Local governments are embracing more modern and scalable ways to support their communities. In an effort to save both time and money, Costa Mesa Sanitary District  (“CMSD”) used machine learning to automate and streamline manhole maintenance. Manhole maintenance is an essential part of the upkeep of cities. Manholes provide critical access points for underground public utilities, allowing inspection, maintenance, and system upgrades. But failure to upkeep manholes can cause a multitude of problems, from road hazards to sewer blockages, and can make it difficult for workers to access underground public utilities, which can lead to other safety issues. Manhole maintenance is an essential part of Costa Mesa Sanitary District maintenance, but this process requires the work of an outside consultant and costs the CMSD over $100,000.

ML to the rescue 

CMSD, in collaboration with SpringML and Google Cloud, developed a project to streamline manhole maintenance by leveraging the power of machine learning (ML) to detect sewer manholes and rate their conditions. This solution saves CMSD $40,000 every year, freeing up funds for other public service projects.

Every quarter, one member of the CMSD drives a car outfitted with a GoPro camera. This car travels through the entire District area, which includes the city of Costa Mesa and small portions of Newport Beach, CA, which is about 218 street miles, and records the roads to detect approximately 5,000 manholes. At the end of the recording day, CMSD members transfer images and videos from the GoPro SD card into a local server. Then these files are automatically ingested into Google Cloud Storage for processing. From here, the machine learning algorithms detect which manholes need repair.

Google Cloud products are used throughout this project. Once the images and videos are in Google Cloud Storage, a workflow with Cloud Scheduler spins up the VM every night to detect if there are new videos on Cloud Storage. If there are, this triggers cloud machine learning, which reviews the numerous images and videos, rates each manhole, and determines if any require maintenance. 

Machine learning to detect and grade manholes

SpringML applied a very systematic approach to detecting and grading the manholes. First, image processing ensures that the region of interest is only the section of the road in front of the vehicle to avoid any privacy concerns. Then SpringML applied a 5-step process using machine learning to detect and grade the manholes.

1. SpringML developed two separate custom TensorFlow-based Mask R-CNN models. Mask-R-CNN is a deep neural network that is used for image segmentation tasks, which means it can separate different objects in an image or a video. 
   
   The first Mask-R-CNN model was created to accurately detect if an image showed a manhole cover. This was a critical step because sewer manhole covers look similar to water main covers. To make the model accurate, it was important that there be no false positives. SpringML used around 50 images of sewer manholes and other images to train and validate that the algorithm could successfully detect sewer manhole covers. 
2. Once a manhole is accurately detected, the surrounding area is masked using image processing to focus on the manhole and then the cropped images are sent to the second model which is used to detect damage in the area around the manhole, called the apron. To reduce the processing time, the second model only does the inference if a manhole cover is detected.
3. The second Mask-R-CNN model uses CMSD Guidelines to decide what types of damage contributed to the rating system. For training purposes, SpringML uses a TensorFlow-Keras inside a Virtual Machine on Google Cloud. The initial model was trained and the Keras weights were saved on Google Cloud Storage. This helped create a versioned system of the models as the model gets refined over time.
4. Then, duplicated detections are cleaned up to have a unique detection for each manhole cover.
5. Finally, Manholes are graded from a 1-5 rating, with 5 representing high damage and 1 representing low damage. 

Once cloud machine learning has analyzed the new videos and images, the final scores are stored in BigQuery. Results are then served to members of CMSD via a simple web application where they can see which manholes need to be maintained. Two staff members review the ML results and determine priorities for repairs. One of the most interesting features of this project is that the model gets continually retrained based on feedback submitted in the web application. For example, if the model inaccurately detects a manhole, a member of CMSD can mark that in the web application and their feedback is immediately used to refine the model.

This solution shows how leveraging machine learning can streamline a necessary local government project and save money and labor while being highly scalable! Not only does this system streamline the manhole maintenance process, but it also allows for more frequent review of manhole conditions and provides a historical view of how the District’s manholes change over time.

Want to learn more about Google Cloud machine learning? Check out this tutorial to  learn TensorFlow and Keras and check out more machine learning tools in our AI Platform.

Cloud-native features are helping public sector teams innovate faster than ever. Ideas discussed in a morning meeting can be a working proof of concept later that day. Managed services can remove administrative burden and reduce the steps needed to design and provision cloud infrastructure. Security can be built-in from the beginning with Identity and Access Management (IAM), Virtual Private Cloud Service Controls (VPC-SC), and Data Loss Prevention (DLP). Short-lived services and infrastructure-as-code allow rapid and cost-effective prototyping. These technologies can be used to architect a solution that follows the principle of least privilege and helps you secure your data.

So the question becomes: given the complex problems agencies face, where do you start? Google Public Sector now offers “Getting Started” and “Scaling” service offerings for CCAI, DocAI, and BigQuery to help you jumpstart your AI journey, based on where you are.

Complex problems, simpler AI-based solutions

Solving more challenging problems with cloud-native technology doesn’t have to be overwhelming. You can approach them the same way you might solve a puzzle: start with one piece that follows another until the larger picture takes shape. Though you can simplify the steps, solving these problems still requires powerful tools. Google Cloud’s AI/ML capabilities may be the answer for your team.

Google Public Sector is making it easier to get started with advanced technologies, beginning with artificial intelligence and machine learning (AI/ML) workloads for government organizations, and it’s something you can do now, one piece at a time.

Automating your FAQs with CCAI

Does your agency require a team to answer commonly asked questions? What if you could train an agent to answer questions immediately and operate 24/7? Contact Center AI (CCAI) can do this and more. Already using CCAI and need the agent to level up to address complex interactive dialogs? Getting Started with CCAI and Scaling with CCAI are new Google service offerings specifically designed to help public sector organizations tackle situations like these.

Automate data entry with DocAI

How many hours does your team spend manually reviewing or entering data from standardized forms? What if you could automatically pull data right from the page? Google Document AI (DocAI) specializes in exactly this—even if the form has handwritten text. Getting Started with DocAI and Scaling with DocAI are new service offerings that help you remove this burden from your team. DocAI automates data entry and makes that data available to other teams while prioritizing both security and ease of use.

Making data and insights accessible with BigQuery

Then there’s all your existing data. You may have years of it stored in many places, and you may not have a way to make use of it when you need it. BigQuery is Google’s enterprise data warehouse. It was designed for data analytics—looking back at historical data to make conclusions about it. But BigQuery’s analytics don’t stop there. It can also look forward in time to make predictions, often using the same datasets. Getting Started with BigQuery and Scaling with BigQuery are new service offerings that help you take your first steps toward AI/ML capabilities by starting with a single table or pipeline that can help make sense of all your data.

The best help is the kind that meets you where you are and gets you where you want to be. Google Public Sector’s new service offerings do just that: help you work through complex problems by meeting you wherever your starting line is, whether you’re ready to start or ready to scale. Let us know if you would like us to contact you about the services mentioned in this article. Let’s solve your highest impact problems together, one puzzle piece at a time.