A full-stack Deep Learning Research Project for Identifying Trout Species & More

This blog is in reference to on-going work for the Aqua Vision Deep Learning Research Project

I recently got into dry fly fishing in Montana. It’s a technical sport and I’m not very good (yet!) but it’s a blast getting out on the river and learning more each time I’m out there.

Image for post
Image for post
Rock Creek, Montana

One thing I’ve noticed when I’m out fishing for trout is that I have little knowledge of the different types of trout species. When I catch trout (which is still not very often) I find myself Google-ing photos and comparing them to what I had caught. I can barely differentiate between the different species. It made me think, maybe there is a way to programmatically identify trout species with computer vision deep learning. …


Image for post
Image for post

I recently went on a weekend camping trip in The Enchantments, which is just over a two hour drive from where I live in Seattle, WA. To plan for the trip, we relied on Washington Trails Association (WTA) and a few other resources to make sure we had the optimal trail routes and camping spots for each day. Many of these outdoor adventure resources can help folks plan for multi-day camping trips, figure out where to go for a hike with parents or make sure to correctly traverse Aasgard Pass, a sketchy 2300 feet elevation gain in less than a mile.


Using Machine Learning to Build a Walkability Score

Image for post
Image for post
Heatmap of Predicted Walk Scores throughout Seattle, WA

I live in Seattle and recently moved to a different neighborhood. According to Walk Score’s proprietary algorithms, I moved from the 9th most walkable Seattle neighborhood to the 30th. I can still easily walk to a local coffee shop and barber, but that’s about it! I can tell that I’ve moved to a considerably less walkable neighborhood but it’s unclear how to quantify the magnitude or what goes into a walkability score.

I’ve previously used the Walk Score API as a data source for predicting clustering of electric scooter locations. Walk Score is a website that takes an address and computes a measure of its walkability on a scale from 0–100 using proprietary algorithms and various data streams. …


Image for post
Image for post
Bird scooters in Columbus, Ohio

Ever since I started using bike-sharing to get around in Seattle, I have become fascinated with geolocation data and the transportation sharing economy. When I saw this project leveraging the mobility data RESTful API from the Los Angeles Department of Transportation, I was eager to dive in and get my hands dirty building a data product utilizing a company’s mobility data API.

Unfortunately, the major bike and scooter providers (Bird, JUMP, Lime) don’t have publicly accessible APIs. However, some folks have seemingly been able to reverse-engineer the Bird API used to populate the maps in their Android and iOS applications.

One interesting feature of this data is the nest_id, which indicates if the Bird scooter is in a “nest” — a centralized drop-off spot for charged Birds to be released back into circulation. …

About

Perry Johnson

Data scientist & engineer http://perryrjohnson.com

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store