blog

07.08.2018-13.08.2018

In eigener Sache

• Im Blog von Wikimedia Deutschland steht ein Artikel (von Manfred Reiter) über die Geschichte von OSM und der WeeklyOSM mit einigen Hintergrundinformationen darüber, was es bedeutet, einen Blog mit 12.000 Lesern zu betreiben. Wie bereits berichtet, erscheint die WeeklyOSM jetzt zusätzlich in Suaheli und Koreanisch, was auch dank einer Wikimedia-Foundation-Spende ermöglicht wurde.

Mapping

• Frederik Ramm fragt auf der Mailingliste Tagging, ob man noch Place-Nodes für Kontinente bräuchte.
• Eine Diskussion auf OSM-talk, die ursprünglich darum ging, ob highway=* + area=yes oder eher area:highway=* für das Tagging von Straßenflächen genutzt werden sollte, artete schnell in eine allgemeine Diskussion über Flächenrouting, Verallgemeinerung und die Dokumentation im Wiki aus.
• Auf der Tagging-Mailingliste wies Andy Mabbett auf das Problem hin, wie verschiedensprachige Namen in OSM getrennt werden. Auf der Wiki-Seite für multilinguale Namen werden Leerzeichen, Leerzeichen + Bindestrich, Querstrich sowie Querstrich + Leerzeichen genutzt. Er wünscht sich eine größere Konsistenz.
• Ein Import von historischen County-Grenzen in Großbritannien löst auf der lokalen Mailingliste eine angeregte Diskussion aus. Obwohl es überzeugende Argumente für das Entfernen der Grenzen gibt, sind einige dagegen. Auch der Mapper, der die Daten importiert hat, meldet sich zu Wort und erklärt seine Hintergründe.
• Das Open-Location-Codes-System, mittlerweile auch als Plus Codes bekannt, hat eine lange und hitzige Diskussion auf GitHub und der Mailingliste Talk ausgelöst. Der Hauptstreitpunkt ist die Frage, ob das System, das Angaben von geographischer Länge und Breite in einen Code umwandelt, als Tags in OSM erfasst, clientseitig in Koordinaten zurücktransformiert oder von Geocodern unterstützt werden sollte. Frederik Ramm hat eine längere Antwort auf die Diskussion als FAQ-artigen Blogeintrag verfasst. Christoph Hormann kritisiert in seinem Blog die Aktivitäten von Entwicklungshelfern, den Armen in Form von Plus Codes ein Adressierungssystem zu geben, als „ziemlich zynisch und menschenverachtend“. Ilya Zveryev vertritt (automatische Übersetzung) in seinem Blog eine gegenläufige Position.
• Die koreanische Community hat alle Keys, die ko_rm beinhalten, dem BCP-47-Standard ko-Latn angepasst. Über einen solchen Automated Edit wurde bereits seit 2015 nachgedacht, als Thierry Bézecourt die Idee erstmals auf Talk-ko aufbrachte. In anderen Ländern wie z.B. Japan wird jedoch weiterhin die Variante xx_rm verwendet.
• Die Abstimmung für vorgeschlagene Fluchtwege ist angelaufen. Diese Wege dienen dazu, Menschen zügig vor Naturkatastrophen wie Hurrikanen oder Tsunamis in Sicherheit zu bringen, und werden in der Regel zumindest in den USA mit einem runden blauen Rettungswegschild versehen.
• Direktes Aufrufen eines externen Editors (z.B. JOSM) aus der OSM-Website heraus funktioniert jetzt endlich auch mit dem Microsoft-Browser Edge. Laut einem Tweet wurde dessen sehr defensiver Umgang mit localhost-Diensten endlich gefixt.

Community

• Am 12. August wurde an verschiedenen Orten auf der ganzen Welt der 14. Geburtstag von OpenStreetMap gefeiert.
• Gregory Marler hat die ersten Videos der SotM auf dem SOTM-YouTube-Channel hochgeladen und in dieser Playlist zusammengefasst.Hilfe bei der Transkription und Übersetzung wird noch gesucht. Was zu tun ist, findet Ihr hier.
• Auf der Mailingliste talk-ch wird vorgeschlagen, mit Gewerbe- und Berufsverbänden zusammenzuarbeiten. Die Geschäfte und Berufsgruppen könnten durch entsprechende Vorinformationen ihre eigenen Einträge in der OSM pflegen. Simon Poole schlägt eine bereits vorhandene Schweizer Karte vor, die solche Aktionen unterstützen kann.
• SunCobalt bastelte eine Statistik, getrennt im DACH-Bereich, die eine Rangfolge aktiver Mapper im Erfassen von addr:housenumber erzeugt. Die Gewichtung (1 Punkt pro Node, 5 pro Way, 10 pro Relation) für einen fiktiven Score wird noch diskutiert – wenn auch nicht ganz ernst gemeint.
• BBC Radio stellt ein OSM-Mappingprojekt in Niger vor, auf dessen Basis der Hochwasserschutz verbessert wird.
• Ein Artikel im Bright Magazine veranschaulicht an einem Beispiel die oft willkürliche und unangekündigte Zerstörung von Slums zu Gunsten von Bauprojekten. Was OSM dabei tun kann: Wenn solche Gegenden schon vor der Räumung detailliert gemappt waren, können die davon betroffenen Menschen eher an eine Entschädigung kommen, da das Ausmaß der Maßnahme dadurch nachweisbar wird.
• Der US-Botschafter in Turkmenistan, Allan Mustard (aka apm-wa), wurde von OSM Belgium als Mapper des Monats ausgezeichnet. Allan beschrieb seine größte Errungenschaft mit der Erfassung von Aşgabat in OSM als die umfassendste öffentlich zugängliche Karte in der Hauptstadt Turkmenistans.
• Niklas Weber, ein deutscher Arzt, untersucht eine durch Zecken verursachte Hautkrankheit in Quilcacé, Kolumbien. Er nutzt OpenStreetMap auf Locus-Map intensiv auf seinen Reisen, wie Carlos (aka kaxtillo) in seinem Blogbeitrag berichtet.
• In Eppendorf ist dem vorletztes Jahr beim Wandern in Spanien verunglückten OSM-Benutzer Thomas Bellmann aka malenki ein Gedenkstein gewidmet worden. Dieser Liederstein liegt am Carl-August-Nendel-Weg in Kleinhartmannsdorf in der Nähe seines Geburtshauses. Im Forum wurden Bilder veröffentlicht.
• Die Benutzeroberfläche von Taginfo wurde erstmals in eine von rechts nach links geschriebene Sprache, das Persische, übersetzt. Jochen Topf, der Taginfo-Entwickler, beschreibt in seinem Blog, welche Schwierigkeiten beim Persischen bestehen.

