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Prizren Region Notes

Recently, a FOSS4G event was held in Prizren, in the Balkans. Here are some research resources:

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GPL Four Freedoms from WordPress

great to see the GPL Four Freedoms with this WordPress 6.4x update

wordpress gpl

LANDSAT 9 is Public

Many, many new resources opening with the venerable LANDSAT 9 Project –LINK–

Canadian EO Strategy 2022

The Government of Canada (GC) recently published a broad strategy overview on the topic of satellite Earth Observation. The document -LINK- is public-facing and emphasizes the “three-Rs” of Resourceful Resilient Ready; a companion document not reviewed here, is written from a National Defence (sic) point of view, called Strong Secure Engaged. This pair, Defense and Sustainability, appear frequently in national strategy documents in the age of Climate Change.

This twenty page document, filled with impressive color photos of the world as viewed from above, is indeed a “green” document and contains many themes familiar in the broad sustainability movement and its vocabulary. The phrase Open Data is mentioned several times, but the acronym FAIR, common in academic circles, does not appear. Ironically, this document gives some passing support to the “economic value of open data” yet recently in the United States under the previous Presidency, weather data, EPA publications and most anything to do with Climate was made drastically less public, while engaging private business partnerships, allegedly to make better use of the resources. This Canadian GC document mentioned the European Union several times, but seldom the USA, its geographic neighbor and long-term military partner. Also new to me was the English-only presentation. The few GC documents I had reviewed in the past always had French language prominently along with English.

When remote sensing and Climate are the topic, flooding and sea level are always included, and this document is no exception. Flood and coastline analysis in remote sensing is a specialty, and I won’t be including much on that here. Another obvious aspect of remote sensing in Canada is that Canada is massive, complex and difficult to map for many reasons. If anyone needs remote sensing from satellites, it is Canada. In fact, we are informed that historically, Canada was the third nation in the world to operate satellites, presumably after the USA and USSR, in the early 1960s. Canada is part of the Arctic Council today, where some members are active competitors for natural resources and shipping. In plain talk, remote sensing has been used from the beginning to watch and measure the economic activity of competitors, and that remains a reliable revenue stream for this expensive endeavor to this day. Included here are details on some important and ongoing environmental sensor missions, including ozone layer and greenhouse gas monitoring, now more important than ever.

Glossy diagrams allegedly showing the benefits of alliance between Academic, Industry and Government programs, appeared to be shallow and relatively low-quality contributions, included for the positive message perhaps, but lacking important details and over-simplifying real life activity, effects and reach of these constantly changing partnerships. The document is an overview and is somewhat lacking in hard facts, other than the existence of showcased sensor satellite missions. Like similar green documents from California, a partnership with First Nations is extensively featured in one section, including education and training for traditionally underserved communities.

From the technology side, one might split topics between “Industry Support” and “Health & Safety.” Industry meaning existing economic activity, agriculture, resources flows, effects on jobs and the like, with Health and Safety including government functions like the monitoring of pest-born disease, water quality, and wildfires. None of these topics are new, especially in light of Canada being an early space pioneer, but the rate, quality and handling of satellite-based remote sensing data is new. Over the decades, it has to be said that previous specialized heavyweights such as High Performance Computing (HPC) have been eclipsed and I would say even embarrassed by the sheer capacity of Google, and more recently Amazon Web Services cloud computing. Despite public posturing, it is rumored that even the European Union Space Agency (ESA) itself uses AWS behind the scenes for its cost and performance. Google, AWS and others have routinely implemented Machine Learning systems on data flows for commercial purposes, techniques that remain mysterious and out of reach today even for established and well-connected companies and government. The ill-defined and somedays dubious term Artificial Intelligence appears in many consumer-oriented marketing material and the buzzwords of business plan promotion, but it is safe to say that we are in early days there still.

Wrapping up, it could be said that we are living in an AWS, post-Google world now, with cloud computing backends for exponentially increasing volumes of remote sensing data. Most advanced nations are promoting high tech for competitive reasons, and the Government of Canada presents its case here.

Summer ASPRS in California

H2020 AI and Big Data

Big Data and Artificial Intelligence for Earth Observation (EO)

19-20 November 2020, European Commission workshop

The two-day workshop presented nine research projects on big data and artificial intelligence for Copernicus and Earth Observation, funded under the Horizon 2020 Space Programme.

European Union’s Earth Observation Programme

The results of the five projects selected under the topic ‘EO-2-2017: EO Big Data Shift’.

The workshop introduced three new projects selected under the topic DT-SPACE-25-EO-2020: Big data technologies and Artificial Intelligence for Copernicus. Additionally, there will be one project selected under the topic LC-SPACE-18-EO-2020 Copernicus evolution: Research activities in support of the evolution of the Copernicus services subtopic Copernicus Land Monitoring Service.

