our last speakers are are bookending keynotes
Dr. John Scrivani, who has a masters in forestry from Clemson
, a PhD in forest management from Oregon State, among
other degrees, which we are not going to list
um and is currently with Virginia Geographic Information Network.
His current job is the geospatial project Manager for VGIN
and his responsibilities involved managing multiple geospatial projects for state
agencies, local government and the private sector. So
um our second keynote for today, um we're really
pleased to have him with us. A lot of
us have been working with him for many, many
years. And so what thank you randi. Um
It took me a minute to find uh the presentation
named Otis, which they all were on the desktop
. So but yeah, so I'd like to move
from the research world to uh kind of the uh
more production world and talk to you about one of
our projects at the Virginia Geographic Information Network, which
is a great example of some of the things we
do and like to do, partly because it was
a very collaborative effort between really if you look at
the data and serving the data to the end.
Uh It's there's plenty of partners from federal agencies,
second world governments, even some nonprofits uh to uh
to universities particularly Virginia Tech, which has been really
instrumental in getting this uh project moving. So let's
see. So uh tell you a little bit about
Megyn. The Virginia Geographic Information Network was established in
Virginia Code in 1919 97. Uh uh It was
tasked to foster the creative utilization of geographic information and
oversee the development of a catalogue of GSS data available
in the commonwealth. Um We have some authorities to
to establish standards and guidelines. Uh One of our
main objectives is to develop a base map layers,
so some core spatial data infrastructure for all of Virginia
, things like Ortho imagery, road center lines and
addressing um elevation and could astral or parcel tax parcel
or land ownership data. Uh We've also moved in
the in the last several years to providing services,
uh geographic information as as web services. And uh
in the last year or so we've also uh established
the G. I. S. Clearinghouse which I
encourage you to go look at a lot of our
data and other people other people's data are either registered
, there are available for uh use either on RTs
online or for download or connection to other uh software
web applications. And then lastly I just mentioned a
little bit about our garden initiative which is um is
our initiative to collaborate with universities and Virginia tech has
been a leader in this area to help provides some
redundancies an extension of our services uh to uh the
first to the university community, but also to the
general public. So this so uh particular thing I
want to talk about is the digital surface model that
we're working to provide a statewide digital surface model and
just a couple of definitions uh digital at least of
the way I use them. A digital elevation model
. Uh would be a a digital model of the
ground surface topography. Uh Digital surface model is elevation
of all features including the ground and anything on the
ground including buildings, trees, um utilities, whatever
. Uh This is an example of a digital surface
model for an area you'll probably recognize. Uh So
this was developed from um lidar data that was collected
uh through um um through the Virginia V. Gin
, through a contract we had with sanborn Map Company
. And uh and the town of Blacksburg paid for
it. We we managed a product and did the
quality control and and produce this product or deliver this
product to the town of Blacksburg. So uh this
was about one metre uh nominal point spacing in the
uh light our data collection and you can see it
. It's got very accurate detail of the lane,
stadium, stadium woods, you know, castle calcium
D uh in the cars in the parking lot and
shrubs and other things all all about. So our
project for the digital surface model was to develop a
digital surface model for the entire area of the commonwealth
from the best most recent and uh obviously available data
. And then to serve this digital surface model out
as a publicly available elevation web service suitable for both
visualization and analysis. And then we, one of
the immediate objectives of our project was to have this
D. S. M. Data served out so
we could utilize it in a application we've recently developed
in cooperation with C. I. T. And
and Virginia Tech, which is the Virginia three D
broadband viewer which is hosted here by Enterprise G.
I. S. So we've had a lot of
people involved in this. Uh We're VJ and obviously
Virginia Tech, both C G I. T.
And Enterprise G. I. S have been involved
in this. Um And then the light our data
that has been used in collect building this digital service
model came from a lot of different projects funded by
and managed by a lot of different people. A
lot of it's from USGS and they're in their in
their national three D. Elevation program. Uh Some
of its from National Geospatial Intelligence Agency, fema and
RCs have also funded a good bit uh Department of
mines, mineral energy, some some local government collections
. Um And then uh some of the um some
of the broadband grant which was administered by C.
