Map901: Building Rich Interior 

Hazard Maps for First Responders

Lan Wang (PI), Eddie Jacobs (Co-PI)

University of Memphis

In collaboration with City of Memphis

PSCR, 7/9/2019
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DISCLAIMER

This presentation was produced by guest speaker(s) 

and presented at the National Institute of Standards 

and Technology’s 2019 Public Safety Broadband 

Stakeholder Meeting. The contents of this 

presentation do not necessarily reflect the views or 

policies of the National Institute of Standards and 

Technology or the U.S. Government.

Posted with permission
Map901

• Collaboration between City of Memphis and U. Memphis

• Survey 7 facilities, 172,800 square meter

• 3D point cloud, 360-degree camera images, other sensor data

• Annotate data with objects of interest to public safety agencies 

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Survey Equipment

• By mounting the camera to the LiBackpack, we collect both 

LiDAR and 360-degree image data sets simultaneously to 

assign RGB info to LiDAR data later.

• GPS locations of external points are used to geo-reference 3D 

LiDAR point cloud data.

• Temperature, humidity and sound information are collected 

separately by two other sensors. 

Velodyne

LiDAR 

sensor

GVI LiBackpack 50

Insta360 Pro 

2 camera

Reach RS+ GPS

(RTK GNSS Receiver)

Digi-Sense 20250-29 

Sound Meter

Digi-Sense Temperature 

/RH Recorder

Integrated System

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Data Format

• ASPR LAS format 7 allows integration of color and 

user-defined classes to facilitate labeling of objects.

Data Format

point cloud ASPR LAS 1.4-R13 with point data format 7 

360 degree video 4K 30fps encoded in H.264 stored in MP4

GPS data NMEA format

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Best Practices in LiDAR Survey

• Open all the doors before the survey

• Avoid capturing moving objects during the survey

• Avoid exiting and entering interior spaces through the same 

threshold when the doorway is narrow

• Do not repeat a route already traveled

• Scan one or two floors at a time and stitch the data together

Videos of a student surveying the Pink Palace 6
• Challenge: difficult to survey small rooms

• Lidar vertical field of view only 30 degrees

• Captures top part of small rooms; cannot see floor

• Wearer does not have room to move and capture more detailed 

result

• Developed a custom solution Signac for surveying small 

rooms.

• Dual cameras provide pose (top) and depth (bottom)

• Intel® RealSenseTM T265

• Intel® RealSenseTM D435

• Raw camera data recorded to attached laptop and 

processed into PLY result

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Signac Result

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Data Processing Workflow

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LiDAR 

data

Annotated 

Images

SLAM 

Algorithm

Images

Machine Learning 

Annotation 

Algorithm

Annotated 3D point cloud 

with RGP information
Image Annotation

• Goal: annotate image data with public safety objects, e.g., fire

extinguishers, building control panels, utility shutoff, exits (49 labels)

• Use Mask R-CNN [1] and Inception-ResNet-v2 [2] to label objects

• Mask R-CNN provides pixel level segmentation with bounding boxes.

• Inception-ResNet-v2 classifies objects and refines bounding boxes.

• Store bounding boxes and class labels in a JSON format (for

annotating the corresponding point cloud)

Equirectangular Images Mask R-CNN Annotated images

[1] K. He, G. Gkioxari, P. Dollr, R. Girshick, Mask R-CNN. ICCV 2017

[2] Inception-ResNet-v2, https://github.com/tensorflow/models/tree/master/research/object-detection

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Fusing Lidar and 360 Degree Images

Memphis Central Library Hickory Hill Community Center

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• Project Lidar points before SLAM onto equirectangular image

• align lidar and video time and angle, then the points are colored 

automatically

• Use SLAM on colored points to create final result
Data Sharing and Facility Access

• Data sharing

• Provided preliminary data to OGC pilot members

• Use existing ArcGIS platform for first responders in 

the city and county to access the data

• Public data platform (data.memphistn.gov)

• Future facility access

• two years beyond the end date of the grant for 

testing and evaluating alternative technologies

• 2 facilities: Memphis Central Library and FedEx 

Institute of Technology

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Functionality

 Navigation

 Pan/Zoom/Rotate

 Bookmarked Views

 Floor by Floor Display

 LIDAR symbology

 Color

 Elevation

 Intensity

 Change Underlying basemap

 Measure Distance Hazards

Platform

ESRI ArcGIS Online

 Web Based GIS Platform

 Able to host large amounts of 

data

 Simple Easy to use interface

Map901 GIS Web App
Team and Collaboration

• City: Mike Rodriguez, Wendy Harris, Gertrude Moeller, 

Joseph Roberts

• U Memphis: Lan Wang, Eddie Jacobs, Junaid Khan, 

Mazhar Hossain, Tianxing Ma, Thomas Watson, 

Brandon Simmers, Meg Homeyer, Abigail Jacobs

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2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City

Hancock County Mississippi Emergency Management Agency

– Partnered locally with NVision Solutions Inc. 

