ANALYSIS OF AIRBORNE LIDAR DATA FOR ROAD INVENTORY CLAY WOODS 4/25/2016 NIRDOSH GAIRE CEE 6190 YI HE ZHAOCAI LIU

OBJECTIVE The main objective of this research is to find an approach to detect and extract highway features, such as guardrails, medians, bridges, and large road signs, from airborne LIDAR data.

INTRODUCTION  ROAD INVENTORY COMPILATION OF COMPONENTS AND CONDITIONS OF ROAD SYSTEM.  METHODOLOGIES FIELD INVENTORY PHOTO/ VIDEO LOG AERIAL/ SATELLITE PHOTOGRAPHY TERRESTRIAL LIDAR MOBILE LIDAR AIRBORNE LIDAR

LIDAR TECHNOLOGYLIDAR TECHNOLOGY  LIDAR LIGHT DETECTION AND RANGING (LIDAR) REMOTE SENSING TECHNOLOGY THAT COLLECTS GEOMETRIC AND GEOGRAPHIC INFORMATION OF TARGETS ON THE EARTH’S SURFACE IN THE FORM OF POINT CLOUDS.  CLASSIFICATION  TERRESTRIAL LASER SCANNING (TLS)  MOBILE LASER SCANNING (MLS)  AIRBORNE LASER SCANNING (ALS)

WHY LIDA R?WHY LIDA R?  HIGH DEGREE OF AUTOMATION  SAFE OPERATION  LESS AFFECTED BY ATMOSPHERE CONDITIONS  EFFICIENT  HIGH POST-PROCESSING EFFICIENCY

FIELD EXPERIMENT AND DATA COLLECTION I-84– Mountain Green to Morgan County/Summit County I-15 north– Payson to Springville I-15 south– Region 2 US-191

AIRBORNE LIDAR DATA PROCESSINGAIRBORNE LIDAR DATA PROCESSING

METHODOLOGY Guardrails Median Barriers Bridge Traffic Sign Rural Urban

Step 1: Find the Region of Interest Transform LAS file to raster file using ‘LAS Point Statistics as Raster’ tool, find the region of interest (road). Step 2: Road Feature Detection Evaluate the z-range each target feature falls into, and delete all the cells that are out of the range. Step 3: Digital Processing Manually digitize the found features in the raster files by creating feature classes. Step 4: Model Builder Automation of the algorithm. METHODOLOGY

STEP 1. FIND THE REGION OF INTERESTSTEP 1. FIND THE REGION OF INTEREST

Original LAS file Raster of road

STEP 2. ROAD FEATURE DETECTIONSTEP 2. ROAD FEATURE DETECTION  Calculate the elevation difference between the points that represent the target features and the points that represent ground from the profile view of the target features guardrails and medians: approximately 0.5 to 1 meter bridge: approximately 7 to 10 meters large traffic signs: approximately 10 to 11 meters  Remove all the cells whose values are out of range and get the candidate regions

Candidate guardrails and medians Candidate bridgeCandidate traffic sign

STEP 3. DIGITAL PROCESSINGSTEP 3. DIGITAL PROCESSING

TASKS ARE REDUNDANT WITH MULTIPLE STEPS SUITED FOR AUTOMATION EXAMPLE IS FOR GUARDRAILS, BUT SIMILAR PROCESS FOR ANY FEATURE MAY NEED TO ADJUST BUFFER SIZE WITH EACH ROAD STEP 4. MODEL BUILDERSTEP 4. MODEL BUILDER

RESULTS AND DISCUSSION RESULTS AND DISCUSSION Bridge Extraction Results and Accuracy Evaluations for I-84

Sign Detection Results and Accuracy Evaluations for I-15

CONCLUSION  Airborne LIDAR technology is promising in detecting some highway features, such as guardrails, medians, bridges, and large road signs.  The proposed method worked well in detecting features from airborne LIDAR data.  In the future, we can improve our method to achieve higher accuracy.

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