Enhancing Local Government Operations with Roboflow-Powered Object Detection

Introduction

Local Government Areas are designated regions within a state or territory, with a local council assigned the task of providing services and managing that region.  They are primarily responsible for providing a wide range of local services and infrastructure within their defined areas. These include managing local roads, waste collection, parks and gardens, community facilities, and providing community services. They also play a role in town planning, building approvals, and environmental management. 

There are multiple challenges that LGAs face:

• Permit Compliance: Difficult to manually track new driveway constructions, modifications, or non-compliant widths/materials.
• Resource Constraints: Councils have limited inspection teams, and budgets for frequent physical surveys are tight.
• Data Gaps: Inconsistent or outdated asset inventories restrict maintenance planning and grant applications

Object detection—the process of finding and labelling objects in images—is a key technology for streamlining operations. It helps lighten the manual load, ensures asset inventories remain up-to-date, and opens the door to more advanced spatial analytical capabilities.

This post will cover a tutorial on how to build a flexible, Roboflow-backed object-detection tool that helps LGA planners and asset managers to automatically detect and map key infrastructure elements from aerial or street-level imagery.

Why Roboflow?

Computer vision models enable computers to "see" and interpret visual data like images and videos, allowing them to perform tasks like object detection, image classification, and facial recognition. Roboflow serves as a platform for developing and deploying these computer vision models.

Roboflow was launched in January 2020, providing everything you need to label, train, and deploy computer vision solutions. The creators experienced how tiring and slow it can be to train and deploy a computer vision model. There was always a need to write excessive code to format data, it was very difficult to collaborate and benchmarking the performance of different machine learning tools was very tedious. Thus, Roboflow was started so that everyone can have easy access to a computer vision model.

The platform is easy and intuitive to use, and there is a lot of flexibility in what can be achieved using their Public(Free) plan.

Driveway Detection using Roboflow

Step 1: Data Collection

For use cases such as car detection or tree detection, there are already plenty of datasets and models trained on cars or trees available on the platform. But, for a specific use case such as driveway detection, we will need to create a dataset and train a model on that dataset. For that, we need to first collect relevant images. If you already have access to aerial imagery from the local council GIS server or street-level photos captured with vehicle-mounted cameras, you can use a subset of the images for training.

In this tutorial, we'll use screenshots of various suburbs obtained from Google Maps and high-resolution stock images obtained from Unsplash.com or Pexels.com.

Ideally, collect at least 70 images, from different angles and lighting, of a similar nature to the images you wish to test with.

Step 2: Annotation

Once the dataset for training is ready, we can create a new account on Roboflow and sign in.

Once signed in, you will be directed to the Projects page. To start the training process, we click on New Project.

Project Name: Anything goes. In our case, it will be "driveway detection model"
Annotation Group: What you want the model to detect. In our case, "driveway"
Project Type: Object Detection.

Create Public Project.

Drop all the images you had collected here. The files could have been images, videos or PDFs.

Roboflow also shows images that look like some of the images in our collection. If there are any images in the suggestions that match your images, you can add them to the dataset and click 'Save and Continue'.

There are multiple options for labelling - Labelling yourself, with the help of your team or using auto-labelling. For this tutorial, we will choose 'Label Myself' and start annotating!

For each image, draw boxes around the driveways and label them. Repeat this process for all the images.

Step 3: Model Fine-tuning

The annotated images can then be used to fine-tune a model of your choice available on Roboflow.

In Custom Training, there are the following sections:

Source Images: The annotated images in your dataset
Train/Test Split: Choose Rebalance. 70% for training, 15% for validation, and 15% for testing is the ideal split.
Preprocessing: Decrease training time and increase performance by applying image transformations to all images in this dataset. Can be left without changing. It involves applying resize or crop, or changing the orientation of all your images. The changes will be applied automatically
Augmentation: Adjustments like changing the hue or saturation, or brightness can be made to all your images. These edited images will then be added back to your dataset. Your final dataset will thus have more samples to train from. Augmentation is not mandatory.

Once you are happy with all the customisations, click Create.

Different models, each with their pros and cons, are listed on Roboflow. Based on your needs, you can choose the one that works best for you. RF-DETR was chosen in this tutorial. Then, Start Training. Depending on the size of your dataset, fine-tuning time varies. For a dataset of approximately 70 images, it will take more than 2 hours. You will get an e-mail notification when the fine-tuning completes.

Once completed, you can find your model in the models section and click Deploy Model.

To use the model and test it out with your images, you can deploy it using a workflow. Choose 'Build My Own'.

The basic version of the workflow will contain three nodes - Inputs, Object Detection and Outputs. For our output to have a clearly marked boundary and label we need to add 2 more Visualization nodes: Bounding box visualization and Label Visualization. For adding the new nodes, click on the + sign after the Object Detection block.

The 2 new nodes can be customised on box colour, boundary radius etc. For this tutorial, we have kept the color palette 'Custom' and Custom Colors 'FF0000'. This gives the boundary and label box a red hue.

Click on the Inputs box. Add the image you wish to test with and click Test Workflow.

Conclusion

By using Roboflow’s platform and object‑detection models, local government planners and asset managers can transform how they inventory and monitor infrastructure like driveways. With this automated pipeline in place, councils can ensure permit compliance by quickly spotting unapproved or modified driveways, optimise maintenance budgets through up‑to‑date asset inventories and enhance spatial analysis, feeding driveway locations into GIS systems for planning and grant applications. The system can be expanded to detect additional assets—trees, footpaths, signage—and the results can be integrated with real‑time monitoring applications. By embedding AI‑powered vision into everyday operations, LGAs can do more with limited resources, deliver faster inspections, and make data‑driven decisions that better serve their communities.

References

• Roboflow (n.d.). Roboflow: Go from Raw Images to a Trained Computer Vision Model in Minutes. roboflow.ai. https://roboflow.com/.