OpenStreetMap Foundation

• Die OpenStreetMap Foundation hat ihren Sitz geändert.
• Die License Working Group hat einen Entwurf für die neuen Nutzungsbedingungen für openstreetmap.org veröffentlicht und bittet um Feedback.
• Das Protokoll der öffentlichen Vorstandssitzung des FOSSGIS e.V. vom 8. August ist veröffentlicht worden.

Veranstaltungen

• Die Geochicas laden alle Frauen zu einer Veranstaltung am Abend vor der FOSS4G Konferenz und dem HOT Summit dieses Jahr in Dar es Salaam ein. Wenn ihr an der Veranstaltung teilnehmen möchtet, registriert euch bitte.
• [1] Am 11. August fand in Tokio die State of the Map Japan 2018 statt. Auf Flickr gibt es Fotos zu sehen.
• Am 9. und 10. November findet auf dem Komaba Research Campus der Universität Tokio die FOSS4G 2018 Tokio statt.

Humanitarian OSM

• Ramani Huria 2.0, ein lokales Community-Mapping-Projekt in Dar es Salaam, berichtet über eine Initiative zur Erfassung des lokalen Entwässerungsnetzes für die Entwicklung von Hochwassermodellen. Als schnell wachsende Küstenstadt ist Dar oft von hohen Wasserständen betroffen. Das Modell sollte dazu beitragen, das Hochwasser und seine Auswirkungen sowie den Einfluss von mangelhafter Infrastruktur und Instandhaltung und Müllablagerungen auf Hochwasserrisiken zu verstehen.
• Ein Beitrag der The Hindu erklärt kurz das Zustandekommen von Missing Maps und berichtet dann von Pete Masters von Medecins Sans Frontieres (MSF). Pete führt Schulungen in Delhi über humanitäres Mapping mit Freiwilligen durch: „Ich bin begeistert, dass ich humanitäre Hilfe leisten kann“. Die neugierigen Freiwilligen sahen es genau so.
• Ein neuer Open-Access-Perspektiven-Artikel von GIScience Heidelberg und dem Roten Kreuz (UK/Deutschland) diskutiert das Missing-Maps-Projekt und dessen Potential für die Rotes-Kreuz- / Roter-Halbmond-Initiative.

Ausbildung/Schulung

• Die Frage, wie man Overpass mit der API-Entwicklungsumgebung Postman abfragen kann, wurde auf GIS StackExchange beantwortet.

Open-Data

• Luftmissionen mit LiDAR-Lasertechnik liefern sehr genaue Höhenmodelle. Martin Isenburg von der rapidlasso GmbH geht mit dem Aufkommen der Lidar-Punktwolken für Estland erneut auf die Frage des Zugangs zu solchen Daten in einem Artikel ein. In früheren Blogeinträgen (1, 2) wurde gefragt, ob es sich aufgrund der Einnahmen von rund 323.000 Pfund pro Jahr zwischen 2007 und 2014 lohnt, den eingeschränkten Zugriff auf solche genauen Höhendaten beizubehalten.
• Auf der ToulouseSpaceShow 2018 kündigte GIScience Heidelberg einen neuen Landnutzungs-Datensatz an, der Daten aus OSMlanduse.org und EU Sentinel 2 kombiniert und zunächst für ganz Europa verfügbar sein soll.

Programme

• Imre Samu zeigte bis zum 20. August einen Taginfo-Prototypen, der Auswertungen nach Regionen ermöglichte. Neben 620 Docker-Instanzen für die verschiedenen Regionen wurden auch zwei spezielle Instanzen für normalisierte Namensunterschiede sowie problematische Namen vorgeführt. Trotz der beeindruckenden Zahl an verschiedenen Taginfo-Instanzen lief der Prototyp auf einer bescheidenen Atom-CPU mit 32GB RAM.
• Wille Marcel kündigt für den 27. August eine DSGVO-kompatible Version von OSMCha an und erklärt in seinem Blogeintrag die Unterschiede.
• lifehacker.com hat den Artikel „Navigate Hiking Trails With Hikepack“ veröffentlicht, ein Review der OSM-basierten, kommerziellen Wander-App für iOS.
• OpenStreetMap US hat eine Ausschreibung zur Neugestaltung der TeachOSM-Website veröffentlicht. Man sollte sich beeilen, um den Zuschlag noch zu bekommen – Stichtag ist 20. August 2018.
• Chip.de hat die Navi-App Live Roads getestet. Die Vorzüge gegenüber dem Platzhirsch von Google sollen in präziseren Ortungsdaten anhand von optimierter Sensorauswertung und dem Feedback von Nutzern zu Verkehrsproblemen liegen. Die App setzt auf Mapbox-Technik und somit OSM-Daten.

