Open Source for Education and Projects Key Aspect of Choosing FOSS4G
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 |
| |
| Alternative Title |
| |
| Title of Series | ||
| Number of Parts | 295 | |
| Author | ||
| Contributors | ||
| License | CC Attribution 3.0 Germany: 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 | 10.5446/43375 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
| |
| Keywords |
FOSS4G Bucharest 2019208 / 295
15
20
28
32
37
38
39
40
41
42
43
44
46
48
52
54
57
69
72
75
83
85
87
88
101
103
105
106
108
111
114
119
122
123
126
129
130
131
132
137
139
140
141
142
143
144
147
148
149
155
157
159
163
166
170
171
179
189
191
192
193
194
195
196
197
202
207
212
213
214
215
216
231
235
251
252
263
287
00:00
Universe (mathematics)Power (physics)Presentation of a groupOpen sourceBitData storage deviceIdeal (ethics)Lecture/Conference
00:24
GeomaticsForschungszentrum RossendorfOpen sourceIntegrated development environmentPoint (geometry)Grass (card game)BitOpen sourceGrass (card game)WordHypothesisMultiplication signOpen setProcess (computing)Musical ensembleSound effectSoftwareDistribution (mathematics)NeuroinformatikPoint (geometry)Expert systemType theoryArithmetic meanFreewareCompilation albumCurveError messageBendingActive contour modelNetwork topologySource codeShared memoryView (database)40 (number)Exception handlingComputer animation
03:58
Grass (card game)Digital filterView (database)Mathematical analysisSoftwareRange (statistics)ForestPoint cloudPairwise comparisonLibrary (computing)Point (geometry)CASE <Informatik>Personal digital assistantElectronic mailing listMachine learningMultiplication signGoodness of fitOpen sourceForcing (mathematics)Point cloudStudent's t-testUniverse (mathematics)Plug-in (computing)Slide ruleLine (geometry)Process (computing)Event horizonMatrix (mathematics)SoftwareTorusMereologyPoint (geometry)Software developerCurveGraphical user interfaceWebsiteSelf-organizationExterior algebraValidity (statistics)ImplementationGrass (card game)CodeSystem callObservational studyDataflowDigitizingPhysical systemGoogolNeuroinformatikMathematical morphologyRational numberLattice (order)Server (computing)Position operatorNetwork topologyOnline helpSmoothingDigital photographyComputer clusterComputer programmingQuantumBitLaser scanningRemote procedure callComputer animation
11:57
Range (statistics)Point cloudPoint (geometry)Process (computing)Exterior algebraOpen sourceBelegleserInformationPoint cloudDigital photographySoftwareKey (cryptography)ResultantProjective planePoint (geometry)Computer animation
12:51
Time domainRange (statistics)MultiplicationSpectrum (functional analysis)Vector spaceShape (magazine)Principal component analysisPoint cloudEigenvalues and eigenvectorsDemosceneVarianceAnisotropyVertical directionNeighbourhood (graph theory)Mathematical optimizationScatteringPlanar graphMathematicsCurvatureView (database)Electronic visual displayPoint (geometry)StapeldateiStatisticsAxonometric projectionDistanceLevel (video gaming)VolumePopulation densityRadiusLocal ringGeometryImage registrationBounded variationSurfaceNumberNormal (geometry)Bit rateArithmetic meanWindowEntropie <Informationstheorie>MathematicsGeometryInformationStudent's t-testPoint (geometry)Point cloudPairwise comparisonRevision controlMultiplication signProcess (computing)Open sourceSoftwareComputer clusterMetric systemNeuroinformatikShape (magazine)CodeNatural numberForestMachine learningComputer animation
14:46
Planar graphRange (statistics)Point (geometry)ForestRandom numberPoint cloudoutputServer (computing)Pairwise comparisonInformationMachine learningForestDecision tree learningMultiplication signVirtual machineStudent's t-testComputer animation
15:12
DampingSimulated annealingAreaRankingMusical ensembleUniqueness quantificationDrill commandsComputer chessPredictabilityForestStudent's t-testUniverse (mathematics)Wave packetBit ratePoint (geometry)Line (geometry)Metric systemGoodness of fitVirtual machineNeuroinformatikPoint cloudCodeFunction (mathematics)Matrix (mathematics)Position operatorMereologyDefault (computer science)Computer fontRandomizationAlgorithmComputer animation
16:41
Range (statistics)Point (geometry)ForestRandom numberPoint cloudPairwise comparisonSoftwareOpen sourceMultiplication signType theoryStudent's t-testConnected spaceSingle-precision floating-point formatService (economics)InternetworkingLecture/Conference
17:13
Server (computing)Pairwise comparisonPoint (geometry)Range (statistics)Machine learningForestRandom numberPoint cloudVariable (mathematics)CurvatureUsabilityDivisorMassFood energyOpen sourceFood energyHypothesisConnected spaceInternetworkingSet (mathematics)CASE <Informatik>Data miningSoftwareLevel (video gaming)Open setMultiplication signTorusTouch typingComputer animation
19:00
Multiplication signWordType theorySoftwareUniverse (mathematics)FeedbackPressureStudent's t-testPhysical systemSystem administratorOpen sourceView (database)Connected spaceBitNeuroinformatikLecture/Conference
Transcript: English(auto-generated)
00:07
OK, my presentation, well, first of all, this is me. I work at University of Padua. I teach. I'm a professor there. I do research and teaching. And basically, since I met the open source community,
00:23
I decided to move all my didactics towards open source. So I will be talking a bit about my experience. And I will touch on three key points. I want to talk to you about my personal story,
00:40
not because it's interesting, but because I think it reflects a lot of what I hear from other people. So it's interesting to see how you come to use open source GIS and the spatial software and how that becomes like a domino effect on other people using it. So I will talk about that.
