Hi, I'd like to RSVP to the Dec. 13 event if possible. Thanks!
On 11/28/06, [EMAIL PROTECTED]
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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
([EMAIL PROTECTED])
3. Re: Probe based mapping of road network (stephen white)
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Message: 1
Date: Mon, 27 Nov 2006 12:26:31 -0800
From: David Asbury <[EMAIL PROTECTED]>
Subject: Re: [Geowanking] Geowanking Live // Dec 13, 18h30 // San
Francisco // Agenda
To: [email protected]
Message-ID: <[EMAIL PROTECTED]>
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.
Thanks,
David
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:
>www.urbanmapping.com/about/contact_directions.html
>
>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
>supplement...
>
>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
>[email protected]
>http://lists.burri.to/mailman/listinfo/geowanking
---------------
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: [EMAIL PROTECTED]
web: http://www.cemar.org
------------------------------
Message: 2
Date: Mon, 27 Nov 2006 16:20:10 -0800
From: [EMAIL PROTECTED]
Subject: [Geowanking] Probe based mapping of road network
To: [email protected]
Message-ID:
<[EMAIL PROTECTED]>
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)
information,
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
roads.
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?
-=Chris
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.
[EMAIL PROTECTED]
650/845-2579
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Message: 3
Date: Tue, 28 Nov 2006 12:13:21 +1030
From: stephen white <[EMAIL PROTECTED]>
Subject: Re: [Geowanking] Probe based mapping of road network
To: [email protected]
Message-ID: <[EMAIL PROTECTED]>
Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed
On 28/11/2006, at 10:50 AM, [EMAIL PROTECTED]
wrote:
> 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).
--
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End of Geowanking Digest, Vol 36, Issue 17
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hauntedcastle.org
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