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.
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. …
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
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. …
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. …