01:02
And then I will talk about how I use it in my job. So that's research and teaching, of course, with some examples. And lastly, just share some ideas with you and see if we can get some question and answer session. Now, for those of you that work with open source,
01:24
probably you remember the first time you met, you understood what open source was. And that, for me, goes back quite some time. And if you are in about my age, between your 40s and 50s, maybe these words recall a song.
01:43
And for those of you who are younger or were not into that kind of music, of course, probably this has no meaning for you. But basically, it's like a love relationship, because you suddenly realize that this type of software opens a lot of doors. But it was not easy.
02:00
So what the phrases mean, wasted days and sleepless nights, because when I met, the first time I understood, I met open source grass GIS. And I started saying, OK, easy, let's use it. But then I said, oh, to use it, you have to install Linux. What is Linux? And then you say, OK, let's compile it.
02:22
But that was not as easy as it sounds. So the learning curve is very steep at those times. But when you basically say that you're waiting for a compilation to go correctly without any errors, when you're not an expert on compiling source code,
02:44
et cetera, it was sleepless nights. You stayed awake and hoped that grass compiled, because you really wanted to see it working on your computer with your Linux distribution. So I don't know if you guessed the song, but it's basically a song by Whitesnake.
03:04
But it depends if you're into that kind of music. So how did it work out? Basically, I still remember I was in a conference like this. And I didn't know anything about open source. I knew a little bit about GIS.
03:21
I used ArcView at that time. And my professor that I was doing my thesis with, we were walking together, and there was a stand with grass. And I asked him, what is this software? And he said, it's free software, and it's also open. You can use it to implement your own whatever you want.
03:40
So my eyes started becoming bigger and said, you mean I can actually download it and use it completely for free? And that's when it all started. And I started reading about it in blogs, in meaning lists, downloading the source code, compiling, et cetera. So that's when it all started. Secondly is the grass user meeting in Trento,
04:04
again, some time ago. And for me, those guys were like Superman, because they really knew how to use the software. But it was also a fun conference. The other conferences I go to sometimes are a bit more,
04:20
I don't want to say stock up people, but it was much nicer to be in a more embracing community of people who really wanted things to work and were not just there to make publications or to go forward with their career. We had nice beers. You know what I'm talking about, because you're here.
04:41
So that's, again, a push forward for me to, again, not only look at grass GIS, but on Kujis, which was just starting to be born, Quantum GIS. And then there's a turning point. The other turning point was programming. So the fact that I was able at the beginning
05:02
to compile after sleepless nights, to compile grass and make it work, got me really curious about how far I can push customization. So that brought me to programming, which then, of course, helped me very much in my research and is still doing so now.
05:23
And so that's why I put this, for those of you who like Matrix, that's the way you choose to actually start and use the red pill, because then you start learning how to program. And then you spend a lot of time to implement your own code. And the first time I tried, my very first publication
05:43
was about implementation of the canny filter in grass very long time ago. And that's how I learned CCIS. And then, of course, when I started teaching, I really had this feeling that because open source helped
06:00
me so much to develop myself into learning how to program, then I said, I don't want my students to learn something which works well. But when they graduate, they have to, exactly what Maria was talking about this morning, they have to spend money, invest money.
