Community building

In my last post I wrote about open approaches for Earth Observation. Open (science/source/whatever) means community. I am in the middle of the reading of "The Art of Community" and it is very inspiring. In some ways, it reminds me of very much of Chapter 4 "Social and Political Infrastructure" of Karl Fogel's "Producing Open Source Software - How to Run a Successful Free Software Project", although Bacon's book is more general.

Anyway, both books give very good insight on the issues and tricks involved in a community-based project and even the particular case of projects which are created and managed by companies.

This is an interesting point, since one could think that a project funded by a company has more chances to succeed than a pure volunteer-based one, but many real examples show that this is not the case. The main pitfalls of a corporate open source project are related to decision making and communication. For a project to succeed, the community has to be respected and open discussions and meritocracy are the 2 pillars of community.

Examples of this are the fact that all decisions involving the development have to be explained and discussed in a way that all developers are involved. Examples of closed discussions at the company level leaving out the volunteer contributors are given.

Another interesting example is the one given by Fogel about meritocracy: any developer should earn the write access to the source repository by proving his value. Usually, in corporate environments, developers have commit access from the first day, while external contributors, which usually are much more capable, have to wait long time before that.

An important player in any open source project is the Benevolent Dictator (BD). I will cite Fogel verbatim here:

"It is common for the benevolent dictator to be a founder of the project, but this is more a correlation than a cause. The sorts of qualities that make one able to successfully start a project—technical competence, ability to persuade other people to join, etc.—are exactly the qualities any BD would need. And of course, founders start out with a sort of automatic seniority, which can often be enough to make benevolent dictatorship appear the path of least resistance for all concerned.

Remember that the potential to fork goes both ways. A BD can fork a project just as easily as anyone else, and some have occasionally done so, when they felt that the direction they wanted to take the project was different from where the majority of other developers wanted to go. Because of forkability, it does not matter whether the benevolent dictator has root (system administrator privileges) on the project's main servers or not. People sometimes talk of server control as though it were the ultimate source of power in a project, but in fact it is irrelevant. The ability to add or remove people's commit passwords on one particular server affects only the copy of the project that resides on that server. Prolonged abuse of that power, whether by the BD or someone else, would simply lead to development moving to a different server.

Whether your project should have a benevolent dictator, or would run better with some less centralized system, largely depends on who is available to fill the role. As a general rule, if it's simply obvious to everyone who should be the BD, then that's the way to go. But if no candidate for BD is immediately obvious, then the project should probably use a decentralized decision-making process ..."

And I will end this post by using the opening quote of Bacon's chapter 9:

"The people to fear are not those who disagree with you, but

those who disagree with you and are too cowardly to let you

know."

—Napoléon Bonaparte

Food for thought.

Open Remote Sensing

Right after the recent Haiti earthquake a community of volunteers put their efforts together in order to build up to date maps of the area in order to help the humanitarian aid on the terrain.

This community is the one who is contributing to Open Street Map (OSM), the wiki-like world wide map on the Internet. A detailed presentation about this activity can be watched here.

This kind of activity can only be carried out if the volunteers have the input data (images, gps tracks) and the appropriate tools (software).

In terms of software, the OSM people rely on open source state of the art tools which allow them to be quick and efficient. As far as I know, their infrastructure is based on OSGEO tools for the storage, the formats, the web services for edition and visualization, and so on.

So open people with open tools generating open maps is something which has been proven useful and efficient for a while now: just take a look at the status of OSM near where you live in order to convince yourself. This is another example of the Wisdom of Crowds phenomenon.

However, one thing which was really special in the Haiti case is the speed at which the maps where created after the earthquake. This may be surprising when one thinks that most of the work for OSM is based on gps tracks submitted by people which are on the terrain or by volunteers digitizing somewhat outdated aerial imagery.

In the Haiti case, what really allowed for a quick cartographic response was that space agencies and satellite image providers made freely available the images acquired not long before and after the event. This allowed for really accurate maps of the communication infrastructures and buildings as well as a damage assessment of those. Refugee camps could also be easily located.

Unfortunately, the availability of this kind of data is not usual. Even with initiatives as the Disasters Charter the images made available by the signing parties and the added value maps are distributed under licenses which are relatively restrictive.

And as one can easily understand, the open people using open tools to create open maps are completely useless if they don't have the images to work with. Many people will agree on the fact that this should change when lives are at stake. Other people will think that, even for general purpose cartography, data should be freely available. Of course, space agencies, the aerospace industries, etc. have hard (economical and industrial) constraints which don't allow them to give away expensive images.

And of course, a bunch of volunteers don't have the resources to put satellites in orbit and acquire images. One thing is writing free software and another thing is rocket science! Or is it?

It seems that a group of people are thinking about doing things not far from that.

The Open Aerial Map project aims at building a world wide cover with free aerial images. Even if the project has been dead for a while, it seems to be active again.

Another interesting initiative is the Paparazzi project which aims at building open source software and hardware for aerial unmaned vehicles. One of the projects using it is made by the University of Stuttgart and aims at developing a system for high resolution aerial imagery.

The list of interesting free projects which could be used to setup an open source global nearly real time mapping system is long. It is likely that all the bricks will be put together someday by volunteers.

If this comes to reality, I suggest just one restriction on the license: the user wanting to print the map will have to read Borges' "Universal History of Infamy" before choosing the scale.

Short revisit cycle and temporal sampling

There is a somewhat recent trend in the development of new remote sensing satellite systems toward short revisit cycle (that is a high temporal resolution). Future systems such as Venµs or the Sentinels will be providing images of a given site every 2 to 5 days.