I. T. Was used to process the uh
oh photo imagery which I'll talk about. So where
we have uh No I guess I'll talk about that
now. We got that slide. So where we
uh the best available data is the lighter where we
have it but we only have a lighter. I'll
have some maps here in second. We only have
a light are available for about 25 30 20 let's
say right now available of the area of the Commonwealth
of Virginia. So where we don't have white are
we went to a photograph, metric production of of
GSM. And so we have one of our other
programs is producing aerial Ortho imagery uh for the entire
commonwealth. The most recent collections were 2011 for the
western half of the state, 2013 for the eastern
half of the state. So where we don't have
water coverage, we used methods with the stereo imagery
from those data collections to create a digital surface model
. So potential applications uh we could, as I
mentioned, the wireless broadband coverage and and our radio
frequency propagation models can be supported more accurate with a
digital service model than than a. D. M
. Uh And then uh one of the things I
. C. I. C. G. I
. T. Is going to be working on soon
as a site suitability for vineyards, solar radiation assessment
and transportations, uh uh safety analysis. So here
is the example of the uh broadband uh three D
. Viewer uh Seth is that live for people to
use now? Yeah. Okay. It only applies
right now to town of Blacksburg because that's the only
data that's available. So, uh, here's an
example of what can be done at the surface model
for looking at potential for solar, uh, solar
panels, looking at the rooftops, looking at what
solar radiation gets to those rooftops at different times of
the years. Uh So the data sources, the
latter data, we have The 10,000 851 sq mi
is the green area in here. This is the
data that has been collected from 2010, uh up
to 2013 and delivered uh to uh the original project
sponsors, but also to Vi Gin. And just
in the last couple of weeks, delivered to Virginia
Tech to Enterprise J. S. Uh, the
two areas kind of impeached color are areas that uh
, have been flown and should be the Virginia Beach
Hampton Roads area hopefully will be delivered next week or
two. And then, uh, the kind of
that Shenandoah Valley one probably a little bit later.
Um, we also have with weather permitting, uh
, have these out of, out of order Or
is that so I'd gone, sorry, way out
of order. Um, so we have current projects
that are being flown right. Uh, hopefully today
we had, I had a report, uh,
yesterday's report of the day before slight. We had
about 40 of this area flown. So another 60
475 sq mi. So Just to kind of summarize
some of the latter projects that we've had since 2010
and the cost of those, uh, we,
we, we got up to about, uh,
if we get through the 2013 and 14 flights,
which are 2013 data that's being delivered in 2014 flights
that are being flown right now, we'll get up
to about 18,261 square miles. Um and which would
be a little less. About 43% of the area
of Virginia. And uh costs have been, You
know, roughly not sure exactly on some of the
2014 cost yet, but roughly$6 million. So
another six or$7 million it's roughly$300$350 per
square mile. So what are is once you have
the water data collected, it's very easy to get
the digital surface model. Uh There is some classification
that is necessary to eliminate noise and other anomalies in
the data. But once you have that, all
you really needed to do is take the first returns
the first thing that white our pulse hits and uh
that can be uh used as your digital surface model
. There's several ways of analyzing that data or explain
that data. Uh In our Js you can vote
in terrain datasets or you can rest your eyes it
into a digital elevation model or a digital surface model
. Um But you can also use the point the
point cloud uh files directly with things called last datasets
and our G. I. S. Or mosaic
datasets of the last files themselves. Excuse me.
The last files are the public domain source uh file
format for storing point clouds. Or you can have
mosaic datasets of last datasets. So uh where we
don't have uh white are uh we fill in with
the digital service model derived from stereo photography. So
we looked at a couple software options of doing that
. Um The methods both both methods uh and use
auto correlation where you take the stereo pairs. And
there's auto automated methods for locating features and two or
more stereo pairs and computing their elevations. Um Several
commercial algorithms are available. We tested to some actives
three D. Correlate er an intergraph semi global matching
program. And we did that. Here's the light
. Our GSM rast arised for the same area we
had in previous slide and here is the semi global
matching. It's a little bit little bit fuzzier.
