[Geowanking] Geowanking Live // Dec 13 (RSVP Will Carter)
wc at hauntedcastle.org
Tue Nov 28 13:34:06 PST 2006
Hi, I'd like to RSVP to the Dec. 13 event if possible. Thanks!
On 11/28/06, geowanking-request at lists.burri.to
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> Today's Topics:
> 1. Re: Geowanking Live // Dec 13, 18h30 // San Francisco //
> Agenda (David Asbury)
> 2. Probe based mapping of road network
> (christopher.wilson at daimlerchrysler.com)
> 3. Re: Probe based mapping of road network (stephen white)
> Message: 1
> Date: Mon, 27 Nov 2006 12:26:31 -0800
> From: David Asbury <asbury at cemar.org>
> Subject: Re: [Geowanking] Geowanking Live // Dec 13, 18h30 // San
> Francisco // Agenda
> To: geowanking at lists.burri.to
> Message-ID: <188.8.131.52.2.20061127122528.022606e0 at cemar.org>
> Content-Type: text/plain; charset="us-ascii"; format=flowed
> I'm a relatively new member to the list, but would very much like to
> attend the Dec 13 event.
> At 01:06 PM 11/25/2006 -0800, you wrote:
> >Greetings, happy holidays, etc...
> >We're looking forward to seeing many of you at our next get together
> >in San Francisco. This evening will have a bit more structure--there
> >will be a series of short talks and more time to mingle/consume.
> >So far we have the following on the agenda. I'm looking for a few
> >more presenters, so please let me know what's on your mind if you
> >are interested...
> >: Anselm Hook on geoaggregation/placedb
> >: Tim Forseman on the International Symposium on Digital Earth
> >(June, San Francisco)
> >: Brady Forest - News/ideas about Where2.2 (May, San Jose)
> >Event will again be hosted by Urban Mapping in their SoMa/Mission
> >Bay offices. Directions here:
> >Please RSVP if you plan on attending. We'd love to have you but
> >don't have unlimited room. Doors will open (technically this is
> >incorrect; please use doorbell) at 6.30, we'll have people speak
> >beginning at 7.30, have a break to mingle, then another round of
> >speakers and more time to mingle. If anybody has a projector that
> >would be much appreciated. We'll have food/drink but feel welcome to
> >Ian White :: Urban Mapping, Inc
> >690 Fifth Street Suite 200 :: San Francisco CA 94107
> >T 415.946.8170 :: F 866.385.8266 :: www.urbanmapping.com
> >Geowanking mailing list
> >Geowanking at lists.burri.to
> David Asbury
> GIS Analyst/Cartographer
> Center for Ecosystem Management and Restoration
> 4179 Piedmont Ave. Ste. 325
> Oakland, CA 94611
> Voice: 510-420-4565 x105
> Fax: 510-420-1345
> email: asbury at cemar.org
> web: http://www.cemar.org
> Message: 2
> Date: Mon, 27 Nov 2006 16:20:10 -0800
> From: christopher.wilson at daimlerchrysler.com
> Subject: [Geowanking] Probe based mapping of road network
> To: geowanking at lists.burri.to
> <OF3D3E4B3B.4D9A54DF-ON882571F6.007A40A2-88257234.0001D1FB at wkamerica.notes.chrysler.com>
> Content-Type: text/plain; charset="us-ascii"
> After watching this group for a while, thought it would be interesting to
> bring up a topic I have been working on for several years and see if I can
> get any help from the geowanking crowd.
> Goal: Create highly accurate and complete digital maps of the
> transportation network suitable for safety of life applications with
> accuracy commensurate with future GNSS systems (decimeters). It seems to
> me that this can only be done through a statistical, probe based, approach
> since imagery and 'mobile mapping' approaches are error prone with low
> revisit rates.
> Problem: Given a very large set of vehicle PVT (position, velocity, time)
> 1) derive the location of the centerline of every lane, along with lane
> attributes such as direction and ability to cross to the adjacent lane,
> 2) derive the location of all turn restrictions and traffic controls, and
> 3) relate the PVT accuracy of the data to the accuracy of the resulting
> 'map' for different quantities of data.
> For extra credit, identify movements within lanes that indicate a vehicle
> intends to turn, stop, or execute some other maneuver. Of course, all of
> these answers must come with a statistical accuracy metric.