Innovative EO solutions that the projects have developed:

* OpenEO   [LINK]
* the CANDELA platform [LINK] [YOUTUBE]
* EO-LEARN PerceptiveSentinel’s Python library [LINK]
* Sentinel Hub [LINK]
* the EOPEN platform [LINK]
* the BETTER Data Pipelines [LINK] [HACK]

New projects:
* CALLISTO mobile/secure Big Data platform
* the DeepCube Platform for analytics and AI
* GEM’s AI-enhanced EO-LEARN framework [TWITTER]
* RapidAI4EO [LINK] improved AI land monitoring applications

YNews user tehCorner writes:

My first job was in a company fully dedicated to milk the European H2020 projects’ cow. Their business model was to offer the lowest budget on a series of projects and then have a bunch of interns like me do all the work on their own with very bad conditions (very few were actually even paid and we didn’t have right for holidays).
At some point we all realized the profit the company was making with us and demanded better conditions, they rejected our claims and we planned to leave all at time they had to deliver the projects and get paid. Nothing got delivered, they didn’t get paid and just went out of business.

GEOS 3.8 Benchmarks

newly minted PostGIS 3 / PostgreSQL 12 / GEOS 3.8 combo

PostgreSQL 12.0 (Ubuntu 12.0-2.pgdg18.04+1) on x86_64-pc-linux-gnu
  Ubuntu linux 4.15 x86_64    i7-2600 CPU @ 3.40GHz
  shared_buffers = 4096MB     work_mem=128MB
PostGIS 3.0.0 r17983;   Proj 4.9.3
database  geom  2D POLYGON  valid,simple,4326   3.1million rows
 * all times in milliseconds, lower is better

--  GEOS 3.7.1  postgresql-12-postgis-3_3.0.0+dfsg-2~exp1.pgdg18.04+1_amd64.deb
ST_IsValid(geom)         22023   21968   21976   21952
ST_PointOnSurface(geom)        53880   53668   53918
ST_Centroid(geom)        4610    4383   4384
ST_Buffer( geom,0.001)      98284   98111
ST_Union( geom, ST_Buffer(geom,0.001))      151677   151452

--  GEOS 3.8.1 r93be2e1d;  RelWithDebInfo
ST_IsValid(geom)         13761   13698   13734   13672
ST_PointOnSurface(geom)        4010   3929  3943
ST_Centroid(geom)        4106    4015   4018
ST_Buffer( geom,0.001)      68152   68387
ST_Union( geom, ST_Buffer(geom,0.001))      109546   109829

note: the graphic here shows only relative time between two runs
of the same operators, not absolute time between operators..

Compared to PostgreSQL 10 / PostGIS 2.4 / GEOS 3.6.2 only two years ago;
big evolution forward on several fronts.

Benchmarks v2 — New Data Sets

* sparcels   450,000 rows, including ~1000 invalids by PostGIS definitions, mostly single ring (190MB)

* cpad19a   72,000 rows with a diverse range of area, vertice count and interior ring count (100MB)

* post_osm_bldgs   25,800 rows of recent OpenStreetmap 2D polygons marked as “building” of some kind

postgresql 10+190ubuntu0.1; postgis 2.5.2+dfsg-1~bionic1
 pg_workers enabled;  4GB shared_mem

        sparcels    cpad   osm  
  3.7.1     5274    5518   311
  3.8.0     2768    3669   210
 3.9dev     2551    1200   172
 3.10.1              800   

PointOnSurface where IsValid	
  3.7.1    21627   11174  1189
  3.8.0     4134    5526   271
 3.9dev     3659    1476   269
 3.10.1             1037   

Centroid where IsValid
  3.7.1     6352    6289   311
  3.8.0     3978    5857   279
 3.9dev     3383    1396   256
 3.10.1             1055   

Buffer where IsValid
  3.7.1    21138   52019  1341
  3.8.0    16711   30226  1097
 3.9dev    15699   27468  1045
 3.10.1            26226   

Union(Buffer) where IsValid
  3.7.1    36636   57707  2155
  3.8.0    27883   35749  1721
 3.9dev    26818   29314  1653
 3.10.1            26226   

* all times in milliseconds; lower is better

GEOS on o13

Geometry Engine Open Source ecosystem overview on
OSGeoLive v13 (o13)   -LINK-   GEOS -LINK-


NAIST Survey on Github dot Com

Hello – I am Brian M Hamlin, a Charter member of OSGeo dot org. “The OSGeo Foundation is a not-for-profit supporting Geospatial Open Source Software development, promotion and education.

