Yeah but once you’ve done it once in R you can just dump your data again, update the theme and boom, done again.

Also 30 minutes? maybe 3.

One of my cats’ name is Mr. 305

#they knew

Turns out land is still cheap and sunlight still generally free.

gif got me like:

“You can observe a lot by just watching.” -Yogi Berra

Idk man, this carnival guy seems pretty confident, and the lady in the labcoat keeps talking about uncertainty…

The universe originated as a vibrational wave from Fred Flintstone playing a dinosaur bone keyboard.

tits out for harambe

I have two of those cats. I still can’t catch them when its time to go to bed.

Yeah. ok. See what kind of biological insight a B- gets you?

Pretty sure from my B- in zoo that sponges eat from what amounts to our waste hole.

So you are supposed to piss in the punchbowl and drink from the toilette.

This is the book you are looking for:

Botany in a day: the patterns method to Plant Identification

ehh go fuck yourself bye…

“It’s crucial…”

new. no filters. it’s like drinking from a garden hose.

my life is spyroing out of contol

I’ve done several AI/ ML projects at nation/ state/ landscape scale. I work mostly on issues that can be solved or at least, goals that can be worked towards using computer vision questions, but I also do all kinds of other ml stuff.

So one example is a project I did for this group: https://www.swfwmd.state.fl.us/resources/data-maps

Southwest Florida water management district (aka “Swiftmud”). They had been doing manual updates to a land-cover/ land use map, and wanted something more consistent, automated, and faster. Several thousands of square miles under their management, and they needed annual updates regarding how land was being used/ what cover type or condition it was in. I developed a hybrid approach using random forest, super-pixels, and UNET’s to look for regions of likely change, and then to try and identify the “to” and “from” classes of change. I’m pretty sure my data products and methods are still in use largely as I developed them. I built those out right on the back of UNET’s becoming the backbone of modern image analysis (think early 2016), which is why we still had some RF in there (dating myself).

Another project I did was for State of California. I developed both the computer vision and statistical approaches for estimating outdoor water use for almost all residential properties in the state. These numbers I think are still in-use today (in-fact I know they are), and haven’t been updated since I developed them. That project was at a 1sq foot pixel resolution and was just about wall-to-wall mapping for the entire state, effectively putting down an estimate for every single scrap of turf grass in the state, and if California was going to allocate water budget for you or not. So if you got a nasty-gram from the water company about irrigation, my bad.

These days I work on a small team focused on identifying features relevant for wildfire risk. I’m trying to see if I can put together a short video of what I’m working on right now as i post this.

Example, fresh of the presses for some random house in California: