It’s not fully 3D immersive, but hey, 2-1/2D ain’t half bad. The “dsm40cm” model of Marin County has been published as the county’s default terrain on Google Earth. It’s a great pleasure to work with folks who are not troubled by a county representing its surface on a 40cm single-precision float grid that weighs in at 77 GB. In terms of data bulk, that is about the same as the entire 30-meter version of the US National Elevation Dataset.
What one gets when piling that much detail into a single county of around 520 square miles of land area is every building pad, driveway, and crown of road paving that were resolved. The dsm40cm model was developed as an ESRI Terrain Dataset that incorporated our best topographic contours (1:4800 scale 10-foot; 1:2400 scale 2-foot,) photogrammetric break and water lines, FEMA LiDAR and NCALM LiDAR data sets. All told, they currently comprise 40 GB of vector GIS data. Then, breaking the county up into 20 work areas to maintain ArcGIS in a stable and productive state, 40cm posting interval grids were generated that covered the entire county. When finished, these grid tiles were mosaicked with ERDAS Imagine into a single seamless grid. The WGS84 UTM, NAVD88-Geoid 2003 result was provided to the Google Earth team earlier this year.
As with all GIS data sets, it seems, the more detailed it is, the more rapidly it may need updating. In the works for the next year or so are several improvements to the dsm40cm model. First: the photogrammetric break lines will be segregated into steeper sets that tend to run along ridges, and shallower slopes that tend to delineate road cuts and building pads. The ridge set will be used as soft constraints to resolve some artifacts where they rise above some contours.
Second: incorporate new LiDAR data as it becomes available. Some data has already been provided for the lowest part of Lagunitas creek, and it appears that Prof. Ellen Hines of San Francisco State University’s Department of Geography and Human Environmental Studies has been funded by USGS to gather LiDAR county-wide this year.
So there will be revisions, but an exciting aspect is to see data flows being brought into existence that support different levels of mirror world development.
Publishing the dsm40cm model in Google Earth is an important (and beautiful) threshold to cross. Making use of the dsm40cm model in county operations such as creek and watershed delineation will be the practical benefit that drives the work in the first place. And before too many more weeks, there may be entirely new approaches to publishing the data in an immersive environment (neither Second Life nor Opensim) to share.
Kent Woodlands building pad and driveway, in the shadow of Mt. Tam
I’d read about this, but never before experienced the agony first-hand. Extracting funds from SL, the wait for funds to arrive at PayPal was a bit slow. In fact, in the time it took funds to go from Linden to PayPal, a bamboo shoot in my back yard could have grown taller than me (that’s my RL not SL height!), and would have been over 2 meters tall. Anyway, Process Credits are quite lacking in symmetry with how quickly credit charges can flow into the Linden realm.
During this week of waiting my random prims have been cleared out from Amida and nary a trace of Berkurodam BART Station remains besides a video in Gualala. The video screen was actually entombed by a neighbor, who may not like it but did not send any message.
Anyway–for me this week is all about generating maps and graphics while keeping up with work. I’ve generated a 50cm terrain grid for parts of my county where perhaps 150,000 people live. With computational process improvements I should be able to make production stable enough to generate a 25cm grid. The point is to model terrain slope and aspect within urban parcels. OpenSim can pack 64 terrain megaprim sculpties over each region to refine terrain more than the built-in 1-meter postings, and display 10cm orthoimagery at full resolution.
Last year, I used first-return LiDAR data of the UC Berkeley campus to generate a 25cm grid for 10cm imagery. Now, I’m working with bare-earth LiDAR data from FEMA, topographic contours (densified to 1.5m vertex spacing), and most importantly, photogrammetric terrain and water break lines.
Throwing all those data into the mix, the data are built into an ESRI Terrain Dataset, from which I generate TIN and GRID models at various reolution and extent. The ESRI ArcGIS 3D Analyst Terrain-to-TIN generator breaks down after about 10 mega-faces (so would I…) And the ArcGIS Terrain-to-GRID generator seems to drift into Windows-unconsciousness after about 1.0 giga-cells. So for the grid, I break it down and do the pieces, then merge the tiles using ERDAS Imagine, because the ESRI ArcGIS raster mosaic function does not produce output grids much over 10 GB. As annoying as learning these ArcGIS limits can be, it is very satisfying (and instructive) to see huge swaths of seamless terrain with great detail once it all comes together. Thanks to the break lines, many driveways and most home building site cuts and fills are resolved. And it will be a lot of terrain by OpenSim standards–enough to calibrate terrain for over 20,000 contiguous regions–not that I ever expect to build it all at 1:1 scale!
Sometime, it just isn’t worth it. Such is my new view of tier, in the context of what matters to me with immersive 3D and GIS. For about six months I’ve continued my hold on some land in the classic Stanford sim of Second Life, without quite being able to work out the boundary changes to just barely squeeze in a 1:1 scale model of a single large building. Even if I had been able to get the parcel into the shape that I needed, I still would not be able to model the structure’s dome with a prim that naturally had the large radius required. Not everyone is trying to model a Frank Lloyd Wright public building; perhaps the land can be better used by someone else with an architectural focus.
I’m scaling back ownership this week to the tier-free 512 square meter level in Second Life. I’m also building up a freshly configured Ubuntu 9.04 Jaunty Jackalope 32-bit server (dual 3.4 GHz Xeon – 4 GB, HP DL360 G4) to do some more serious sort of work with OpenSim. In the past five months I’ve developed some terrain data that can handily provide 1-meter postings over more than 500 square miles. With that much to publish, I really need much, much more than 1/8 of a sim, even a suberbly cool sim like Stanford.
View of beautiful Stanford sim with pond features
The orange area is available at L$20/square meter
So if anyone reading this has use for a great 7520 (< 1/8 sim) mainland location in Second Life with over 40 meters of terrain sculptability, it’s available for L$20/square meter. Discount available for OpenSim community members or known GIS people. With the world’s economy as challenged as it seems to be, I’ve decided that it’s time to focus on where things matter most, and for me now that’s OpenSim more exclusively.
I’ve been waiting for some property boundary issues to resolve in SL, and it’s sort of pitiful to see how long that can take. It’s with ever more regret that I find myself on the Mainland. But that hasn’t kept lots of real-world interesting stuff from taking shape.
The following video is not new. In fact it’s about a year old, but somehow I hadn’t seen it until tonight and I found it somewhat encouraging. Thanks for O’Reilly and Where 2.0 for bringing these two on stage together!
And the following pean to Google Earth did inspire me, personally. Hey, I was reading road maps at 5, covered my wall completely with National Geographic maps at 10, learned to navigate with nautical charts at 12, read aeronautical charts and completed an urban planning project at 14. Sometimes, it’s fun in rare moments when it’s dark overcast and I’m in an exotic place for the first time and I don’t know the way north; more often, I’ll savor the feeling of knowing which way is north while dreaming.
Meanwhile, back at the lab, the global set of county terrain is being compiled into an ESRI Terrain Dataset. This will include over 360 million masspoints, merging both interpolated 2-foot interval contour vertices together with FEMA LiDAR mass points, plus break lines and waterlines from photogrammetry. The goal is to use the ESRI Terrain data as a format to stage everything together to produce 30cm grid interval DEM in the urban areas. With luck, we’ll have that ready about the time that the latest photo mosaic finally gets loaded into ArcSDE successfully. Maybe grids from the Terrain can help create very detailed 3D county models. Hey Wei – we still have inverted terrain in Google Earth at the quarry on San Pedro Point! ;^)
Terrain has been in the mix for me quite a bit these past four weeks. I’ve worked on pushing ESRI ArcGIS 3D Analyst to its limits of masspoint digestibility, trying hard to bring everything into focus at the same time that everything is sinking down to NAVD88 datum. An abundant set of waterlines and terrain breaklines have helped to make possible some terrain models that appear to be as good as any one is likely to get from photogrammetric data. As with LiDAR source, I’m working toward a 30cm gridding interval to sample any reasonable-looking TIN models.
One fascinating aspect of the terrain model is where it ends. There appears to be a new 1:1200 or 1:4800 shoreline that can be sussed out of some combination of 2.5-foot elevation waterlines, 2-foot elevation contours, and related artifacts. In fact, it’s a great patchwork of artifacts that must be stringed together. In the tidal flat areas, there is also plenty of need for validation with multiple photos (hopefully shot at times of lower tides).
Adding to the data bulk there’s a new ortho in town, 30cm natural color flown just about two years ago. There’s hope of extracting it from the grip of California HARN coordinates after it is all mosaicked.