First meta-comment : “Dear Free Open Source Developer” … this is not accurate, and badly so … This English means “your software is free (as in beer)” .. our software is not “free as in beer”, it is “free as in Freedoms” . When a large commercial company, with a history of strong actions against Open intellectual property, says “Hey developer of no-money software, why do you do this?” Do you see how this is “framing” the conversation, to show the others in a certain way?

The problem of the name of FOSS is spread across the world.. Freedom is not the same as “no-money” .

Q. Fast forward to October 2019, GitHub has just released the Octoverse 2019, in a blog. They state that “Ten million new developers joined in the last year alone, 44% more created their first repository in 2019 than 2018, and 1.3 million made their very first contribution to open source“. Furthermore, they have new features such as vulnerability alerts, and automated updates and the increased use of pull requests.

As part of a follow-up, we would like to simply ask the following question: After one year, has your perception on Microsoft’s acquisition of GitHub changed?

We encourage you to voice your opinion on this topic to us and assure that your identity is secure (anonymized). You are free to ask for your data to be withdrawn from the study.

Direct Answer to the Survey Question “How has your perception changed over one year, of the Microsoft acquisition of Github”

first, showing growth numbers alone at the top of your question, appears to show bias by you. I am not persuaded by marketing ads about growth of Github, it is an implicit invitation to “jump on the bandwagon” . For reasons listed below in detail, there is a lot of room for mistrust and doubt one year later. Github slogan “Open Source has won” is not the same as “A Microsoft company called Github has non-transparent, profit-motive control of an important platform for Open Source” !! It is trivially obvious ! Github dot com is a difficult trade-off between visibility, ease-of-use, and the misfortune of being manipulated and recorded for the sole advantage and profit of others.

Github dot com comments:

Centralization — the history of social activity across the ages is filled with a contrast between specialization and not, control or cooperation. When agriculture produced surplus, a management class was born. When armies conquered villages, a warlord was made. Human history is filled with examples of cooperative, productive people being invaded by conquerers who wish to control the surplus of others. It is not an exaggeration to say, that in the Information Age, with Internet and TCP/IP, these lessons do apply. Of course in complex systems, there are multiple effects.. common protection,or larger markets… I am not blind to some benefits here, also.. BUT Microsoft Corporation, in its “DNA” is an aggressive, for-profit conqueror, who makes no issue of taking the business of others, by cooperation or other ways.. Ask Google today what they think of Microsoft, today. However, today we discuss the point of view of the AUTHOR of original software.

Individuals are ultimately the source of invention, even in large organizations. In software development since the personal computer, individuals have a unique opportunity to invent and publish. If the individual publishes via the Internet, how do others find the results ? Of course this is challenging, but plurality grows with local effects.. Japanese language authors, special needs like a diabetic patient, detective stories in English .. lots of example where local authors can publish to certain audiences, but also have large groups via markets and communications… Somewhere in this story, the forces change to the scale of society and the world.. especially in military competition, in electronics themselves (since they have no language, only circuits), and “security” which I will not try to describe..

Social-scale competition creates an endless need for collecting the works of others, for advantage. Did you know that Github dot com was hired by the United States Pentagon, to create constant reporting on relevant publications and activity privately ? (more on this kind of thing in a later section).

I show that local publication, or market segment publication, supports diversity of opinion, of cultural expression, and trade opportunity to others, when the products are ready. Individual authors are the origin of invention, even in large settings, and benefit from some mix of localization and markets. Extreme centralization has a negative history across the ages. An endless hunger for the work of others drives some un-balanced market activity, and can be psychologically associated with the role of raiders, plunderers and slave owning in extreme cases, which is obviously profitable. The rules of law and moral drivers put some balance on extreme aggressive activity, over time.

Vendor Lock-In with Features — In the normal course of teamwork on Open software, especially software with a long life, the tickets, comments and collaboration features become very important. Microsoft has a long history of using features on top of standards to lock in customers in a “soft” way. Github shows all of the qualities to fit this kind of strategy. Hundreds of Linux software developers over time, led by Linus Torvalds, wrote the difficult and precise GIT software, but it is hard to use. Github dot com adds a user interface, and facilitates common GIT patterns via a good-looking, straightforward web interface. The value-add to GIT is obvious, which contributed strongly to success at Github. Microsoft paid to own the right to control the feature set development, and has shown over decades that they use this control for vendor lock-in.

