 billion points, the new hybrid index was 1.7 to 9.1 times faster compared to traditional indexing. Additionally, the proposed approach offered in-base functionality not previously available in the form of incremental nearest neighbor searching and planar surface selection.

Posted on

Kavli Futures Symposium: Sensing the City

The prestigious Kavli Foundation is sponsoring a CUSP symposium led by Prof. Debra Laefer. The symposium “Kavli Futures Symposium: Sensing the City” will convene leaders across scientific disciplines who are exploring the development and use of sensors and the integration of sensor networks throughout cities. The symposium aims to explore best practices in creating, storing, indexing, and exploiting sensor network data.

Posted on

Moore-Sloan Seed Grant: “Development of a Sample-based Modulated Wasserstein Distance for Heterogeneous Datasets of Unknown Distributions for Small Feature Change Detection”

Prof. Debra Laefer and Prof. Esteban Tabak from NYU’s Courant Institute were awarded seed funding from the Moore-Sloan Foundation to pursue “Development of a Sample-based Modulated Wasserstein Distance for Heterogeneous Datasets of Unknown Distributions for Small Feature Change Detection”. The grant supports a graduate student to work with the two professors on the development of this research project. To learn more about this work, please see the project page here.