Pothole Detection System Using ArcGIS | Esri India

 Enhancing Road Infrastructure: Using Deep Learning to Detect and Assess Potholes in ArcGIS

Robust road infrastructure is pivotal for the economic growth of any nation. However, challenges like wear and tear, as well as natural disasters, contribute to the formation of potholes, posing significant risks to road users. According to data from the Ministry of Road Transport and Highways, fatalities due to potholes totaled 2,015 in 2018, 2,140 in 2019, and 1,471 in 2020.

To address these challenges effectively, leveraging advanced technology becomes imperative. This blog explores an innovative approach using high-resolution imagery and deep learning tools within ArcGIS Pro Image Analyst to detect and locate potholes. Such a workflow not only facilitates accurate detection but also enables authorities to assess the severity of potholes, thereby informing timely maintenance decisions.

Deep Learning Workflow in ArcGIS

Implementing a deep learning solution in ArcGIS involves a structured approach:

  1. Prepare Data: Begin by gathering high-resolution imagery that covers the road network of interest. Using tools like Label Objects for Deep Learning pane or the Training Samples Manager, collect training samples specifically focusing on potholes.

  2. Train the Model: Export the collected training data in the appropriate format, such as RCNN Masks, which outline the exact polygon boundaries of potholes. ArcGIS supports various metadata formats and deep learning model types, ensuring flexibility in model development.

  3. Utilize the Model: Employ the "Train Deep Learning Model" geoprocessing tool to train a MaskRCNN model. This model can then be deployed using the "Detect Objects Using Deep Learning" tool to identify potholes within input imagery accurately.


Assessing Pothole Severity

Beyond detection, it's crucial to assess the severity of identified potholes. This can be achieved by estimating volume using digital elevation models. Geoprocessing tools such as Raster to TIN, interpolate polygon to multipatch, or custom Python scripts enable detailed analysis of pothole dimensions and depths. Such insights are invaluable for prioritizing maintenance efforts and optimizing resource allocation.

Further Exploration

For those keen on exploring similar workflows programmatically, the ArcGIS API for Python offers comprehensive capabilities. This allows developers to automate tasks and integrate deep learning functionalities seamlessly into existing workflows.


Additional Resources

Explore more about deep learning tools in ArcGIS through the following resources:

By leveraging these advanced tools and methodologies, stakeholders can significantly enhance road safety and infrastructure management. This proactive approach not only mitigates risks associated with potholes but also lays the foundation for sustainable and resilient transportation networks. 


To Know More Visit : Esri India 

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