Roads from Above: Augmenting Civil Engineering & Geospatial Workflows with Machine Learning

Road from Above is partly based on my Australia Postgraduate Intern Projects (Computer Vision and Machine Learning for Feature Extraction) within Aureon Group in Melbourne.

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.

Road from Above

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MacOS X: Installing TensorFlow from Sources [TF Binary Attached]

When I am using TensorFlow on my MacBook Air, I always get annoyed by the warnings comes from nowhere, so I followed the documentation below to build TensorFlow sources into a TensorFlow binary and installed it successfully.  In theory, this will make the TF running faster on my machine.

Here is the document:

If you are a Mac user, you could download the TF binary from here:

Then, you could use conda to initialize an environment with Python=3.6 and install TF by typing:

sudo pip install tensorflow-1.8.0-py2-none-any.whl

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QR Code Detector with Webcam (Python / OpenCV / Pyzbar)

This project is forked from zbar library, I added some modifications, so the webcam can be used as an image reader to detect QR and Barcodes.

code128_1

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