Comparison of LIDAR Derived Data to Traditional Photogrammetric Mapping David Veneziano Dr. Reginald...

Comparison of LIDAR Derived Data to Traditional Photogrammetric Mapping

David Veneziano

Dr. Reginald Souleyrette

Dr. Shauna Hallmark

GIS-T 2002

April 19, 2023

USDOT Remote Sensing Initiative

• NCRST-InfrastructureUniversity of California - Santa Barbara (lead), University of Wisconsin, University of Florida, Iowa State University

• Sponsored by– USDOT– RSPA

• NASA

• Joint endeavor with Iowa DOT

Problem

• Highway location/relocation studies require surface terrain information

– economically site new or relocate existing infrastructure facilities

– make final design plans

• Current data collection methods for surface terrain modeling

– Electronic Distance Measurement (Total Station)

– Real Time Kinematic Global Positioning Systems

– Photogrammetry (aerial photography)

Limitations of Current Data Collection Methods

• Labor Intensive

• Time-consuming

• Costly

• Dictated by conditions (time of year, sun angle, weather, etc.)

• May require data collectors to locate in-field

Solution

• Evaluate use of LIDAR (Light Detection and Ranging) as alternative to current data collection methods– Feasibility– Accuracy– Application– Costs and benefits

LIDAR: The Technology

• Emits stream of light pulses and records return time and angle of emissions

• GPS -- positional information

• Inertial measuring systems measure roll, pitch, and yaw

LIDAR: The Technology• Corrects distance measurement

for each pulse

• Calculates corrected surface coordinates (x, y, z)

• Data processing can extract measurements of the bare ground (removal of vegetation, snow cover, etc.)

• Digital aerial photography can be taken at the same providing an additional layer of data.

Advantages of LIDAR

• Ease of data acquisition

• Increased ability to determine surface elevations in difficult areas

• Significantly less time for creation of elevation data

• Less dependent of weather, time of year, time of day

Pilot Study

• Iowa 1 Corridor

• Already mapped using photogrammetry (1999)

• Project Scope– Compare accuracy of LIDAR

vs. photogrammetry

– Evaluate use in highway location studies

– Compare costs and benefits of LIDAR vs. conventional survey techniques

Data Collected

• LIDAR XYZ ASCII – First Return

– Last Return

– Bare Earth

• 1 Foot resolution digital orthophotos

Data Issues• Incomplete filtering of

structures and trees• Row crops still present

Accuracy Comparison Techniques• DTM Comparison – Compare elevations extracted from

digital terrain models developed from LIDAR to reference data to determine elevational differences between the two datasets

• Point Interpolation - Bilinearly interpolate LIDAR points to reference points to obtain elevational differences (only performed on points lying on flat surfaces)

• Nearest Neighbor Comparison – Select LIDAR points within a specified tolerance (both x and y) of the reference points to calculate elevational differences between the two datasets

• Nearest Neighbor Comparisons– Photogrammetry is baseline

– LIDAR vs. Photogrammetry

– Photogrammetry and LIDAR vs. GPS control (future)

“Pure” Accuracy Comparison

Technique• Join Photogrammetry point table to LIDAR point table• Resulting table produces horizontal distance field between LIDAR and

Photogrammetry points • Query out points within desired distance (ex. 15 cm)

Test of Vertical Accuracy

• The National Standard for Spatial Data Accuracy states that twenty or more test points are required to conduct a statistically significant evaluation

• A Root Mean Square Error Test can be performed on 20+ common x, y values between the reference and LIDAR datasets to determine the vertical accuracy

Calculations

Accuracy Results

LIDAR vs. PhotogrammetryNumber of

PointsMean Z

Difference RMSE NSSDAHard Surfaces 30 0.066 0.257 0.503Ditches 31 0.222 0.471 0.923Harvested Fields 32 0.046 0.214 0.420Unharvested Field (Pre Filter) 41 2.445 1.564 3.065Unharvested Field (Post Filter) 41 0.157 0.397 0.777Wooded Areas 21 0.159 0.398 0.781

Highway Location Application• Compare area calculations of different

alignments produced from photogrammetry and LIDAR data– Does LIDAR produce more accurate

calculations of the area above and below a baseline (photogrammetry)?

– If so, LIDAR may produce cost savings as designers would have information regarding where more or less earthwork would be required, allowing them to plan the most efficient route

– Area calculations produced through GIS analysis could yield cost savings by guiding designers in finding optimal routes

Profiles of alternative alignments

Profile Generation

Preliminary Profile DifferencesRoute 2 Profile

220

225

230

235

240

245

250

255

0 5000 10000 15000 20000 25000

Distance (Meters)

Ele

vati

on

(M

eter

s)

LIDAR

Photogrammetry

Preliminary Results: 3 Alignments

• Alignments approx 19,000+ meters long (11 miles)• Minimum elevation difference – 5cm• Maximum elevation difference – 2+m  

AlignmentLength

LIDAR Above

LIDAR Below

Cumilative Difference

Average Differnece

Route 1 19933.32 1861.39 3471.50 5332.89 0.27Route 2 19588.78 1860.22 5253.76 7113.98 0.36Route 3 19416.51 2178.42 5300.99 7479.41 0.39

LIDAR Above, Below and Cumulative Diff are in squared meters

Future Work: Compare Costs and Benefits

• Accurate LIDAR would be capable of supplementing photogrammetry

• If LIDAR collection costs are comparable or lower than traditional methods, such data collection would be more cost effective

Compare Costs and Benefits

• Cost/Benefit analysis will determine if LIDAR can financially compare to photogrammetry– Compare costs of data collection methods– Examine data delivery times and the cost

savings derived from more rapid delivery

Conclusions

• LIDAR is still an emerging and developing technology compared to photogrammetry

• Usefulness of LIDAR limited primarily to leaf-off collection for highway design needs

• Additional photogrammetric data collection will still be needed to meet final design needs

Conclusions

• Accuracy of LIDAR is better on hard, flat, bare surfaces as opposed to surfaces covered by vegetation

• Areas with steep changes in elevation (ex. ditches) may be less accurately represented

• LIDAR accuracy in areas with heavy vegetation coverage (ex. row crops) is poor

Conclusions

• Elevational profiles between LIDAR and Photogrammetry surfaces roughly match

• Area estimations show that LIDAR alignments are, on average, 30-40 cm above or below those of photogrammetry for the length of each alignment– Area estimations may be influenced by

vegetation, etc.

Questions?