06:21
Maybe they just graduated. They want to open a company or be a freelance. And they're stuck with having to buy a software when there's very valid alternatives. That's why I started asking my department to install CoGIS in the computers in the university.
06:42
And that's the face of the informatics guy when he meets me, because I asked him to actually install something which was quite different from what he was used to. And I really insisted on that, because at that time, open source was still not so easily installed
07:01
and not everything went smoothly. And that's why I was not so well-liked. Well, not well-liked, but it was extra work for the department. And that's not always seen positively. So the end of the story is all this process
07:22
of using FOSS4G was an education process that now brought me to use open source for 99% of my work. So I think that's quite a common story. It's not something you never heard about.
07:45
You probably hear it from most people who use FOSS4G. The thing that is interesting is that there's so many open doors that sometimes they make you develop sometimes software. Like this is a software I was studying in laser scanning.
08:03
And it looked really nice. So I used a lot of my time to actually develop a graphical user interface that I never used again. And so that's probably the only, not negative, but the only thing you have to be careful about, not to be sucked into the many possibilities of what you
08:25
can do with open source software. And so regarding the teaching, now it's quite normal, as you can imagine, that instead of using QGIS for teaching and other for higher education
08:43
like PhD courses and post-doctoral courses, there is R, which you probably all know about. And I use a lot of Cloud Compare because I do a lot of research in laser scanning and point clouds. And I will talk actually mostly about the summer school
09:01
that I co-organized with some colleagues from Austria and Germany. Because again, most of the software there that is used is open source. And the last one is just a very recent event that we had in Italy, which made me look into the quite popular as a snap to process synthetic aperture radar
09:25
imagery. So basically, the bottom line of this slide is there is really tools for anything you might need. And there is no reason why not to use them. Courses in the University of Padua, just go there quite quickly.
09:42
Remote sensing, there's a popular plugin by Luca Conjedo, which is semi-automatic classification plugin. I use a lot of shared tutorials by this guy from India who made a really nice website with tutorials that I can share with students. And this made me understand that documentation that you
10:01
find is very important because the more documentation, the more people read it and then learn how to use the software with this less steep learning curve. But another thing that you can actually see in this slide is that a lot of success stories are single people.
10:22
If you see, I just mentioned two, but there's so many, it's very hard to touch all of them. But a lot of things are done by single people who are very proactive in what they do. And that's quite important for the development
10:40
of open source software. Summer school is basically what I want to share about. This is a summer school that comes every two years in a place called Obergurgel, which is not just a very nice mountain place, but has a lot of, let's say, problems with hydrogeological risk.
11:01
So debris flow, falling rocks, a lot of erosion. And it's also by the tree line. So there's a lot of study going on there by University of Innsbruck about the processes of ecological and also geomorphological processes. But regarding open source, what's going on?
11:23
The assignments, there's different assignments in the summer school. And the assignments that I was working on was basically grab a drone plus terrestrial imagery and through photogrammetry create a point cloud. Then from the points we extract
11:40
features that are then used for classification, because we want to see each point as if it belongs to bare ground or grass or snow. Now each of these steps can see an open source software. The only thing that, let's say, was a bit problematic for me
12:01
at the beginning was that for photogrammetry, photo scan is still the quick and dirty software, which is not open source. So there is alternatives now, because again, a key aspect in education is that also the open source community has very fast-paced projects.
12:22
So there is now alternatives to photo scan, which I did not use because I had to learn them myself how to use them before substituting it. But probably in the next edition of the summer school, we will substitute photo scan and we will have a full process with open source software.
12:44
This is just a result of the point cloud with also near infrared information. I will not go into details, but you can talk to me if you want later for more of those details. Then the next challenge was from the point clouds,
13:01
we have information about color, we have infrared information, but we want also information about shape. So what I did, this is a way, it's the mathematical concept of extracting features which describe shape. And there's a lot of these features that you can extract. The problem was that I did not have an open source
13:25
software that extracted these indices. So actually, I developed the process in R for the students. But R is very slow. So to process about half a million points,
13:41
it took almost a whole night. Because I will not go into details, but this needs neighbors. So the half million points, each point had to look at the neighbors, between 10 and 50 neighbors. So that took a very long time. But surprise, when I was in the summer school,
14:00
I downloaded the new version of Cloud Compare. And I was just browsing to see how the change changes. And big surprise for me, I saw this voice said compute geometric features. You know, and I was almost, a tear came in my eye, because I saw that all of those features,
14:20
most of these features were available. And you can just process the point cloud from Cloud Compare. Took about 30 minutes. So I was very jumping out of my chair and telling the students to forget my R code, because it would have taken way, way too long. And they used Cloud Compare also
14:43
for extracting these features, which then go to this random forest. It's a machine learning classification and regression tree method that allows to use these features. And let's see how much time I have.
