Developing an UI for historical orthophotos timeseries data
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
| Title |
| |
| Title of Series | ||
| Number of Parts | 351 | |
| Author | ||
| License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/68986 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language | ||
| Production Year | 2022 |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
| |
| Keywords |
FOSS4G Firenze 2022256 / 351
1
7
13
22
25
31
33
36
39
41
43
44
46
52
53
55
58
59
60
76
80
93
98
104
108
127
128
133
135
141
142
143
150
151
168
173
176
178
190
196
200
201
202
204
211
219
225
226
236
242
251
258
263
270
284
285
292
00:00
Multiplication signDigital photographyRange (statistics)Computer animation
00:18
Server (computing)Archaeological field surveyComputer-generated imageryState transition systemScatteringDecision theoryRSA (algorithm)Archaeological field surveyScaling (geometry)Range (statistics)ResultantUser interfaceMereologyMedical imagingConnectivity (graph theory)Multiplication sign1 (number)Digital photographyComputer animation
02:38
InfinityPrice indexScale (map)Computer-generated imageryRaster graphicsReflection (mathematics)Multiplication signVector spacePolygonMedical imagingScaling (geometry)CuboidSubject indexingQuery languageWeb 2.0Web browserClient (computing)Level (video gaming)Service (economics)Computer animation
04:29
Price indexMultiplication signProgram flowchart
05:01
Archaeological field surveyPrice indexAsynchronous Transfer ModeRange (statistics)Single-precision floating-point formatLevel (video gaming)Range (statistics)Subject indexingWeb 2.0Selectivity (electronic)Computer animation
05:33
Archaeological field surveyDifferent (Kate Ryan album)Selectivity (electronic)ExistenceDigital photographyMedical imagingMultiplication signComputer animation
05:54
Open sourceZoom lensLevel (video gaming)Attribute grammarPersonal area networkForestMetadataMusical ensembleTime zoneVector spaceDot productMetadataSystem administratorView (database)Channel capacityDigital photographyMereologyDimensional analysisLevel (video gaming)Time seriesCategory of beingZoom lensAttribute grammarMultiplication signSelectivity (electronic)Server (computing)Slide ruleUser interfaceElectronic mailing listAsynchronous Transfer ModeComputer animationProgram flowchart
09:36
Core dumpComputer animation
09:54
Observational studyArchaeological field surveyServer (computing)Medical imagingLevel (video gaming)XMLComputer animation
10:31
Archaeological field surveyObservational studyPrice indexAsynchronous Transfer ModeTesselationSubject indexingCuboidComputer programmingBitRight angleDatabaseMedical imagingScripting languageDigital photographyMultiplication signPolygonElectric generatorProduct (business)Open sourceComputer animation
11:55
Digital photographyLevel (video gaming)Artistic renderingRange (statistics)Server (computing)CASE <Informatik>Multiplication signPixelComputer animation
12:24
SineOpen sourceDigital photographyDatabaseComputer file
13:19
Visualization (computer graphics)Zoom lensMetadataTime zoneAttribute grammarArchaeological field surveyPersonal area networkBoundary value problemPoint (geometry)AreaMedical imagingMereologySoftwareProduct (business)Process (computing)Negative numberComputer animationProgram flowchart
14:13
MiniDiscTransformation (genetics)Electronic program guideMathematical analysisOpen sourceDigital photographyAuthorizationService (economics)
14:41
Open sourceCloud computingGEDCOMMetadataAttribute grammarDigital photographyAuthorizationParameter (computer programming)Web 2.0Connected spaceMedical imagingService (economics)Category of beingProjective planeResultantLevel (video gaming)MereologyComputer animation
16:53
Vector potentialLevel (video gaming)InformationSign (mathematics)Internationalization and localizationWindows RegistryDigital photographyService (economics)Beta functionWeb 2.0Client (computing)Computer animation
17:08
Projective planePlanningMultiplication signMereologyMappingService (economics)OvalResultantConstructor (object-oriented programming)Computer animation
Transcript: English(auto-generated)
00:00
filter the existing footprints of the existing auto photos by what the user is interested in. User selects the time range, it can be a certain year or start year end year range.
00:20
And maybe if you can about six or seven minutes. National survey now around two and a half years. So the basic goal was to and then the other way hometown. Okay, here's an example of our coverage for the year 2022.
00:44
But if your user is interested in this area. So if he orders auto photos from 2022, user gets nothing. Interesting to see what will follow.
01:04
Okay, here are some old coverages from the 1930s. You get some idea about how scattered the data is.
01:23
Okay, then we had some ready-made components of national SDE portal, which we decided to add some new features to that portal.
01:47
Okay, I'll stop here. This is part of the end result. You can see that when user has selected a certain pounding box, the timescale here is updated automatically.
02:04
It shows only the images that are available on that certain area. There's one image, one year from the 1930s and the next couple from 1950s. And then new ones come every third year.
02:20
And this scale time range is updating automatically when user is panning and zooming on the user interface. What comes next? There is a time slide, I believe.
02:54
Okay, this is something about the technical solution that we made. We have, let's say, 100,000 images.
03:04
And if all these 100 bounty boxes or polygons are transferred to OpenLayers client, it will be quite slow to use. So we have a hybrid solution that on a small scale
03:30
we are generating the index map with web map service. It's time-enabled.
03:41
And when we zoom in closer, we switch to vector data because with vector data, we can deliver more attributes and give more possibilities for the browser client
04:00
to make filtering and queries of what these images are about.
04:33
I'm not sure. It may work now. I should be trying because this is the first time when I see these slides.
04:46
Okay, maybe it's just that I need to do my best. Okay, this is about selecting different years and how the coverage is changing, but you saw that part already.