Programmierung

• GIScience Heidelberg veröffentlichte ein neues jupyter notebook, das zeigt wie man die OpenRouteService Isochrones API zur Analyse der Erreichbarkeit von Gesundheitseinrichtungen in Madagascar nutzen kann.
• Anusha Singh setzte die Arbeit am Web-basierten Public-Transport-Editor fort. Laut ihrem Benutzer-Blog auf osm.org hat sie viele Verbesserungen wie einen Anfänger- und einen Expertenmodus und interaktive Tutorials zur Verwendung der Web-App ergänzt.
• Florian Schäfer hat sein Wikipedia-Plugin für JOSM im Google Summer of Code verbessert. Das Projekt ist auf Github umgezogen, wurde neu strukturiert, und Wikipedia- sowie Wikidata-Angaben werden nun auf Konsistenz überprüft.
• Srikant Chepuri beschrieb seinen erzielten Fortschritt bei der Weiterentwicklung von GTFS2OSM in seinem Benutzer-Blog. Das Tool nutzt einen GTFS-Feed, um fehlende Elemente des öffentlichen Nahverkehrs in OSM zu ergänzen.
• Matias Dahl, der 2015 einen Blogbeitrag über Veränderungen des amenity-Tags schrieb, hat eine neue Website namens tagdynamics.com erstellt, die einen Einblick in die Veränderungen von OSM-Tags geben soll. Die work-in-progess-Website zeigt, wie Tags, Values oder Tag-Value-Paare wie bspw. building = yes sich mit der Zeit verändert haben. Der Quellcode ist auf GitHub verfügbar.

Releases

• Der noch recht neue OSM-Editor Pic4Review hat laut einem Tweet kürzlich ein größeres Update erhalten. Zu den Änderungen gehören unter anderem die Unterstützung der deutschen Sprache und die überarbeitete Benutzeroberfläche.

Kennst du schon …

• .. die Dissertation von Chris Anderson-Tarver über das Crisis-Mapping nach dem Erdbeben2010 in Haiti?
• … das experimentelle OSM basierte 3D-Tool Little-big-City von Yi Shen?
  Hier als Beispiel der Southwork-Park in London. Durch Eingabe der Koordinaten (Punkt-Trennung!) kann jeder Ausschnitt gewählt werden.
• … DroneRadar? Die Webseite zeigt auf einer OSM Karte, wo in Polen Drohnenflüge erlaubt bzw. verboten sind.
• … die Liste der Server der OpenStreetMap Foundation mit Informationen zur Ausstattung, Verwendung und Standort?

Weitere Themen mit Geo-Bezug

• Im August haben Spanien und Portugal einen Vertrag ratifiziert , der die Flussgrenzen auf dem Miño und Guadiana festlegt.
• Wie CBS News ausführlich berichtet, lässt es sich kaum verhindern, dass Google-Dienste individuelle Bewegungsprofile erstellen, auf Android-Geräten ebenso wie auf iOS. Selbst entsprechende Privatsphäre-Einstellungen werden dabei bewusst ignoriert, mit dem Argument, es nütze dem Anwender doch. Auch deutsche Medien berichten (1, 2).
• Pocketnavigation hat das OSM-basierte Sport-Navi Rox 12.0 von Sigma getestet, einen Konkurrenten des Garmin Edge.
• Eine Petition will die Grenze von Provinz Batman in der Türkei in die Form des „Batman“-Logos ändern.
• Die Trockenheit lässt im Vereinigten Königreich verborgene historische Stätten unter landwirtschaftlichen Nutzflächen auf Luftbildern leichter sichtbar werden.
• Die „Berliner Morgenpost“ hat einen interaktiven Vergleich für Satellitenbilder mehrerer ausgewählter Orte in Deutschland von Anfang Mai und Ende Juli/Anfang August 2018 veröffentlicht, um die optischen Folgen der Trockenheit begreifbarer zu machen. Der deutsche Astronaut Alexander Gerst hat Fotos aus der ISS getwittert, die die Trockenheit in Mitteleuropa zeigen.

Wochenvorschau

Wo	Was	Wann	Land
Mumble	OpenStreetMap Foundation public board meeting	2018-08-16
Rapperswil	10. Micro Mapping Party Rapperswil 2018 (inc. OSM-Treffen)	2018-08-17
Lüneburg	Lüneburger Mappertreffen	2018-08-21
Lübeck	Lübecker Mappertreffen	2018-08-23
Düsseldorf	Stammtisch	2018-08-29
Stuttgart	Stuttgarter Stammtisch	2018-09-05
Bochum	Mappertreffen	2018-09-06

Hinweis:
Wer seinen Termin hier in der Liste sehen möchte, trage ihn in den Kalender ein. Nur Termine, die dort stehen, werden in die Wochennotiz übernommen. Bitte prüfe die Veranstaltung in unserem öffentlichen Kalendertool und korrigiere bitte die Einträge im Kalender, wenn notwendig.

Diese Wochennotiz wurde erstellt von Kateregga1, Nakaner, Polyglot, Rogehm, SK53, Spanholz, SunCobalt, bjoern_m, derFred, doktorpixel14, kreuzschnabel.

This is a followup to my previous question, for which I did receive responses, to help me to develop a better understanding of OpenStreetMap.

I want to post a screenshot saved as a jpeg image to a new diary entry, and attach an explanation of the problem illustrated in the screenshot. Is there any way to do this that doesn't require posting to a third party application, then posting a link in the diary entry to the third party application?

I don't have any third party applications that I can post the jpeg mage to, save Facebook. If I use Facebook, the link allows people to see other parts of my site; which isn't acceptable.

I have a question to pose to the OSM contributors body regarding rendering of natural=wood areas, which I think would be better described with a screenshot of the rendered map.

I realize that OSM is powered by volunteer labour, and I highly appreciate it, I'd just like to find out how to get an answer to my question.

Specifically, some of the areas that I have tagged in the Alberni Valley as natural=wood, show up as dark green on the rendered map; whereas other, adjacent areas, with exactly the same tagging, do not show up as dark green but are essentially blank, nor do the labels attached the unrendered areas appear. If I could post a screen shot, this anomaly would be a lot easier to understand.

