Skip to main content

Utility poles Geo-Localization and Risk Estimation using Deep Learning

Project Information

AI, arcgis, big-data, conda, cuda, deep-learning, geographic-information-system, gpus, hpc, machine-learning, pip, python, tensorflow, unix-environment
Project Status: In Progress
Project Region: Northeast
Submitted By: safwan wshah
Project Email: safwan.wshah@uvm.edu
Project Institution: University of Vermont
Anchor Institution: NE-University of Vermont
Project Address: 590 Main street
Burlington , Vermont. 05401

Mentors: safwan wshah
Students: Jamie Voynow

Project Description

It is useful for communities and utility companies to maintain the quality of their utility poles. These poles are typically made from wood and can be damaged from various weather and environmental conditions. When a utility pole is damaged and falls, the entire community could face the consequences of power outages and dangerous electrical wires in/around the streets. Our goal is to leverage computing resources and deep learning to build geo-localization/assessment models for finding poles of poor quality/high likelihood of damage from street images. This tool will better prepare communities with poles at risk and the utility companies that support these systems to deal with the maintenance of these assets.

The reason behind the superior performance of deep learning approaches is the availability of a large amount of labeled data. A more complete and sufficiently large dataset with geospatial information. In this project, we will use datasets provided by the Vermont Transportation department (VTrans). A previous collaboration between Vermont Artificial intelligent Lab and VTrans has introduced the Automotive Repository of Traffic Signs (ARTS), the largest dataset for U.S. traffic sign recognition. The dataset contains 65,152 sign annotations with spatial and geospatial information from 25,212 high-resolution images that include most of the MUTCD sign types and their GPS information.

In this project, we will use the same annotation tools to label street pols. Labeling will require us to go through each image and specify poles attributes such as x, y coordinates by pixel and GPS location. Given a labeled utility pole, we can either develop a mechanism to calculate its angle of orientation, or we can also label this by hand.

Once the dataset is in order, we can begin with the modeling phase. The most important underlying functionality of our model will be object detection specifically for utility poles. Deep learning is most well known for its contribution to object detection research, and we will be expanding upon this research domain with the addition of a utility pole assessment to our object detection system. Our final product will first detect utility poles and then assess quality/angle/positioning. If the performance of the detection model reaches satisfying performance we will enhance the detection model to include geolocalization.

After the detection and geolocalization, we will estimate the falling risk. This can be done by either integrating this step in the detection model itself or implementing it as a post-processing step by calculating the pole angle from the detection model output.

Project Information

AI, arcgis, big-data, conda, cuda, deep-learning, geographic-information-system, gpus, hpc, machine-learning, pip, python, tensorflow, unix-environment
Project Status: In Progress
Project Region: Northeast
Submitted By: safwan wshah
Project Email: safwan.wshah@uvm.edu
Project Institution: University of Vermont
Anchor Institution: NE-University of Vermont
Project Address: 590 Main street
Burlington , Vermont. 05401

Mentors: safwan wshah
Students: Jamie Voynow

Project Description

It is useful for communities and utility companies to maintain the quality of their utility poles. These poles are typically made from wood and can be damaged from various weather and environmental conditions. When a utility pole is damaged and falls, the entire community could face the consequences of power outages and dangerous electrical wires in/around the streets. Our goal is to leverage computing resources and deep learning to build geo-localization/assessment models for finding poles of poor quality/high likelihood of damage from street images. This tool will better prepare communities with poles at risk and the utility companies that support these systems to deal with the maintenance of these assets.

The reason behind the superior performance of deep learning approaches is the availability of a large amount of labeled data. A more complete and sufficiently large dataset with geospatial information. In this project, we will use datasets provided by the Vermont Transportation department (VTrans). A previous collaboration between Vermont Artificial intelligent Lab and VTrans has introduced the Automotive Repository of Traffic Signs (ARTS), the largest dataset for U.S. traffic sign recognition. The dataset contains 65,152 sign annotations with spatial and geospatial information from 25,212 high-resolution images that include most of the MUTCD sign types and their GPS information.

In this project, we will use the same annotation tools to label street pols. Labeling will require us to go through each image and specify poles attributes such as x, y coordinates by pixel and GPS location. Given a labeled utility pole, we can either develop a mechanism to calculate its angle of orientation, or we can also label this by hand.

Once the dataset is in order, we can begin with the modeling phase. The most important underlying functionality of our model will be object detection specifically for utility poles. Deep learning is most well known for its contribution to object detection research, and we will be expanding upon this research domain with the addition of a utility pole assessment to our object detection system. Our final product will first detect utility poles and then assess quality/angle/positioning. If the performance of the detection model reaches satisfying performance we will enhance the detection model to include geolocalization.

After the detection and geolocalization, we will estimate the falling risk. This can be done by either integrating this step in the detection model itself or implementing it as a post-processing step by calculating the pole angle from the detection model output.