1. Hancock High School – 309,347 sq. ft.

2. Bay High School – 142,762 sq. ft.

3. Hancock Middle School – 139,662 sq. ft.

4. Bay Middle School – 104,630 sq. ft.

5. North Hancock Elementary – 89,541 sq. ft.

6. East Hancock Elementary – 91,640 sq. ft.

7. South Hancock Elementary – 92, 560 sq. ft.

8. West Hancock Elementary – 90,039 sq. ft.

9. North Bay Elementary – 64,415 sq. ft.

10. Waveland Elementary – 76,486 sq. ft.

Location: 10 public schools comprising 1,201,082 sq ft of indoor space. 

Includes USDA Designated Rural Areas and SBA HubZones. Mix of older high 

schools and newer elementary schools rebuilt after Hurricane Katrina to 

similar specs.

Project: Create indoor maps of public buildings using LIDAR
2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City

Public Safety Applications: EMA coordinates with fire and law 

enforcement for any significant crisis and takes the lead for 

natural disasters.
2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City

Key Deliverables

• 10 attributed, lidar data sets for 10 public schools

• Vector data set for relevant public safety indoor features

• Mississippi Automated Resource 

Information System (MARIS) data 

download page

• Any open-source software tools developed

“Reach” Goals

• Automated feature recognition and annotation

• Immersive VR Headset display

• Lightweight, open-source classification software

Potential Impacts

• New and improved approaches in public safety tactics

• Common standards and best practices for indoor maps

• Increased public/private research into indoor mapping due to 

high-quality, widely available reference datasets

• Increase in derivative technology research

• Open-source tools to make processing indoor data and 

creating maps easier

• Recognition of US as a global leader in Indoor Mapping 

technology
2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City

Zeb-Revo RT
2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City

Manual Lidar Annotation

LATTE
2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City

Automated, specific feature 

identification and classification
2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City

Scalable Lidar Classification??
2018 NIST Public Safety Innovation Accelerator Program - Point Cloud City
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Indoor Mapping with Handheld 

LiDAR in Enfield and Storrs, CT

Paul Januszewski

Matt Mucci

Michael Carifa

Jason Parent 

James Hurd

Tom Meyer

Enfield Fire
The need for indoor navigation systems

• Disorientation is a major hazard for first responders.

• Thermal cameras are only part of the solution.

• Indoor navigation systems are the missing part of the solution.

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What Hollywood 

firefighters see

What real 

firefighters see

What a thermal 

camera sees

https://www.dhs.gov/publication/precision-outdoor-andindoor-navigation-and-tracking-emergency-responders-pointer

Indoor tracking and 

navigation (POINTER)

What we would 

like to see!
Mapping for indoor navigation

• Indoor navigation requires accurate maps.

• Light detection and ranging (LiDAR) can be used to map 

buildings “as built” accurately and efficiently.

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Project objectives

• Create database of point clouds 

annotated with items-of-interest.

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• Create floor plans showing 

items-of-interest.
Items

-of-interest

• Exits, windows, fire alarm and 

suppression system 

components, utility shut-offs, 

etc.

• Useful for pre-plans, incident 

management, and building 

inspections.

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Project buildings

• 12 buildings in Connecticut.

• 1.4 million square feet.

• Schools, administrative, industrial, academic.

• Built or renovated 1960 to 2016.

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LiDAR scanner – Paracosm’s PX-80

• 300 pts/sec; range of 80m.

• Simultaneous RGB image collection 

with integrated spherical camera.

• Scan durations up to 30 minutes.

• Data collection by EFD personnel.

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PX-80 data outputs

Colorized point cloud Spherical RGB (true color) 

imagery
Point cloud processing

• Python scripts divide point clouds into subsets:

• Smaller size improves graphics rendering for manual classification.

• Separate ceiling from walls for easier classification.

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Point cloud classification - auto

• Scripts created to classify small hard-to-see features.

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Point cloud classification - manual

• Manual classification with 

Lasview (LAS Tools) 

• Classify remaining items.

• Quality check.

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• Reference imagery viewed in 

Paracosm’s Retrace

• Helps identify items in point 

cloud.
Geo-referencing point clouds 

• Scans stitch together and 

georeferenced using Cloud 

Compare.

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• Aerial imagery provides 

ground control points for 

georeferencing.
From point clouds to maps

• Floor plans digitized from 

georeferenced point clouds using 

ArcGIS.

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• Scripts will be created to 

automatically map locations of 

items-of-interest.
Conclusions and lessons learned

• Certain hard-to-see features 

were reliably classified by scripts 

using feature sizes and shapes.

• Lasview was low-cost and easy to 

use for manual classification.

• Developing scripts to subset and 

help classify point clouds greatly 

improved efficiency.

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• PX-80 scanner can be used 

effectively with little training 

or technical proficiency.

• Colors in the point cloud tend 

to be distorted. 

• RGB images (viewed in 

Retrace) helped to locate 

features in the point cloud.
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Indoor Mapping with Handheld 

LiDAR in Enfield and Storrs, CT

Paul Januszewski

pjanuszewski@enfieldfire.org

Jason Parent 

Jason.parent@uconn.edu

Enfield Fire

Questions?
Come back for the 

Next 

Session

2:25 PM

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#PSCR2019