We did it we did it with three ft spacing
for the uh the output points. Uh but it
looks pretty good. Uh The uh initial uh try
with some active was not quite as good. Um
What we had some issues with cassell coliseum, that
kind of arch curved arch ruth. Uh They went
through back through and found some ways of dealing with
that. But one thing I did want to point
out is that if you look at both this some
active product and the S. GM product and then
look at the stadium woods area and then the white
areas are taller. Uh So uh part of that
is certainly one reason for that is that this is
leaf off imagery, which is what our base map
imagery is intended to be. Uh So these these
large decisions trees with the uh they're having more trouble
with the auto correlation methods and photograph a tree to
get the true tree heights where the lights are,
even though that's the latter was leaf off as well
. It did a much better job. So,
uh but we uh and we did some here is
just a difference between the inactive and the lot are
subtracting it. So the reds showing you under prediction
of heights. Um and the blues showing you over
prediction of heights. And actually what that is actually
where the stereo pairs did a little better. Because
what's actually happening, we determined there was that the
light are because of its scanning nature was has shadow
areas that the stereo pairs are filling in better.
So a lot of those uh thought of those are
actually areas that are just in shadow in the white
are. So uh um we decided to go ahead
with uh the same active product, partly because Sanborn
, which was producing our image, our Ortho photography
was actually doing using the same active product For the
oracle rectification of the 2013 imagery. And they included
in the contract that they deliver that digital surface model
From that processing to us at no additional cost.
So, the 2013 data didn't cost us anything.
And to keep it all consistent and after what we
learned with the quality assessment we could feel that was
pretty good, fairly comparable S. GM. Product
that we have them go back to the 2011 data
and process that to produce the digital surface model for
the areas where we needed it. Uh So uh
the uh we performed the QA Qc and house on
the sand board product we gathered, we had that
data delivered to us in last file format. So
in terms of serving it started and serving it,
it can be treated the same as the lidar data
. Uh Now one thing with the digital surface model
from photograph a tree. We can't without additional processing
create a digital elevation model, bare earth. So
where we don't have wide our data which is fairly
easy to produce the bare earth digital elevation model.
We're gonna go and rely on our traditional methods of
producing a digital train model or digital elevation model which
is photograph metric, derive mass points and brake lines
that were done by by stereo analysts. So and
that's days were updated in 2011 and 2013. So
uh so getting that data, the data has been
collected, processed uh and delivered to Virginia Tech and
uh last collections of last files uh Quite a few
. Gosh, I've forgotten now. Seth remember how
many terabytes of data I delivered you? It's about
five or six so far. We haven't expanded at
all. Yes. Right. Because it's yeah there's
there's un compressed last files and there's 22 competing compression
algorithms out there or compression formats. But anyway it's
lots of data obviously. So uh the the general
idea of serving these is to use mosaic datasets and
a mosaic dataset. If you're not familiar with,
you can either have a whole collection of imagery or
in this case point clouds. And you can you
can build a mosaic dataset which is really just a
set as a catalog of all those files, image
or point cloud files and mosaic The rules how you're
going to mosaic those when you put them together into
a seamless product and then how you rast arise them
, how you take them and put them into a
mosaic pasteurized property. So it actually doesn't create a
new copy of the data. It doesn't do the
rationalization and make a whole new file there. It
just has a way of of of serving that data
out through these mosaic mosaic the rules and rosters,
raster rationalization properties. So you have a service which
can be the elevation service shown here which is in
a sense produced on the fly. Now we can
speed up serving if we cash some of that.
