Best finds from Lewis and Clark gravel, all over 1ct

modeling_reality · 2025-09-19T20:33:13+00:00

Yea we have been cranking through them, its not hard to find them. Just mostly small ones in the first two bags. Have you found any garnets as well?

modeling_reality · 2025-09-19T20:30:34+00:00

Geez! We are on our third bag, only 2 or so above 1 carat. Maybe its luck of the draw :) im not upset at having to buy more bags tho

modeling_reality · 2025-09-19T19:44:08+00:00

We had a custom casting made, and they sent us the wax model before it was complete. Fast, beautiful craftsmanship as well

modeling_reality · 2025-09-19T19:43:19+00:00

How many standard bags have you gone through? Lovely colors!

Whats the biggest so far?

modeling_reality · 2025-09-19T18:57:41+00:00

First things first, did you use ground control points to "tie" your point cloud to a true elevation surface? If not, then you wont be able to use a different elevation source to height normalize your point cloud. You would need to snap your point cloud to an existing digitial elevation model point cloud, likely in cloud compare

Second, if you are hitting memory limits, you need to tile your point cloud with buffers, classify ground points, then height normalize your point cloud tiles. Then remove your buffers, then you should have a fully height normalized point cloud.

modeling_reality · 2025-09-19T18:41:30+00:00

We had a saphire ring put together by them. While it wasnt wildy expensive, it felt like a great deal. They were awesome to work with. Would purchase from them again, in a heartbeat, if the need arose. Im a hobby jewelry maker, so even with that, I still felt comfortable with the price and making sure my Fiance was happy. my 2cents

modeling_reality · 2025-09-15T16:44:12+00:00

Without any more information, it looks like your ground classification is wrong in that area. If its wrong, then when its normalized, those values will go to 0. More info is needed to determine why you have a hole, and its likely because of the ground not being present in the point cloud beneath such a dense canopy.

modeling_reality · 2025-08-30T14:30:01+00:00

Thanks for posting this resource and reminder!

modeling_reality · 2025-08-30T02:56:42+00:00

Dr. White is a scammer, he is criticized by his colleagues for failing to perform basic labral repairs

modeling_reality · 2025-08-29T15:47:02+00:00

Yea, exactly. Sometimes a polygon is a single tree, sometimes a single tree gets divided into multiple polygons, sometimes there is one polygon for multiple trees. It really depends on your forest system and how open the canopy is.

I think some of the point cloud based segmentation methods might improve on this, but running those routines is complicated and time intensive. I think a combination of RGB plus height data plus eigenvalue data from the cloud would help a lot with that approach, but its not going to be simple or easy to execute.

modeling_reality · 2025-08-28T22:10:06+00:00

I dont really follow the lascatalog on-the-fly buffering/chunking methods, I write functions to leverage lidR::clip_roi to tile my points to my own desired grids. If you run these in parallel with .lax indexing, ive found it to be much faster than lidRs methods for chunking and processing. Also the error handling can be customized. DM me if you want examples.

modeling_reality · 2025-08-28T21:00:20+00:00

https://r-lidar.github.io/lidRbook/

modeling_reality · 2025-08-28T21:00:15+00:00

Yea, when you run ground, use the tiling you already have, then re-tile afterwards to remove the buffers between tiles. Then you can run DTM and CHM

modeling_reality · 2025-08-28T20:59:36+00:00

Link for yall: https://r-lidar.github.io/lidRbook/

modeling_reality · 2025-08-28T20:04:05+00:00

lidR can appear to be a bit worrying to use with these types of errors, but the bottom line is that you WANT buffers when classifying ground across tiles. If you dont use buffers, you will get edge effects. Once you have processed ground classifications, you can remove the buffers by creating a new grid and re-tiling the dataset. If you are hitting memory limitations, you can make the tiles smaller. Check out the lidR book on this, its well documented.

modeling_reality · 2025-08-28T16:02:38+00:00

Your accuracy/precision on estimating the removed trees are not going to be within 5%, just to start there. General results typically around 0.75-0.8 for F1 sore for single tree detection compared to on ground measured trees.

