It also sounds like they would already have one, and / or that it would be relatively easy to move if they want or need to. Don't they go back to their more rural homes for special events, for example?

Agree - ger living is not necessarily a failure of public policy, it could just be a cultural decision. Even Genghis Khan lived in a ger. Of course, for some people, it's likely to be a matter of necessity, for others, a matter of choice, but it's not prima facie bad.

> When ineffective policy results in a large chunk of the populace generationally living in yurts on the outskirts of urban areas, it’s clear that there is failure.

That's not at all clear.

I've traveled across Mongolia on a motorcycle many years ago, and one thing I never expected is how absolutely everyone living in a permanent house also has a yurt in their backyard, regardless of how good the house is. This made no sense to me as an outsider (like, do you really need a second house?) so I asked a local about this, and was given a funny look. Yurts are just hardwired into the culture, it's a status symbol, it's where you invite a guest, it's what you use when living outside, it so many things at once.

Mongolia is also really struggling right now with a mass migration off the plains because of several very cold winters that have decimated their flocks. There just isn’t enough room for them to move into permanent buildings even if they wanted to.

I've been in a Uzbekistan palace, I think it was Khiva. And it was, well, a palace, with courts and richly decorated rooms. But at certain fully enclosed by walls court there was a circular place where yurt have been standing. Khans were tracing their lineages back to Genghis Khan, and it was unbecoming for the khan to spend nights under the firm roof, even if it's in a middle of the city with long sedentary life style traditions. All the visiting relatives wouldn't approve.

https://en.wikipedia.org/wiki/Toshhovli_Palace

https://en.wikipedia.org/wiki/Toshhovli_Palace#/media/File:K... that circular spot.

'yurt' is a Turkic word (don't confuse with Turkish), means "dwelling place", also commonly used for "home country" in languages like Uzbek. Yurt for the nomads was far more than just a place to sleep. Mongols I believe use a different word - 'ger'. Fun fact - Turkic languages were lingua franca of the Mongol Empire. Mongols perceived their language as "sacred" and wouldn't let non-mongols teach it freely. That could be one of the reasons why we have Turkic languages spread across the vast territory instead of Mongolic.

Just so you know, Turkic languages span an enormous landmass from Turkey in the west, across the Caucasus, Central Asia, Russia, China, parts of Iran, Afghanistan and Mongolia. This represents one of the largest continuous language family distributions on Earth - spanning roughly 13,000+ kilometers east-west across Eurasia.

> Finally, that's a much smaller number than I expected for a country of 3 million.

172k of them? That still seems like quite a lot of yurts; certainly more yurts per capita than anyone else has.

> Finally, that's a much smaller number than I expected for a country of 3 million.

172.7k yurts. Assuming that these are family residences for the most part, if we take an average occupancy of 4 (which is probably too low - the fertility rate is still quite high there) gives ~691k people living in yurts - approximately 20% of the population of 3.5 million - sounds reasonable.

My quick estimate before clicking the link was:

From my memory: 3 million people, 1.5 living in the capital.

Let's say 1 million are living outside cities.

4 people per yurt.

250,000 yurt.

Add some extra yurts because there will be people having more than one or people living in a house with a yurt in the garden or yurts used as warehouses, etc

300,000 which is almost the double of the count from the ML app.

This is a nice idea that often comes up in geo/ml projects. (Why not just use OSM for all your labels?)

To start, OSM doesn't use Google Maps imagery for annotation due to licensing concerns. As someone else mentioned, it's rarely clear whether researchers have the right to use Maps imagery let alone download/re-publish it. Part of the reason is that Google sub-licenses imagery from several different providers who are usually extremely protective of IP. So immediately you'd have image/label alignment issues.

Even if you had access to the image that someone used for labeling, it's non-trivial. They might not even have used an image! For example you might walk around and take a GPS reading next to every object and use the keypoints as object centers. Sometimes the annotation quality is low, for example if you want to try using building outlines or roads as segmentation targets for aerial imagery. Or things are simply misaligned. Also since yurts are inherently mobile, you might not even be able to use those labels because objects have moved and there's no guarantee they'll be present in Google Maps.

Finally you'd have issues of omission/commission, because you would have to assume that OSM is complete. That's very sensitive to how active the local community is. Some places are accurate down to the fire hydrant. Where I live, there are plenty of unmapped businesses that have been here for years. Though you could definitely use it to cross-check your own labels + predictions.

The standard for detecting objects on tiles is to discard border predictions and rely on overlap (sliding window) prediction + non max suppression (NMS) to handle duplicates. The overlap is usually something like 1x receptive field of your model, and your "discard" region is a bit larger than your max expected object size.

Ideally you’d verify against an in-person count of yurts over some control area. Otherwise this is just based on an assumption of what yurts look like on satellite.

I chuckled a little, but as a non-native speaker: what would be the correct phrasing? "Using machine learning, I counted all the yurts in Mongolia?"

thank you for this

*edit (I mean this sincerely, it made me laugh and I did not see it at first)

I'd bet quite highly that it's non-zero.

I assumed a yurt was a type of person/job, so I initially read the title the same way

Do they build some kind of foundation for them?

Just to clarify for some people, in polite company, a turkish house-tent is what we call a yurt, whereas a house-tent in mongolia is not a yurt but a ger. In France these are called shabadoos, in canada we call house-tents plumbuses, and in the US these are called fleebs. I personally get offended when locals see my shabadoo and refer to it as a ger.

> People reading this uncritically will come away with completely wrong ideas about the number of yurts in Mongolia

Who is harmed by carrying around a mistaken number for this, especially if they notice the 40% confidence?

As to the rest, I read it as an application of tools for an interesting question, not a comprehensive or authoritative how-to. It’s scaled napkin math, and napkin math is very useful.

Just create another account.

I don't understand the reasoning behind that besides market exclusivity.

Object detectors output detection bounding boxes along with confidence scores. The higher the score, the more confident the model is that the associated bounding box is a correct detection.

When used in applications (like this one), the user typically establishes a confidence threshold and then every detection above that threshold is treated as a positive detection, the rest are discarded. The choice can be arbitrary or (sorta) principled.

One potential issue with this is that gers actually leave circular patterns once they've been removed, where the soil is disturbed and the grass is light-starved or dead. It takes a couple years for those to fully disappear after the ger is removed. Gers themselves can be any color. White is traditional (from a time when white wool was more expensive), but not mandatory.

It's in the article, second to last passage: "In total I found 172,689 yurts with a prediction score of greater than 40%."

With one extra step, you could get a lower bound with very high confidence. Just take a random sample (N=100) of the detected objects, and determine manually what percentage are really yurts. Then multiply that percentage by the number of detected objects.

I was just about to mention that there's a local developer/improv artist with similar experiences and then I saw your username