> Background: There are a lot of GPS units in a lot of cars collecting a lot
> of data on where the cars (roads) are and how they move (controls such as
> yields and stops). This data is then thrown away. If this data can be
> captured (and there are efforts underway to do this), how does one build a
> map of the roads and all of the signs and signals that control the motion
> of vehicles? I believe that the entire infrastructure that influences the
> behavior of vehicles is captured in this data, and that, by the central
> limit theorem, the data has ever increasing (and quantifiable!) accuracy.
> This is exactly what is needed for map based transportation safety
> systems currently under development. This is one very promising way to
> address the 40,000+ fatalities/ $200B a year caused by accidents on US
> We spent a couple of years looking at this and devised a k-means approach
> bundling data across the direction of travel to pull out the lanes. The
> data could then be grouped by lanes to derive centerlines. Stop signs and
> traffic lights were easy, we never got to yields or speed limits. Our
> approach was successful, but computationally intensive, and required that
> one work with the entire data set rather than a Kalman filter approach
> where data can be incrementally added to improve the solutions validity
> (or indicate that the world has changed). We also did not get far on the
> accuracy metrics. The key to this problem seems to be grouping vehicles
> into like groups going from 'A' to 'B', where 'A' and 'B' are any two
> arbitrary points on the road network with an accuracy of around 30 cm. We
> can 'generally' assume that a vehicle is within 30cm of the 'lane center'.
> One problem, of course, is that the accuracy of any individual vehicle's
> position is generally somewhat larger than the lane width.
> Does anyone know anybody working this (or similar) problems?
> Any ideas on how to approach this from the geo-statistical crowd out
> there? We came at this from an AI perspective, and I think a
> geo-statistical approach might have gone a different direction.
> Other thoughts?
> PS- This approach is really promising for getting public, low cost,
> accurate maps of transportation networks, and yes, there are some serious
> privacy issues to work through. There will never be unique identifiers in
> the data, and we can cut out the first and last mile.
> Christopher.Wilson at dcx.com
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> Date: Tue, 28 Nov 2006 12:13:21 +1030
> From: stephen white <steve at adam.com.au>
> Subject: Re: [Geowanking] Probe based mapping of road network
> To: geowanking at lists.burri.to
> Message-ID: <545B4EF2-D29E-4B3B-B921-3C3458AB156E at adam.com.au>
> Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed
> On 28/11/2006, at 10:50 AM, christopher.wilson at daimlerchrysler.com
> > Goal: Create highly accurate and complete digital maps of the
> > transportation network suitable for safety of life applications
> > with accuracy commensurate with future GNSS systems (decimeters).
> > It seems to me that this can only be done through a statistical,
> > probe based, approach since imagery and 'mobile mapping' approaches
> > are error prone with low revisit rates.
> As with my previous posts to this mailing list, this is just going to
> be my unsubstantiated opinion.
> A statistical approach to GPS is assuming that the error margin is
> perfectly uniform around the actual location. I would expect that GPS
> readings would have error offsets in specific directions depending on
> environment like a nearby building or terrain shape. Aerial imagery
> is about the only thing that I would trust for this kind of accuracy
> as it has the human factor of being able to eyeball for error.
> Collaborative feedback (aka the community) would be the statistical,
> probe based, approach to identify problems. You can still use error
> margins to indicate the trustworthiness of such data, and gradually
> add in extra information from merging several ways of collecting the
> same data. I would investigate vision systems and image recognition
> as an approach, as road markings are very easy for a computer to
> identify. Lines and black bits.
> Use GPS tracks to locate roads, then computer vision to extract road
> information. GPS tracks contain additional information like turning
> lanes, but trying to extract too much information from the same
> source becomes a problem in filtering trends vs outliers. If you can
> correlate against other sources of the same data, then you can make
> more concrete deductions as well as being able to more easily verify
> the data on the spot.
> By aerial imagery, I don't mean Google Earth. I mean very high
> resolution source data (used to make maps) so you can see all the way
> down to the gum spots on the pavement. This is obviously not as easy
> to collect as a bunch of GPS tracks, but you're going to find it a
> very hard sell to attach life-saving importance to something that
> politicians and the public can't see for themselves.
> For that reason alone, you'll need to conclusively prove that your
> tracks are accurate to that degree, which can only be verified by
> plotting against the reality of the roads themselves.
> Steve (the unknown guy without a famous website).
> steve at adam.com.au
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> End of Geowanking Digest, Vol 36, Issue 17
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