Urban LiDAR and Remote Sensing

Developing a high performance spatial data management tool to efficiently store, index, integrate, and query point cloud datasets

Black and white image the aerial view of a city

Research

The team will research solutions to address the uniquely 3D challenges inherent in converting massive LiDAR point clouds into actionable insights for improving urban life. These challenges include spatial data indexing, parallelization of 3D data storage and querying, automatic change detection, multi-modal data integration, deconvolution of full waveform data, and the development of effective user interfaces to support the use of this exciting new data.

Goals

Driven by ground-breaking, high density urban LiDAR datasets, this project-based course will form research teams that will work together to develop a high performance spatial data management tool which will allow data users to efficiently store, index, integrate, and query point cloud datasets that are both high resolution and of a massive scale.

This VIP course will coordinate with the NYU Tandon School of Engineering and CUSP.

Methods & Technology

  • Big data
  • LiDAR
  • Hyperspectral imaging
  • Civil, urban, environmental, transportation engineering
  • Spatio-temporal database design and indexing
  • Parallel computing
  • Data integration

Subteams

  • Full Waveform
  • Change Detection
  • Databases
  • Parallelization
  • Data Integration
  • GUI

Areas of Interest

  • Civil Engineering
  • Applied Urban Science and Informatics
  • Electrical and Computer Engineering
  • Statistics and Applied Math
  • Computer Science

Partners

  • Civil Engineering
  • Applied Urban Science and Informatics
  • Electrical and Computer Engineering
  • Statistics and Applied Math
  • Computer Science

Faculty Advisor

  • Debra Laefer
  • Email: debra.laefer@nyu.edu