This problem has just recently appeared, and through no action of mine, as far as I know. I refer to the rendered map frequently, to view how my edits (using JOSM), appear.

Some people say, don't map for the renderer, but aside from using the rendered map as a means to control my edits, I have numerous times shown the rendered map to others, to help them understand OSM, and perhaps be interested enough to become contributors. There are few people in the Alberni Region of Vancouver Island who are interested enough in contributing to make a concerted effort to upgrade the map, for the benefit of all users, and I would like to encourage others to become contributors. The rendered map is a valuable resource, and has to be considered a the final product.

When people see the extent of the resource extraction roads and other land use detail, current and otherwise, that show on the rendered map they are usually enthusiastic to have more such detail developed, and might possibly become contributors, or provide me with on the ground edits which I can incorporate using JOSM.

I expect there is a large number of technically oriented people, who use a variety of mapping applications, to produce specific maps for specific purposes, with OSM as a base, but that is not my objective. I am trying to develop a map focused on the Alberni Region of Vancouver Island which will help people to understand what the area is like, in terms of land use, and provide them with a guide for navigating, using OSM rendered maps, the many resource based roads in the Region. I expect to be able to simply refer them to the OpenStreetMap website and let them see for themselves the mainly industrial roads and openings in the forest, which define the land use in the Region. Anything more complicated will simply be self-defeating.

Will this work: link to Facebook document?

Wow! the experience our trainees got from us day before yesterday was awesome. The training was coordinated by our well experienced and a professional Mr Sunday Victor who is currently a Cordinator of UNIQUEMAPPERSTEAM, Nigeria. Trainees were trained on how to select a task and carry out mapping of a disaster areas using Task.HOTOSM. The trainees equally learnt some basis of GIS and Remote Sensing during the training which make them even became more excited as the training became a fascinating one. I took the advantage of the training and mapped out some areas prone to disaster and other environmental hazard in Nigeria and my heart was full of joy. Therefore I humbly thank the openstreetmap founder for creating a platform that has become a major tool for geospatial data generation which helps in responding to disaster and Lessing the impacts of it across the world.

Well, on my first day as an editor, I made some mistakes.

It's right there on the wiki, very clearly: Imagery can be wrong. But I didn't though of that when trying to fix this lot of roads which seemed oddly aligned with Bing imagery. I'm sorry.

I think there should be more advice about that on at least ID's walkthrough (a link like the above one maybe). I had no idea that the map could be disaligned with the aerial image.

There's some way to revert a changeset?

I haven't written a diary entry in a while, so this one's like a mash-up of multiple entries ;) Bear with me.

A week or two ago, I set out to survey a bus route that has long intrigued me - the "Outer Mudrika Service" aka "बाहरी मुद्रिका सेवा", or "OMS" for short. Like all "Mudrika" buses, it takes a circular route - but in this case, a big circle of Delhi. As of the 6th of August, I had mapped it as serving 84 stops - the average bus route serves at most 40, and this was not even it's final form...I mean, I'd only mapped around half of it :P

The survey turned out to be more gruelling than I had anticipated. OMS starts and ends at Uttam Nagar, so I headed there (also by OMS, adding stops along the way I had missed in the night the last time). At Uttam Nagar Terminal, though, hardly anyone seemed to know exactly where to board it. I stood around an hour, not daring to sit lest I miss it when it comes (the fact that it's a somewhat low-frequency route didn't help). Figured I'd eat my packed lunch, but the place had an abundance of flies. Twice I walked between the terminal and the first bus stop where I suspected I'd actually find it, almost a kilometer away.

On the second time, at around 16:00 hours, I actually succeeded in boarding one. Wasn't ideal - it would terminate halfway through, at Anand Vihar. Better than nothing, I figured.

Turns out, it was a blessing in disguise. Not only was even half of its route fairly long (particularly when you're hungry and tired from standing so long! [1]), some manner of procession near Burari brought it to a halt at the apex of a flyover for some fifteen minutes. Atleast I got to eat :P By the end of it, my phone was running low on juice and I myself was thirsty and out of water. Called it a day at Anand Vihar and took a Metro back home.

Fortunately, the rest of the route was already mapped, albeit based on sightings and deduction. The result is Delhi's longest mapped bus route, serving over 160 stops - https://www.openstreetmap.org/relation/8062453

Some time ago I went on a trip to Himachal Pradesh. Seeing the state of the map there - particularly the roads - has given me a lot more work to do. I recorded GPS traces for unmapped roads, but I resolved to improve the accuracy of (the very roughly mapped) existing roads first - not realizing how much work that is. Enough to carry on for months :)

(ThorstenE in #osm mentioned spline support for OSM. That would be amazing, and would elegantly reduce all this work by multiple orders of magnitude -_-)

I've counted how many bus stops I've added, and now I happened to wonder how many POIs I've added in general, so...

type:node and (shop=* or amenity=* or leisure=* or public_transport=*) and user:contrapunctus in India (hope I haven't missed anything major) tells me I've added a total of 2301 POIs in India

Of these 5 are in Ahmedabad, 8 are between Delhi and Chandigarh, 271 are between Chandigarh and Himachal Pradesh, and 2,017 are in Delhi.

These include -

• 920 bus stops
• 168 fuel stations
• 134 public toilets
• 111 ATMs
• 102 banks
• 65 restaurants
• 60 places of worship
• 51 fast food joints
• 34 cafes
• 32 convenience stores (or departmental stores, as we call them here)
• 28 schools
• 26 clothing shops
• 24 chemists
• 21 dustbins
• 20 dairy stores
• 16 salons
• 14 car dealerships
• 11 dentist's clinics
• 10 car repair shops
• 9 each of benches, gyms, and taxi stands
• 7 each of alcohol shops and ice cream parlors
• 5 each of bakeries, clinics, kindergartens, and police stations
• 4 each of bookshops, drinking water, and embassies
• 2 bus stations

Hard to believe - have I really added so much already? Of course, I admit that these are really just the tip of the iceberg...