15:00
Basically, what I gave the students was a hands-on tutorial using RStudio, which we might also be able to see really quickly. It's just a few lines of code. This is RStudio. It's in my computer back in the university.
15:21
And with about 50 lines, I took the students through all the steps for applying the machine learning random forest algorithm to their points. And so I was teaching them the final steps for classification.
15:42
Now, the font is very small, so I will not pretend that you will be reading it, but basically, reading the points and this part trains the data. And after training, there's a prediction step.
16:00
I cannot read it myself, but trust me, it's there. There's a prediction step. And after the prediction, you can just apply. You can export the classified points. And you can open in Cloud Compare the classified point cloud and visually see the classified points.
16:25
And last in line, of course, there is some steps for accuracy metrics, which you see down there. There's true positives, false positive rate, and there's kappa index, and there's many other. There's confusion matrix for the classification output
16:42
that you can extract to see how well your classification went. So open source software allowed me to give this type of service, you could say, to the students participating to the summer school without putting a single penny in software, just putting my own time in preparing,
17:03
of course, these tutorials. Now, I think my time is more or less OK, but I can just finish up. This was back up in case the internet connection didn't work.
17:20
So you just, this is what you saw before live. And final thoughts, basically, is I think teaching is very important because it fosters people to use open source in JS, but also you sometimes find people who start to fall in love with what
17:41
the possibilities are of using this software. So I always look, for example, for people who are so enthusiastic about what they learn about open source that they can do their thesis with me or they start doing a PhD because those are like gold.
18:03
It's like a gold mine because then those people, the enthusiasm grows, and then they become themselves contributors to FOSS. So that's very important. And I saw in, I touch with my own hands the importance of shared tutorials and data sets because that makes things easier for people to teach.
18:22
Being lazy, you don't want to reinvent the wheel, so if there's already good tutorials out there, that helps a lot. And I end up with saying a big thank you to people who are much more proactive than me into promoting time and energy. I was in Geospatial Week in Anske in Holland some weeks ago.
18:41
And again, there was people doing tutorials in OpenStreetMap. You know them probably, Maria and the other ladies. So I think we have to thank them also for pushing knowledge forward. Thank you. Any questions?
19:02
Question time. Plus five minutes for answers. I'm curious to know if you experience any pushback from your colleagues in converting all of your course material over to FOSS.
19:24
That's a good question because at the beginning, the ArcView, now it was ArcView, was very much rooted in the system. And in University of Padua, we also have a campus license. So actually, of course, the university pays very little for the whole license. So they're doing a lot of marketing,
19:42
which is normal, they're a company. And when I started using Kuji as the main, let's say, obstacle was of course from the informatics guys, because they had to do extra work. A little bit also from the people that, like after students graduate,
20:01
we have a lot of connections with companies because we get feedback if what we teach the students helps the companies or not. Now ArcGIS was very much used in companies. So they said that they would have preferred students to know ArcGIS. But our answer was that we do not teach
20:21
where the buttons are. We just teach what's behind GIS. So we, and we did not want to be, how do you say, lobbied into using one or another software. But no, we did not have absolutely any type of other pressure.
20:44
Hi, my name is Jonne Bos. I'm from Nieuwlandt Reioinformasi, from the Netherlands. And I teach, but it's not a university. So we really like to have all our courses only in QGIS and stuff.
21:03
But we have, yeah, our students are not from universities, just people who work somewhere and want to buy a course and how to reach people from companies and from municipalities and things
21:22
with your open source courses instead of your ArcGIS courses, which they know. If you have a company and you have bought ArcGIS, you obviously want a course on ArcGIS.
21:42
So the thing is, education starts from university because the people who graduate, they will be used to QGIS. And now in Italy, I see a lot of public administration, they are just changing. It took a while, but now it's a downhill process. But before it was very, there was a big obstacle because it's very, very costly to public administration
22:03
and also companies to get rid of a software, do, how do you say, again, teach again all the people who use that type of software. And it's also a risk because they don't know, like us, how well QGIS works. So they can just take our word for it. But I think that it was a problem some time ago
22:24
and now it should get better because the more people use open source, the more other people want to use it because they see it works. So it's, but I understand that professionals, of course, it's hard.