Transparency — Computer systems have a unique capacity to make lasting records of vast amounts of information, report on that information, and transmit that information. “Data is the new Oil” is a saying that has been repeated in the recent Internet times. When individuals and teams make progress on their challenging new technology, their time and effort is in the invention. Yet a common computer system can record the efforts and results of the activity quietly, report on it, and transmit it, without the knowledge of the users. It is practically a “one-way mirror”. Operators of the common system can see ALL the activity of the users, yet users may not see the activity of others. Who owns this reporting capacity ? What restrictions are in place for privacy ? What kinds of records are kept, on which teams ? We are in new territory. Ask the Board of Directors of a Stock Market, if there is advantage to reporting on the sum total of all trading in their system. It insults the intelligence of authors, to suggest that smart people cannot know that there is value being created by reporting on their activity. There is no gurantee of fairness and rights without oversight. Microsoft Corporation was repeatedly convicted of unfair trade practices, while the founder Bill Gates displayed his wealth for more than twenty years. Despite promises, ads and financial contributions to Universities, there is no way to know what is being done without Transparency. Since it is effectively not possible to know all the parts of such a large and active system, it is very, very difficult to see fairness over time with a centralized, opaque system owned by a competitive corporation.

Records on Others as a Profit Source — if Data is the New Oil, then selling that data is obvious. As in the example above, Github earned contract money from the USA Pentagon by creating high-level, consistent reporting privately for their wealthy, competitive client. Why does the individual and team put their core content on a common site, and derive no profit (money) from the sale of reports on it ? The distribution of profits in FOSS eco-system is deeply debated right now. It is obvious that many lack the de-facto ability to collect money from their software, although every author needs to sleep somewhere, and eat food. I am writing to you from the San Francisco Bay Area, which is heavily impacted by unequal distribution of money.

Records on Others as a Surveillance Source — common security is a balance, and a moving target. From the point of view of law-enforcement, there is no end to the details they may want (“need”) on the activity of individuals or teams, over time. But the rule of law (supposedly) sets a balance between this security record keeping, and the actions of an individual as they choose. Moving to budgets, in fact, in a large society, security is constantly funded, while authorship is sporadic and un-predictable. Over time, constant budgets have a survival benefit, while authors may starve or take forced choices during low productivity. Very large companies are attracted to constant revenue over sporadic invention. This creates “perverse incentives” in markets to create new products for security, instead of supporting invention by authors.

Psychologically and behaviorally, a predator mode is part of human nature, and has a rough history associated with it. Surveillance of the activity of others, routinely exceeds rational need in nations around the world. Add to this the “other” of activity of a different group, tribe, ideology, market or similar, and the drives to create surveillance quickly escalate. Like other social expressions, the ones who really are purposefully dangerous, are much more difficult to detect and monitor, than those who are expressing healthy rebellion, trying new things, or learning by acting out in some developmental way.

I have shown that there is an extreme and unhealthy tendency to create surveillance records for sale to constantly funded security systems in a large society. Github can be used in this way, sometimes beyond the real bounds of software development.

Motivations Matter — Just as a corporation seeking profitable control is seen as “natural” , so the drive of authors to invent, express, and solve problems is also natural. The motivation of a company the size of Microsoft, with a system as active as Github, is not possible to summarize in single sentences. However, it is safe to say that the motivations of authors tend to problem solving for profit and also for non-profit, while the motivations of a corporation tend toward capturing and controlling profit in money terms. As a contributor to “Free as in Freedom” software, I will absolutely defend the rights and interests of authors first.

Buzzwords on Advanced Methods


On the OSGeo Japan -discuss mailing list regarding FOSS4G 2019 KOBE.KANSAI :


  translation: this year, there are two courses that match the theme of Geo-AI, where you can experience deep learning from the beginning!

An OSGeo Japan wiki page says:    今年のテーマは「Geo-AI」です。
    translation: this year’s theme is “Geo-AI”.

Geo-AI a closer look – “geo” is easy, what about AI?

a working definition of AI*:
 Artificial Intelligence is the theory and development of computer systems able to perform tasks which previously required human intelligence.  AI is a broad field, of which Machine Learning ML is a sub-category.


Data Science overlaps Artificial Intelligence, Machine Learning exists in that overlap.

When people speak about Machine Learning these days, they are often speaking of Deep Learning, a subset of Artificial Neural Networks NN.. networks consisting of connected, simple processors called “neurons”  wikipedia -LINK-nn -LINK-dl This blog post -LINK- describes NN training methods, including “weakly supervised” and “transfer learning.”

Since 2009, supervised deep NN‘s have won many awards in international pattern recognition competitions, achieving better-than-human results in some limited domains. — Schmidhuber 2015 Survey of Deep Learning

Deep Learning is especially effective on data sets like images and sound, and typically improve with the amount of training data available. Deep Learning is not new, but what is new are the volumes of available labelled training data, and the capacity to compute on them. Many Deep Learning programming libraries are in Python, and many libraries run natively on a GPU. -LINK-sklearn -LINK-pyTorch

Data Science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. wikipedia -LINK-ds

Machine Learning as a subject, is the study and application of algorithms and statistical models that computers use to execute some tasks without explicit instruction; sometimes known as predictive analytics. -LINK-ml

* ai-and-the-global-economy-mark-carney-2018