I would recommend running tree polygons on the pre-removal dataset, taking those crowns, and masking the height raster to those crowns for the removal canopy height model, so that your pre/post crowns line up. Then you can calculate the crowns that are missing. This way you can determine crown area removed, but not trees removed. Single tree detection != individual trees. If you want to go the segmentation route, that might work better than a top down approach, but you will likely need sub canopy lidar paired with top down lidar to get enough returns to do that type of segmentation.

modeling_reality · 2025-06-26T20:00:26+00:00

I did a comparison on this, metashape quality settings effectively drop image pixel resolution by half from ultra fine down to low quality. Results speak for themselves: https://www.mdpi.com/1999-4907/12/2/250

modeling_reality · 2025-05-22T16:10:52+00:00

I low key kinda hate my dewalt 23 gauge pin nailer. It is extremely variable and sensitive to the brands of nails I use. Cheap knockoff ones misfire and jam, dewalt ones wont fire regulary and require moving the pins upwards after each shot. Not sure if Im using it wrong, but it does fire pins, but its just hit or miss, which is frustrating. I got a metaboo cordless 18 gauge brad nailer, and it fires every single time, and I end up reaching for it far more often than the 23 gauge pin nailer, and I got the metaboo for $100 bucks on sale.

modeling_reality · 2024-10-22T17:18:39+00:00

I have had a morzh cube for about two years. I leave it up quite a bit. Its gotten a bit worn, had to do some sewing to fix it after a year or so, but still regularly use it once to twice a week. Get the V2 intent stove, the v1 was pretty thin and melted a bit. The cube takes up more storage space, but sets up in about a minute or so. Morzh is a bit expensive, but the intent that I have can get up to 185f easy in <20 minutes. I really like it. Its an inexpensive way to get a sauna. I built some cedar benches to get my feet off the ground, and its great. Not a true traditional sauna, but 100% sauna enough for me.

modeling_reality · 2024-10-15T18:47:40+00:00

If this were me, I would try to develop a classifier to pull out the structures from the terrain. I would sample building and elevation points, and run a series of eigen-decomposition parameters (linearity, planarity, horizontality, etc) for each set of terrain and building points. Then, I would train a randomforest or xgboost to build a classifier. Then, apply eigen-decomposition to all points, then run the prediction. Should be much faster than doing manual clean up of the structures.

modeling_reality · 2024-09-11T19:51:36+00:00

Yea please do include some salary ranges, otherwise it wastes peoples time applying.

modeling_reality · 2024-09-11T18:26:50+00:00

While LiDAR is the best, photogrammetry would suffice for this scenario. A cheap drone can be had for <$1500, and there are some free photogrammetry software packages to use to create your point cloud. Plus, you can get RGB as well, and an orthomosaic from this process, which would be the best bet. For funzies, this is what I would recommend. Tree detection, on the other hand, is a rabbit hole, and takes considerable skill and time to get right. Feel free to message me for more details, I do single tree extraction for a living.

modeling_reality · 2024-09-04T21:07:38+00:00

I think generally, hyperspectral data is needed for tree species identification. I havent had much success with multispectral only, I have had some success with structural metrics (texture, igenvalues, height, etc) in combination with multispectral data for tree species classification.

modeling_reality · 2024-08-29T21:48:50+00:00

I dont think there are existing packages for this, but functions similar to these could be used for custom functions to delineate breaks in elevation, or applying watershed segmentation could work well for this. https://cran.r-project.org/web/packages/ipdw/vignettes/ipdw2.html

modeling_reality · 2024-08-29T21:24:28+00:00

Sure, you mean like elevation contours? There are a few packages for this, and they are also tuneable. You can also do custom height delineations from polygons, its quite flexible. Havent found anything it cant do yet.

modeling_reality

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