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Creating a Catalog of Point Clouds for Public Buildings in Enfield, Connecticut

Enfield Fire Dept No 1 Logo

Enfield Fire Department

This project involves collecting and cataloguing indoor point cloud and image data for a number of buildings in Enfield and Storrs, Connecticut. Enfield has a population of approximately 45,000 people and contains a large variety of buildings that are well-suited to demonstrating the value of indoor 3D data and would allow for future research on indoor mapping, localization, and navigation. These buildings include schools, stores, warehouses, and other commercial and industrial buildings of various ages, shapes, and sizes. Buildings on the University of Connecticut Storrs campus have great variety in architecture and would supplement the buildings surveyed in Enfield and add to the overall variety of buildings included in the project.

Data Available Upon Request

Contact jason_parent [at] (Jason Parent) (Award Principal Investigator) to request the data

Project Overview

The proposed project would collect point clouds for 12 buildings representing schools, industrial buildings, and administrative buildings as well as university buildings (Figure 1). The point clouds would include the building interior as well as the outside area in the immediate vicinity of the building. The targeted buildings are owned by either the Enfield town government or the University of Connecticut which will help ensure that the occupancy of these buildings does not change in the foreseeable future. The stable occupancy will make it more likely that these public buildings will remain bound by current letters of commitment and so be available to future researchers. Privately-owned commercial and industrial buildings will be avoided because their ownership can change unexpectedly and the occupants of these buildings might be less receptive to the data sharing or future building access required by the program.

The team for the proposed project is composed of members of the Enfield Fire Department (EFD) as well as researchers from the University of Connecticut (UCONN). The EFD personnel have knowledge of the features that are of interest to public safety entities and will guide the UCONN researchers in classifying the point clouds and creating annotated floor plans. The UCONN researchers have a working knowledge of LiDAR and imaging systems including experience in LiDAR field data collection and LiDAR point cloud processing. They have led or participated in a number of research projects that have centered on mapping features with LiDAR and image datasets. One UCONN researcher is a geodesist with expertise and experience in survey-grade mapping and will ensure the data produced are of high quality and positional accuracy.

Department of Natural Resources and the Environment (NRE) at the University of Connecticut (NRE) department professors Jason Parent, Chandi Witharana, and Thomas Meyer would provide the following services and products in conjunction with the proposed project:

  • Technical assistance with field data collection
  • Processing of survey data
  • Annotation of point cloud data
  • Development of annotated floor plans
  • Long-term archiving of all project data

<< Back to the PSIAP - Point Cloud City Page 

* As a service to the research community, NIST provides the links to the datasets created by financial assistance recipients that received awards under the NIST Public Safety Innovation Accelerator Program – Point Cloud City Notice of Funding Opportunity.  NIST had no role in the creation of the datasets other than to fund these awards.  NIST makes these datasets available “AS-IS” and provides no warranties, express or implied, of merchantability or fitness for a particular purpose.  NIST makes no representations that the use of the datasets will not infringe any patent or proprietary rights of third parties.  Providing a link to these datasets does not constitute a NIST endorsement of the third-party institutions involved.  NIST makes no representations about the quality, reliability, or any other characteristic of the datasets.

Created October 1, 2018, Updated February 3, 2023