So we're going to probably do that but it doesn't
have to be done. And then there's other things
once you have that surface out there you can add
additional processing function change to create things like slopes services
like slopes, hill shade aspect other anything you can
basically do is a digital elevation model. You can
do that and serve it out without actually having to
create a whole another bite terabytes worth of data to
do that. And so uh what Virginia Tech's Enterprise
Gs is going to do is build uh pretty much
this model where we're going to take the last files
, put them into last data sets. We're gonna
we're not going to go directly into mosaic datasets or
terrain but we're gonna go from the last data sets
to mosaic datasets and then publish out the Image service
elevation Image service. They're basically the same thing in
terms of the every software but obviously imagery showing your
reflections values and the elevation services showing elevations. So
we have some still some work to do. It's
not out there ready to roll yet. We have
to determine whether we're going to use brake lines in
the light. Our data set, we have uh
we developed all the light are with USGS guidelines for
what's called hydro flattening For one thing light are the
near infrared light or has used is pretty much all
absorbed by water. So you get very sparse returns
and water and very inaccurate elevation. So you get
a very rough, inaccurate surface over water. So
what is done is break lines or polygons are created
to flatten the area where there is water and USGS
standards. It's two acres in size or water body
or 100 ft with for a scream. So if
we incorporate those into certainly the digital elevation model we
want to serve. But one of the questions do
we incorporate that as well into our digital service models
? And probably I'm thinking we will, we're gonna
look at that, make sure that's the right way
to go. But for two reasons, one yr
doesn't do water well, obviously as I mentioned,
but neither does the stereo viewing. You also get
very irregular surface inaccurate services over water. So I
think we probably should just flatten the surface model for
where there's water. Uh there's options. Uh the
latest release of R. J. S has a
compressed file format which is, you know, reduces
the storage size by Uh down from 10 of the
original size. And it also is claimed to be
a faster in serving. So if that works,
that will be that will save us this space and
increased performance. Um then uh the uh we also
need to determine what services, what image services we
want to put out their elevation services. Certainly the
digital surface model we need for our application, a
digital elevation model. The ground surface for the lighter
areas. That's gonna be for straightforward. And then
for the non white are areas uh We probably want
to go with producing a digital elevation model from our
digital train model of of of mass points and brake
lines. And then uh and then will this data
be available for download if you want to use it
for further analysis on your own desktop? That's certainly
our intent. I think we have to figure out
just how that's gonna work with the uh with the
capabilities we have here at enterprise Js. And then
uh then we'll create and publish these services, announced
them to you and but you start using them.
Um so a couple of the things that I wanted
to mention before I finish up is that I mentioned
before the Virginia genius clearinghouse is uh what we like
to be, we like it to be the place
for you to go to find data for Virginia Julia
, spatial data for Virginia, it's implemented through RGs
online. Um And one of the things I wish
they would changes, I don't know if you can
see it, but there's a little signing, there's
a little sign in link up there. And so
if you come to this site, you may think
you need an account to use that to sign in
. You don't have to anything that's publicly available.
You can search for and find uh you only need
to sign in if you want access to some of
our restricted data sets. And if you need access
to those restricted data sets actually you won't be able
to find them. But if you know about them
um if you know about them or you know you
think they may have data and one of our restricted
data sets actually that you may be most interested in
our tax parcels of land ownership parcels. And um
if you have an RGs online account from Virginia Tech
for instance. Uh and you you know contact me
or somebody else that region and you have a good
reason for accessing that data. Uh Well we can
give you access to that group to get access to
that data. Uh It's just our local got many
of the local governments prefer that. We don't make
their data just easy to download. Many of the
parcel data sets are available and you'll be able to
find them that the federal government has indicated there five
of us just sharing them. So um so I
encourage you to go there and look around you.
There's things you can download like layer files, shape
files, trained data sets. Um There are also
things you connect to like our imagery services. Other
are geo coding service uh and other services you can
add to a web map or a web application.
