Aurecon’s experts, across Cape Town, Melbourne and Auckland offices, have been teamed up to develop and test approaches that capture and validate new and existing measurements of the metropolitan road network. Due to the confidentiality, we reduced the resolutions of the aerial images and only opened limited results on the public domain at https://roadsfromabove.netlify.com/. Thanks to Greg More, the design of this website got the best feedback from the workshop (Visualization for AI Explainability) of the IEEE VIS 2018 conference in Berlin, Germany
Visualization for AI Explainability: Projections and Dimensionality Reduction. The goal of this workshop is to initiate a call for “explainable” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.
For VIS, the ultimate goal of interpretability is to help users gain insights into the model for more responsible use of ML. Our application is trying to quantify the surface area of road networks in cities.
This project also presents the use of machine learning (convolutional neural networks) to augment human processes in civil engineering and geospatial applications. In this case, we build an application to quantify the amount of asphalt (pavement) on a defined area of the road network.
The process takes aerial images as input and translates these into units & layers of abstracted patterns (called kernels). These patterns become the building blocks to enable the machine learning system to classify every pixel of an image: for this project as either road or non-road.
Please click the original website and find more interesting details: [Link] Roads from Above. You can interact with the web app demo, like changing the aerial areas for the model or the colours of the land.
Please feel free to leave comments or contact for further project details and more information.