I've often been asked by people who are close to me and know of my OSM work - who uses OSM? Does your mapping help anyone? It's hard to ingrain the infinite value of open data in their minds, but a more interesting answer has slowly been emerging - me! It helps me.

On our road trip to Himachal, I had tried to map as many fuel stations on the way to Janjehali. On the way back, when our car was running low on fuel [2], I was able to know exactly how far away we were from the nearest fuel station. The patchy data coverage there didn't deter the user of OsmAnd offline maps, either.

When I need to know what Metro exit I need to take, taking a look in OsmAnd is easier than consulting the signs. (Google Maps doesn't have this, AFAIK.)

When I went to a bank which I thought was on street A but happened to check OsmAnd and realized in time that it was on street B.

Google Maps itself has failed me many times - telling me there's a swimming pool for what turned out to actually be a pond in a park, telling me there's a chemist 3 kilometers away from where it actually was, and other such comical moments.

And while OSM doesn't yet have the POI-completeness to match, it's already exceeding my own memory's record of local knowledge - storing and reminding me of things I've long mapped and forgotten. All it needs - as far as I'm concerned - is

• multi-modal PT routing, real time traffic information, snappier performance, and better search in OsmAnd,
• (not so importantly) the ability to modify tags of ways and areas in OsmAnd, and
• support for Bezier curves/splines in the database. [3]

Lastly, I now seem to have hit the 700 changesets mark. Yay me. :)

[1] I tend to not sit when surveying a bus route - the seats in the main, low-floor cabin are all reserved for women or the elderly or persons with disabilities, and if I sit in the high-floor rear section, I often miss the names at bus stop signs.

[2] the only way to get fuel in Janjehali is through the 'black market', which is expensive and/or not assured of purity

[3] Of course, bigger adoption in India would be nice, too...

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Have you ever wondered where you will get to if you walk at random in a city ? Will you go far away or simply wander around in your neighborhood? It's a well-known mathematical problem, usually applied in my country to people who have drunk too much.

A few days ago I thought it might be fun to watch that drunkard walk on Openstreetmap. Some quick coding with Leaflet, Overpass, Nominatim, etc., produced something I called Deambule. Every time you click on Walk, the drunkard, or let's say the robot, walks 50 steps (where a step means going from one OSM node to the next OSM node on a way). At every corner it chooses one of the ways at random (trying to avoid directions he took very recently).

This is perfectly useless, and yet I think there's something fascinating about it: it's fun when the robot explores new neighborhoods and very boring when it cannot escape a public garden it has already visited several times. I might have called it Flâneur or Dérive, but the reference to Walter Benjamin or the Situationist was a little presomptuous for a robot that doesn't even know why it is walking.

You may ask the robot to walk more on the same path, or you may start other paths around the world. Remember that all paths are shared by everybody. At the time of this writing, on path #1 the robot (green marker) has almost come back to its starting point (blue marker) after walking more than 300 km inside and outside Paris.

After a few hours, 15 new paths have been created, presumably by people who saw this diary entry. I removed those which do not contain any nodes except the starting point (I assume they were probably created by mistake).

I guess I am the first girl child to map from OSM Nigeria community and else where.Its fun and exciting to map with mapswipe,identifying buildings in zambia on a satellite imagery.I needed a phone from uncle victor to play games,and he actually gave me to do so.But later,he mentioned that the phone is used for mapping,I became eager to learn .I got into it and discovered it was like a game giving me scores for good swipes made.I mapped and scored .... The next day I joined uncle Victor to the office where UniqueMappersTeam members were mapping and i could not see myself being idle ,watching while they map.Maybe,If I map,uncle victor would give me a phone or get me shawama.So i watched the video on how to map,and watched Blessing mapped and she helped me to learn how to map,then .I started mapping carefully,using her OSM account. Later,when Blessing Oshoma left,Emmanuella wanted me to continue mapping using her OSM account and I said no,I need my own account created and wow ,she helped to create an acount for me and there I go mapping on OSM!!! Any one there,,,,,,,in high school junior mapping on OSM in Nigeria and else where..? I want to know,otherwise,I guess I am the first girl child of 9 years of age to join this community to map for people in need.Thanks to mum for giving me the opportunity to spend my summer vacation in Port Harcourt with uncle Victor,the team coordinator of UniqueMappersTeam,Nigeria As I go back to Owerri today ,I am going to share with mum and others about UniqueMappersTeam and OSM.And I will also share with my fellow students as I resume for Junior secondary school,class-1 Thanks to Uncle Victor,Aunty Onyi,Blessing,Emmanuella and UniqueMappersTeam for giving exciting time this holiday period.

07/08/2018-13/08/2018

SotM Japan 2018の集合写真 ¹ | © Photo by 渡邉剛広, CC BY 2.0

About us

• Wikimedia Germany wrote (automatic translation) an article about OSM and weeklyOSM in particular. It covers the history of both, OSM and our Weekly News, and some background information on what it means to operate a blog with 12,000 readers. As reported, weeklyOSM is now translated to Swahili and Korean, which was partly made possible thanks to a Wikimedia Foundation grant.

Mapping

• On the tagging mailing list Frederik Ramm wondered whether place nodes for continents are useful. Even if they are it may not be possible to place them consistently, given that continents can be defined in different ways.
• The discussion on the OSM talk mailing list that originally started with the question on whether highway=* + area=yes or area:highway=* should be preferred, soon expanded to questions about routing over areas, generalisation and as usual the documentation or the lack thereof in our Wiki.
• Andy Mabbett raised the question "Slash, space, or spaced hyphen in multi-lingual names" on the tagging mailing list, pointing to the supposedly inconsistent wiki page. In fact, it merely documents different practices were adopted in distinct multilingual locations around the world.
• The upload of historic county boundaries in the UK caused a long discussion on Talk-GB as to whether these objects should be in OSM or not. While there are many good arguments against intangible historic data, the discussion was not that clear in favour of the removal. The mapper who uploaded the changeset participated in the discussion and gave some background about his motivation.
• The possible use of the Open Location Code system, also known as Plus Codes, within OSM has resulted in long and heated discussions on GitHub and the talk mailing list. The issue is scarcely a new topic for OSM.

