151–160 of 225 posts
Panopticon. I want worldwide 24/7 live video footage. I want with enough resolution to identify letters on a printed page on the ground. And, I don’t want it to cost the entire global GDP to make and operate. Get cracking.
Earlier quoted context omitted.
Unfortunately, physics won't allow for that - with optical instruments you need a big aperture (lens) or you need to fly much lower than Starlink, or both. 1 or 2m imagery may definitely be possible, though! Check out BlackSky for reference.
If you can assume the scene you're capturing is fairly static over time, you can sometimes sort of cheat physics a little bit (keeping space constant and leveraging time) by resampling areas multiple times from different perspectives and cleverly combining that data together Problem is that most the interesting bits people want more data on are quite dynamic in space and time, not just time. Even when it's not you do…
Earlier quoted context omitted.
Not broadly true in my opinion. To give an example from the financial services industry: * Satellite provider or analytics firm sells "car counts" for retail stores * Financial institution is intrigued; this must correlate with sales, right? * But..why don't we just buy credit card transaction data and foot traffic data from clearing houses and GPS trace providers? I often liken satellite imagery to salt. It's great…
This was our experience with car count data which we sold to most of the sophisticated quant funds a few years back. We sold 'raw counts' and our own 'derived counts' that took into account cloud cover etc and was produced by our data science team. The quant funds completely disregarded our derived counts as useless.
Me: 2 years at a satellite imagery company a few years back This is mostly true, though I will add there are some repeatable insights that people will pay for. In the financial sector, there were plenty of people who paid for car counts data back in 2016 (though many customers did not renew) and there are still people today who are interested in oil inventories data, and mall traffic data (though satellite data is be…
Weather alerts?
Weather data is usually derived from geostationary satellites which is sort of an adjacent field to the lower-orbit imagery the article is based on (i believe?)... but i know of a couple of projects doing analytics here - not sure about commercial potential though, they're early stage startups or academia.
sure. but working in aeeospace is cool.
> Allow me to put it more succinctly: selling derived data as a subscription product does. not. work. I don’t care what it is. The juice is never worth the squeeze.¹ Count cars. Count airplanes. Count ships. Segment land cover. Track oil inventories. Estimate biofuels. Measure water levels. Etc. Etc. Etc. I see that he outlined a few exceptions at the footnote… I'll also add Plaid. I think this guy is making some hug…
Fed Ex (quite surprising) have decided that freely providing delivery dates to consumers is too value to leave to third parties... ...or to leave to the shipping endpoint customer, either. I can't tell you how many clicks it takes to determine when a particular shipment will arrive at my door. Off the top of my head: 0. Email from vendor: "your package has shipped!" 1. Log in to the Fedex account. 2. One would think…
Me: 20 years in the defense space business. Stating my own opinion. This is basically right. The problem with space imagery is that almost everyone who wants it has a niche use case, and those few organizations without a niche use case (the US Weather Service, various militaries, etc) generally want imagery that's so specialized to their own problem that they have to spec, buy, and operate their own orbital assets. T…
Is this really true? I guess I can believe it, but it seems strange that the United States is sending over $800 million in weaponry, but won't send over satellite imagery.
>In my opinion, every supervised machine learning model is hopelessly biased by the intent of its creator(s). Namely, it inherits the bias of its training dataset (both geographic and semantic). Profound. And True. Sometimes I wonder whether we can truly call them learning models at all.
Author here - not an original insight, although it's clichéd enough that I can't point you to where I picked it up from. I also want to emphasize that I do not view bias as a bad thing in the context of supervised models. In some ways, I think it's the whole point of a supervised model (to inherit the judgment of its creators). If the bias helps filter predictions that are useful for your goals, it's a good thing.
>In my opinion, every supervised machine learning model is hopelessly biased by the intent of its creator(s). Namely, it inherits the bias of its training dataset (both geographic and semantic). Profound. And True. Sometimes I wonder whether we can truly call them learning models at all.