One may wonder which real applications may need this high temporal resolution. Although there are some applications which need it, it is also true that most of the applications foreseen for these systems will work with fewer images per period of time.

However, there are several point to take into account when we think about temporal sampling. First of all, at least for optical systems, the cloud cover can make unusable a high percentage of the images which are acquired by the sensors. Second, even if the mean number of images per year for a given application may be low, some of the applications may need a higher temporal resolution during some periods of the year (example of the phenology of crops). And the fact is that flight mechanics make easier to keep the same orbit all over the life span of a satellite.

And last, but not least, many new applications can emerge when this kind of data is made available to scientists.

In the coming versions of the Orfeo Toolbox, specific tools for image time series will be made available in order to help the users of this kind of data to use them efficiently.

Open tools for modeling and simulation

I has been now nearly 2 months since I moved to a new job. My research subject has slightly evolved, from sub-meter resolution image information extraction (mainly object recognition for scene interpretation) towards high temporal resolution for environment applications. The change in the resolution dimension (from spatial to temporal) is the main re-orientation of the work, but if I have to be honest, this would not be challenging enough to justify leaving what I was doing before, the nice colleagues I had (though the new ones are also very nice), etc.

The main challenge of the new job is to dive in the world of physics and modelling. Although I did a lot of physical modelling and simulation in my PhD, this was 10 years ago and it was SAR over the ocean, while now it is mainly optical over continental surfaces.

I have the chance to have landed on a lab with people who are specialists of these topics, so I can ask lots of questions about very specific issues. The problem is that I am lacking basic knowledge about state of the art approaches and I don't like to bother my new colleagues (since they are nice!) with stupid questions.

There is where open available ressources are very helpful. I will just cite 2 pointers among the lots of relevant stuff I am using for my learning.

In terms of modelling the physical and biological processes in an agricultural field, I found Daisy, which is an open source simulation system developed by Søren Hansen's team at the University of Copenhagen. Added to the source code, there is a very rich theoretical documentation available.

Once these processes are understood and simulated, I was also interested in learning how things can be measured by optical sensors. I found Stephane Jacquemoud's PROSAIL approach for which source code and reference documentation (papers, PhD dissertations) are available online.

From there, I just put things together in order to learn with a hands on approach. PROSAIL is Fortran and Daisy is C++. I wanted to plot some simulated vegetation spectra. So I fired up Emacs and started writing some python code in order to loop over variable ranges, launch the simulators, plot things with Gnuplot.py, and so on. And then, I remembered that we have python wrappers for OTB, which would make possible the use of the 6S radiative transfer code using OTB's internal version of it.

And here we are, with a fully open source system which allows to do pysics-based remote sensing. Isn't that cool?

I has been now nearly 2 months since I moved to a new job. My research subject has slightly evolved, from sub-meter resolution image information extraction (mainly object recognition for scene interpretation) towards high temporal resolution for environment applications. The change in the resolution dimension (from spatial to temporal) is the main re-orientation of the work, but if I have to be honest, this would not be challenging enough to justify leaving what I was doing before, the nice colleagues I had (though the new ones are also very nice), etc.

The main challenge of the new job is to dive in the world of physics and modelling. Although I did a lot of physical modelling and simulation in my PhD, this was 10 years ago and it was SAR over the ocean, while now it is mainly optical over continental surfaces.

I have the chance to have landed on a lab with people who are specialists of these topics, so I can ask lots of questions about very specific issues. The problem is that I am lacking basic knowledge about state of the art approaches and I don't like to bother my new colleagues (since they are nice!) with stupid questions.

There is where open available ressources are very helpful. I will just cite 2 pointers among the lots of relevant stuff I am using for my learning.

In terms of modelling the physical and biological processes in an agricultural field, I found Daisy, which is an open source simulation system developed by Søren Hansen's team at the University of Copenhagen. Added to the source code, there is a very rich theoretical documentation available.

Once these processes are understood and simulated, I was also interested in learning how things can be measured by optical sensors. I found Stephane Jacquemoud's PROSAIL approach for which source code and reference documentation (papers, PhD dissertations) are available online.

From there, I just put things together in order to learn with a hands on approach. PROSAIL is Fortran and Daisy is C++. I wanted to plot some simulated vegetation spectra. So I fired up Emacs and started writing some python code in order to loop over variable ranges, launch the simulators, plot things with Gnuplot.py, and so on. And then, I remembered that we have python wrappers for OTB, which would make possible the use of the 6S radiative transfer code using OTB's internal version of it.

And here we are, with a fully open source system which allows to do pysics-based remote sensing. Isn't that cool?

Geospatial analysis and Object-Based image analysis

I was searching in the web about the use of PostGIS data bases for object based image analysis (OBIA) and Google sent me to the OTB Wiki to a page that I wrote 6 months ago (first hit for "postgis object based image analysis").

It seems that this is a subject for which no much work is ongoing. Julien Michel will be presenting some results about how to put together OBIA and geospatial analysis (+ active learning and some other cool things) in a workshop in Paris next May.

Changing places

Today is my last day at my current position. On the 1st of March I will be at my new position at CESBIO.

This is not a major change. I keep working for CNES in remote sensing image processing, but I will be more focussed on multi-temporal image series (preparing Venµs and Sentinel-2 data use). The applicative context of all this work will be the Land Use and Cover Change.

I am mainly interested in introducing physical prior knowledge in image analysis techniques.