  Plus Codes, which are basically a transformation of latitude and longitude coordinates into a more memorable format. Like the proprietary scheme What3Words they offer better geolocation in places, such as many countries in Africa, where most buildings lack addresses.

  The key dispute is whether they should be implemented on OSM and if so where: as tag/value pairs, computed on the client side, or provided by geocoders.

  As a response to the recent discussion, Frederik Ramm wrote a FAQ styled blog post covering the most important points to which he added social arguments as well. Christoph Hormann and Ilya Zveryev (automatic translation) provide two different perspectives on their blogs.

• The Korean OSM community changed all keys containing ko_rm to the BCP 47 standard compliant ko-Latn. This change has been discussed for the first time in 2015 on the talk-ko mailing-list where Thierry Bézecourt mentioned the -Latn tag. In 2017, users Nrimbo and Artemis64 both proposed a mass edit and the community agreed on it each time. Other countries still use the xx_rm scheme, neighbouring Japan for example.
• The voting for the proposed feature evacuation routes is under way. Evacuation routes intend to aid people’s evacuation away from natural desasters like hurricanes or tsunamis and are typically signed with a round, blue evacuation route sign, at least in the US.
• According to a tweet from the JOSM developers, Microsoft has fixed their browser Edge, such that it is not treating content from local loopback as mixed content anymore, which means that "Edit with JOSM" now works, as it has for years already in other browsers.

Community

• The 14th birthday of OpenStreetMap was celebrated around the world!
• Good news from Gregory Marler on Twitter: the first SOTM videos of individual sessions are now uploaded to the SOTM Youtube channel. They are working on uploading them all and created a playlist for you. You are invited to add transcriptions and translations as explained here.
• BBC radio features Fatima Alher and OpenStreetMap Niger who are mapping the flood plains in Niger for Flood Resilience.
• The demolition of slums to create space for other projects were often without any consequences as no one could tell how many people were affected. The lack of information is often a problem as it makes the demand for compensation hard or impossible or it may even prevent the demolition in the first place. Slum mapping has become popular as it’s harder to destroy what is documented.The article at the BRIGHT Magazine explains the issues of unmapped places like the slums in Kenya but also the help that projects like Map Kibera can provide.
• United States ambassador to Turkmenistan, Allan Mustard (OSM apm-wa), has been chosen as Mapper of the Month by OSM Belgium. Allan described his biggest achievement as having created the most comprehensive publicly accessible map of Ashgabat, the capital of Turkmenistan.
• Niklas Weber, a German physician, examined the tick-borne skin disease in Quilcacé, El Tambo, Cauca, Colombia. He uses OpenStreetMap on Locus-Map intensively on his travels as Carlos, aka kaxtillo reports in his blogpost.
• User malenki died in a hiking accident in Spain in 2016. A memorial stone has now been dedicated to him in his home village of Kleinhartmannsdorf.
• All mappers use taginfo at some time or another. Jochen Topf, the developer, blogs that the tool now supports 18 languages, after the addition of Farsi (فارسی / Persian) as the first right-to-left language. If you can help with translations, either by adding a new language or by completing an existing one, please have a look at this wiki page.

OpenStreetMap Foundation

• The OpenStreetMap Foundation has changed its headquarters.
• The License Working Group has published a draft for the new terms of use for openstreetmap.org and asks for feedback.

Events

• Geochicas invite all women conference participants to an evening event in advance of this years’ FOSS4G Conference and HOT Summit which will be held in Dar es Salaam. If you want to join the event, please register.
• State of the Map Japan 2018 was held in Tokyo on August 11. The photos taken during the day are published on Flickr.
• FOSS4G 2018 Tokyo will be held at Komaba Research Campus of the University of Tokyo on November 9th and 10th.

Humanitarian OSM

• Ramani Huria 2.0, a local community-based mapping project in Dar es Salaam, reports on an initiative to map the local drainage network in order to develop flood models. As a rapidly expanding coastal city Dar is often affected by high water levels. The model should help understand flooding, its impact, and attribution of limited infrastructure, maintenance, and solid waste on flood risks.

  The type, dimension and state of drainage infrastructure in 200 flood prone sub-wards will be mapped over 6 weeks this summer. The project is supported by the World Bank and HOT.

• The Hindu, an Indian newspaper, wrote about both crisis mapping in general, and a session hosted in a hotel in Delhi by Medecins Sans Frontieres for explaining the Missing Maps project and training of volunteers.
• A new open access Perspective Article by GIScience Heidelberg and Red Cross (UK/Germany) discusses the Missing Maps Approach and its potential within the Red Cross and Red Crescent movement

Education

• The question on how to query Overpass with the API development environment Postman was answered on GIS StackExchange.

Open Data

• Aerial missions using LiDAR provide very precise elevation models. With the publication of Lidar point clouds for Estonia, Martin Isenburg of rapidlasso Gmbh reflects again on the question of access to such data. National agencies often keep such data behind very high paywalls. Previous blog posts (1, 2) questioned whether the income of around £323,000 per year between 2007 and 2014 made it worthwhile to keep restricted access to such accurate elevation data.
• At the ToulouseSpaceShow 2018 GIScience Heidelberg announced a forthcoming new landuse data set combining OSMlanduse.org with EU Sentinel 2 data, initially for all of Europe

Software

• Imre Samu shows a proof of concept for a taginfo extension to enable filtering by area.
• A new version of OSMCha with the changes required by the GDPR will be released by the end of August. Read about what will change in Wille’s diary post.
• lifehacker.com published an article Navigate Hiking Trails With Hikepack, a review of the OSM based, commmercial hiking app for iOS.
• OpenStreetMap US has issued a request for proposal concerning the redesign of the TeachOSM website. If you want to apply for this paid contract, you should hurry as the deadline is August 20, 2018.