The other thing as I mentioned, our garden effort
which is a collaboration with universities to help get our
base map data out to the universities for them to
use but also provides us additional uh redundancy and having
other sites serving some of the same services and maybe
even serving some of the services we're not interested in
serving like older imagery. Our our objective is to
provide the latest imagery to local governments. Uh,
so that's our focus and we may not want to
we want to retire some of our older data sets
, but certainly a lot of people might be interested
in older data sets. Uh, so Virginia Tech
again has been kind of leader on this setting up
the garden site and some of our services are replicated
here. And uh, you know, and so
will be the elevation service will actually be the,
at least at first, the only place that that
elevation service will be available through the Virginia Tech garden
site. We also have a garden at William and
mary. Some of our imagery services are served there
. Uh We tend to, we want to have
everybody have the similar U. R L garden dot
whatever university dot edu. Um We also have a
memorandum of understandings in place with Virginia, Commonwealth University
University of Richmond mary Washington, jmu. In Radford
. Uh to participate in the garden program. Some
of those have some services already stood up, but
we've not made them kind of publicly available. They're
not really quite ready for what? No. Why
? Uh ready to go? Why yet? So
Rush through that. But I hope only have five
minutes so after. So we want to take some
questions. You have time. So I didn't know
if you wanted to mention uh not data up and
use playing cover effort that you guys are working.
Uh Yeah, the the the Bay program, the
Chesapeake Bay program has really interested in in getting uh
good land cover information, high resolution when cover information
more, you know, down to the probably like
one m resolution uh for the entire Chesapeake bay.
Uh We may try to get it for the entire
state. Uh There are there are two budget amendments
, one in the Senate and one in the House
bill right now. That would provide some fun to
do that. And Vi Gin is tasked with coordinating
that uh as you if you follow Virginia politics,
we don't have a budget. So we don't know
whether that money is going to come or not.
So hopefully hopefully we'll know soon, hopefully before July
one. So I still have a I'm not furloughed
So, uh, but the other thing I forgot
to mention quickly is that we do have the 2013
imagery services out now. And I think you sent
out a list announcement on our list sir. Yes
. I'm calling to ask this question on the land
use land cover. How you have a plan on
how you're going to develop that uh go back to
the uh the intent is to use high resolution product
a lot of it. A lot of the impetus
is getting very accurate and previous cover and the feeling
at least for a lot of people is that you
need at least one m resolution or higher resolution.
So uh uh, certainly my feeling is that Virginia
based map because it's leaf off and it's at least
one ft some type places six inches or three inch
resolution is the best place to go. Uh We
may also use the nape imagery where we which is
available statewide at one m leaf on and we may
also use the light our data where we have it
because that could help to the classifications. But another
thing we have to another hurdle with that is getting
all the partners and interested parties to agree on a
classification scheme. That's been that's been interesting. All
right, other questions. Thank you, sir.
All right, well thank you. Before we break
to the posters, we have a special presentation by
Dr Campbell. Yes, I'm not an important mission
but I won't be long. Uhh I think we're
all aware there are people in our community who do
some work that gets recognition outside our community and sometimes
they make special awards for these folks and sometimes the
people involved are not available to get the award at
the time it's presented. So the American view people
are group of 39 states who works nationwide on sentencing
NGS activities has asked me to present to tammy Parisse
a special award. Mhm. Yes, you have
to come. Okay. Uh huh. To appreciate
this. Nice citation. It talks about her worked
outreach to communities outside with geospatial activities outside the university
. Her work books, I forgot um training state
agency employees and especially for her work to develop tutorials
to bring um the religious image processing tools to a
broader community of people which is now reaching what's the
latest count on our. Mhm. This is about
beyond the house, so Thank you very much before
we break to the social. A couple points.
First off there are over 40 posters out there and
they're excellent, so don't bolt because the oral portion
is done, there's lots of fun science out there
to really explore honestly. Um I want to give
credit to the remote sensing interdisciplinary graduate education program for
a second year in a row. Um sponsoring our
social um and so enjoy it. Um Also credit
to the same program for helping to sponsor our keynote
speakers. I also want to give credit to our
poster session judges who are the following carrie, Goldbaek
, Dubose, joe baker, MEREDITH steel and bob
Oliver who will be presenting hopefully the results of that
Judging at about an hour. So we'll come back
here at 10 after five for the results of that
judging. Hopefully Folks will be around and can and
will stick around long enough to socialize because it's really
fun to interact with each other and see what's going
on. The whole purpose of this activity, you
know that we've been doing the last 16 years is
to build community. So let's go out there and
have a cold or not so cold one and build
some community. So, um please um let's go
do it. We'll be back here in an hour 519 --> A:middle L:90% .