05:09
Okay, then the web client, it has two modes. You can select one certain year or you can select a wider time range.
05:22
And the index map is updating automatically according to the selection and the swipe tool. So you can compare two selected auto photos from different times
05:43
and both the left-hand side and right-hand side, they can be whatever image that exists.
06:08
Okay, will you come here? Because this part is something that I do not know anything about. I can say something about this.
06:22
So this is our admin tool and you can see the selections that you can do for when you're adding the BMS layer with time dimension. You can select like an animation player for a continuous time series. And for this purpose, we created a new mode
06:43
that handles these historical photos. So you will need to, or you can select another layer that is registered to OSCARI as the metadata
07:01
to provide those, well, the vector features that we use to update the user interface that shows the years that have photos. This is OSCARI.
07:21
So this is like a new feature for OSCARI. So this is the admin tool you get with OSCARI. But this, so this is basically a layer listing. We see the old aerial photos here. You can see that it's a time series layer.
07:42
And this part here is the admin UI that you can configure when you have a time dimension. You can select the metadata layer for it that will be used to generate those dots on the timeline based on the map view port
08:07
and the attribute for those vector features that will be used for what property is the year on the data.
08:20
And then you can select if on which level you want to query the vector features or at what zoom level you stop using it because there's too much features if you look from like the whole country. So it will just show the dimensions on the VMS layer.
08:47
Let's see what comes next. I haven't seen these slides either before this.
09:09
Yeah, we hit the main news and our user, daily users went like, I don't know, 50 times more than normal. So we had to bump up some new servers
09:22
and yeah, basically double the server capacity to handle all those new users.
09:47
Yes, of course. We are also like the core OSCARI developer, our main contributor team at National Land Survey. So yeah, we just made it available in OSCARI for everyone.
10:11
Before that, I can tell something about the technique and data. The images are GeoTIFF images.
10:21
We had a map server used already. So what we had to do was to generate accurate footprints because in our tile index before, we used that bounding boxes.
10:41
But in the beginning of the, you see that there are huge no data areas. In these historical auto photos. So we had to generate accurate footprint polygons and we used that for that certain open source script
11:04
or program made in University of Alaska. Right now, I do not remember the name. And then we have the tile index in PostGIS and it's updated daily because it's the same stories
11:25
for auto photos that we are using every day. New production and also they, all the time they are creating new historical auto photos. We put them all in the same target because we think that if an image is from yesterday,
11:40
it's more like historical. And yes, and for the performance reasons, we had to make a little bit tricks on the database because map server is, it is stupid with rendering.
12:02
If users select a wide time range and it finds, for example, seven auto photos in a pile, map server just starts rendering for the oldest until the newest one. And actually all the newest pixels are meaningful.
12:22
And map server did not handle this case. So we decided to handle it in the database. We have triggers that analyze the coverage of the auto photos and putting an end date for each auto photo file.
12:40
So what was the last valid date for that image? After that, we have something newer. And that's something that I was dealing with.
13:27
Thank you. I might have missed that in the beginning, but I was wondering if the paper pictures that you had, how you geo-referenced them? Because like at some point you need to say, this is a picture of, I don't know, an area outside Helsinki
13:41
or with the exact boundaries of the picture, right? And was that a manual process or was it like an automated scanning process? Sorry, I do not really know how they are producing these auto rectified images, but I believe that they use the same software
14:01
than they are using in a normal production. And I also, at least part of the images are not actually on paper, but on negative films. Yeah, Jukka. So this is a new feature in OSCRI.
14:24
How configurable is it for ordinary, well, for other author photo group, other services? So, I mean, I was just thinking, your solution here, you've tailored your services to access the data correctly.
14:42
Is that something that other people who have author photo services, they would need to do something similar? Or will it just work out of the box? Yeah, sorry. My colleague is busy.
15:04
So the VMST layer is, you can just use whatever you have. Obviously the tricky one is the metadata layer. But you can use, so it's basically querying OTC API features,
15:25
API now, and you can configure the connection parameters and the year or the property attribute for the feature. So it's, you can use it. We could document better how to use it,
15:43
but obviously you're interested. Yeah, please contact us. It looks really good. Well, actually the end result is logically rather close to stack because we are getting metadata for its granule or its author photo from the JSON data
16:04
from OTC API features service. The idea is pretty close to stack idea. So we are just enhancing web map service with that. I would like to ask, can users download the images as their own geotips
16:24
or is it all online? Like if they wanted to bring it into their own project and also do you have like license issues if people wanted to use it for their own research or private companies wanted to use the images?
16:41
Well, the license part is easy. They are creative commons for attribute only. So it's simple to use. The old historical images and our new images have the same license. But downloading, okay, users can do it. They know how to do it.
17:00
We have a beta service, web coverage service and it's possible that it is open, but there are not so many web coverage service clients generally used. And we are planning to improve these download services
17:22
in the next phase. But now what works pretty well is the browsing part. Just a comment on the outsider. So I'm not working for national answer, right? But you can actually add the layer in Coogee IS and then you can start using.
17:41
And in my personal hobby, I've created some maps to background of our summer cottage from 1950s. Very short question. Thank you. I'm very impressed. I can't wait to go and click myself.
18:00
But a question. Do you have any plans to do the same thing with historical maps? Yes, I have been involved in a project that we have georeferenced about 40,000 historical maps from the 1850s, even from Russian time.
18:27
And the georeferencing is ready, but the service is to show the result for the users is under construction. We are going to have to wrap this up.
18:40
I really want you guys to give these folks a standing ovation or a clap or something for just extra fly by the seat of your pants.