Programming

• GIScience Heidelberg published a new Jupyter Notebook which demonstrates how to use the OpenRouteService Isochrones API to analyse health care accessibility in Madagascar.
• Anusha Singh continued working on the web based public transport editor. According to her diary post she added a lot of improvements like a beginner and an expert mode and interactive tutorials on how to use the web app.
• Florian Schäfer worked on improving JOSM’s Wikipedia plugin during his Google Summer of Code project. According to the documentation he added validator warnings if wikipedia and wikidata don’t refer to the same item and made wikidata items with coordinates a lot more accessible than they were before. Florian also helped out other GSoC students and has maintained the Mapillary plugin over the past 4 years.
• Srikant Chepuri wrote a diary post about the progress made on GTFS2OSM, a GTFS to OSM integration tool. GTFS2OSM aims to make use of a GTFS feed to add missing public transport elements to OSM easier.
• Matias Dahl, who wrote a blog post in 2015 about tag transitions of amenity=* , has created a new website called tagdynamics.com that is intended to provide more insight into the transition of OSM tags. The work-in-progess website shows how tags, values or tag/values like building=yes have transitioned from or to over the time. The source code is available on GitHub.

Releases

• Pic4Review, a simple OSM editor allowing contribution based on street level pictures, just received an update according to this tweet. The new version improved the UI, "mission" management and added German as an additional language. (Missions are thematic categories, examples here on the dev instance).

Did you know …

• … Chris Anderson-Tarver wrote a doctoral thesis dissertation on the OSM crisis mapping done in response to the 2010 earthquake in Haiti.
• … Yi Shen provided a nice way to present OSM’s data. On a GitHub.io page you can create your own Little Big City with OSM data. The source code is available on GitHub.
• ….the DroneRadar map? The service indicates on an OSM basemap the areas in Poland where you are allowed to fly and which restrictions apply .
• … the list of the OpenStreetMap Foundation servers with information about equipment, use and location?

OSM in the media

• As pointed out in the Portuguese OSM talk list, an OSM map was used on the Portuguese news program of RTP (regarding the wildfires occurring at Monchique). You can watch at this website at the timestamp of 10:58.

Other “geo” things

• On the 12th of August Spain and Portugal ratified (automatic translation) an agreement to accurately delineate their borders along the rivers Miño and Guadiana.
• Google is very adamant about tracking your location, whether on Android phones, or services like Google Maps on iPhone. Even explicitly turning off location tracing may not do so for every app.
• A petition wants to change the border of the Batman province in Turkey into the shape of the "Batman" logo.
• The recent heatwave has revealed previously unknown archaeology sites in the UK.
• The Berliner Morgenpost has published (automatic translation) an interactive comparison of satellite images of several selected locations in Germany from early May to late July/early August 2018 in order to make the optical consequences of the drought more comprehensible. The German astronaut Alexander Gerst posts pictures of dried-out Central Europe as seen from ISS on Twitter.

Upcoming Events

Where	What	When	Country
Mumble Creek	OpenStreetMap Foundation public board meeting	2018-08-16
Rapperswil	10. Micro Mapping Party Rapperswil 2018 (inc. OSM-Treffen)	2018-08-17
Lüneburg	Lüneburger Mappertreffen	2018-08-21
Derby	Pub Meetup	2018-08-21
Lübeck	Lübecker Mappertreffen	2018-08-23
Takatsuki	みんなでエディタソン#01	2018-08-26
Düsseldorf	Stammtisch	2018-08-29
Dar es Salaam	FOSS4G & HOT Summit 2018	2018-08-29-2018-08-31
Manila	Maptime! Manila	2018-08-30
London	Missing Maps Mapathon	2018-09-04
Praha – Brno – Ostrava	Kvartální pivo	2018-09-05
Stuttgart	Stuttgarter Stammtisch	2018-09-05
Bochum	Mappertreffen	2018-09-06
Buenos Aires	State of the Map Latam 2018	2018-09-24-2018-09-25
Detroit	State of the Map US 2018	2018-10-05-2018-10-07
Bengaluru	State of the Map Asia 2018	2018-11-17-2018-11-18
Melbourne	FOSS4G SotM Oceania 2018	2018-11-20-2018-11-23

Note: If you like to see your event here, please put it into the calendar. Only data which is there, will appear in weeklyOSM. Please check your event in our public calendar preview and correct it, where appropriate.

This weeklyOSM was produced by Kateregga1, Nakaner, NunoCaldeira, Polyglot, Rogehm, SK53, SunCobalt, TheSwavu, YoViajo, derFred, jinalfoflia, k_zoar.

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Dear all,

Yesterday, v4.14.0 of the OpenStreetMap Carto stylesheet (the default stylesheet on the OSM website) has been released and rolled out to the openstreetmap.org servers. It might take a couple of days before all tiles show the new rendering.

Changes include

• Added text-repeat-distance for waterways
• Added text-repeat-distance for railways
• Added icon for leisure=bowling_alley
• Added icon for leisure=outdoor_seating
• Added icon for leisure=bird_hide
• Added icon for shop=video
• Added icon for shop=paint
• Added icon for shop=massage
• Increased casing width of tertiary road on z12
• Standard text halo for fitness_centre and fitness_station
• Updated Docker images definitions
• Small documentation updates

Thanks to all the contributors for this release.

For a full list of commits, see https://github.com/gravitystorm/openstreetmap-carto/compare/v4.13.0...v4.14.0

As always, we welcome any bug reports at https://github.com/gravitystorm/openstreetmap-carto/issues

Har raderat snabbmatstället bredvid Coop som har lagts ner för någon månad sedan. I övrigt försöker jag mappa ut en ö i ån nedanför mig. Men problemet är att hela ån bara är en smal linje , vilket inte stämmer med verkligheten. Ön kom till därför att en gång i tiden gick det ett avloppsrör rakt ut i ån och där utloppet var bildades en ö. Idag är det fastland, fast ingen verkar äga ön. Har lagt ut trall som gångstigar, men det känns ändå ostadigt att vistas på ån då allt i princip är gungfly. Tanken var att bygga en hel skulpturpark på ån, som svämmar över varje år. Ungefär som ett Atlantis. Men så långt kom jag aldrig.

A fairly big but silent change in the openstreetmap.org map rendering infrastructure has been completed in the last days which is going to have a significant effect on mapper feedback through the standard style map.

here

Technically this is just a system and software update. This however includes a new osm2pgsql version which fundamentally changes the way multipolygon geometries are assembled. This change in osm2pgsql already happened more than a year ago but osm2pgsql had not been updated on the OSMF rendering servers since then.

Before this change standard style map rendering tried very hard to salvage whatever could be salvaged from a broken multipolygon geometry. This sometimes led to strange results but usually something was shown in some way even for invalid geometries. This led to mappers often being not very diligent about multipolygon geometries - as long as it shows up in the map it is fine. For data users not using osm2pgsql (like for example my low zoom map demo) this was a big problem because if they rely on valid geometries these invalid multipolygons are all unusable - yet customers of course expect them to be usable since you can see them in the map.

Long story short: That the standard map rendering should not try to be most tolerant about multipolygon validity but rather be more strict about it to give mappers better feedback about their mapping has been a demand of many people for a long time. This has now finally happened.

Osm2pgsql now uses libosmium for building multipolygons which means it is tolerant about

• duplicate nodes
• duplicate segments between nodes
• noded self intersections (i.e. rings intersecting with a node at the intersection)

It however is strict about

• non-noded self intersections
• open rings

Quite a few people have noticed during the last week or so already that there are a few prominent gaps in rendering where there previously were features shown. Many of these have already been fixed quickly because of this improved feedback. This is not yet very visible in the multipolygon error numbers because these count errors and do not consider their visual impact and what has been fixed quickly is primarily a few high impact cases. But there is hope that with this change in rendering and the resulting improvement in feedback to mappers about errors the increase in the number of errors in multipolygons might be stopped or at least be slowed significantly.

here

There are plenty of prominently visible gaps in the map due to broken multipolygons still waiting to be fixed of course. If you work on fixing such errors it is also a good idea to take the opportunity to split larger geometries into smaller features which can be maintained more easily and are less prone to breaking.

here

You can find these errors not only through missing geometries in the map but also through the OSM Inspector

Hussain Street, Rasool Park Lahore

How do I post screen shots in new diary entries?

#Here i lonched

Neste post descrevo, e submeto a opiniões, os passos adotados para um processamento semi-automático de vetorização a partir de imagem de satélite Sentinel-2, com controle de parâmetros e validação manuais feitos pelo usuário, tendo em vista a vetorização de áreas de matas com suficiente distinção entre natural=wood e landuse=forest (mata natural e mata cultivada).

Importante:
esta metodologia é aqui apresentada como uma preparação para uma possibilidade de proposta de mapeamento para a comunidade OSM, como um metodo auxiliar ao mapeamento, e que tem como foco somente o mapeamento voltado a coberturas de terreno (landcover).
O processo todo resulta em simplificação, como curvas com espaçamento entre nós não menor que 10m (a resolução da imagem é 10m/px).
Não serve para objetos pequenos como lotes, praças, etc., pois removeria detalhes que podem ser vistos nas imagens padrão do OSM.

O propósito é:
-poder gerar desenho de grandes áreas de mata densa, em interiores do território, não densamente urbanizados;
-com diferenciação de vegetação usando índices apropriados, para forest e wood, o que não é facilmente, e/ou comumente, distinguido nos desenhos sobre as imagens padrão. Mesmo assim, deve ser verificado visualmente o resultado ao final do processamento, junto com as imagens padrão do OSM.

DESCRIÇÃO INICIAL DOS DADOS:

IMAGENS: SENTINEL-2 / Cobertura: ~100x100Km
Documentação original: https://www.sentinel-hub.com/develop/documentation/eo_products/Sentinel2EOproducts
Licença compatível com OSM: conforme https://wiki.openstreetmap.org/wiki/Sentinel-2
Fonte das imagens: https://earthexplorer.usgs.gov/ : camadas Sentinel-2
Bandas utilizadas: B11 (testadas todas as bandas, B11 se mostrou a melhor para o objetivo; condição:nuvem=0)

Índices utilizados: NDVI; CRE

NDVI: Normalized Difference Vegetation Index : Fórmula: NDVI = (B08 - B04) / (B08 + B04)
CRE: Chlorophyll Red-Edge (abbrv. Chlred-edge) : Fórmula: CRE = (B07 / B05) ^ -1

PROCESSAMENTO - PASSOS:

1)ESCOLHA DA IMAGEM DE SATÉLITE - SENTINEL:
Escolhida imagem em data que não gere discrepâncias (para mais ou para menos) de atividade vegetal: preferencialmente entre equinócio e solstício; evitar alto inverno e alto verão.

Caso Bom Jesus, RS:
https://earthexplorer.usgs.gov/ - Sentinel - "T22JEP_20180420T132231_B11"
Data: 2018/04/20
Hora: 13:22 (09:22 Brasil; gera alguma sombra)
[IMAGEM-1: abaixo. A imagem abaixo pode ser aberta em nova guia para visualizar detalhes em zoom maior.]

2)Examinar no QGIS valores do raster; procurar valores limites de distinções de classes:

-Usar as imagens em cores naturais, Sentinel-TCI (True Color - ver imagem abaixo ) e Bing, para localizar e marcar exemplos claramente identificáveis dos tipos de vegetação a serem distinguidos em classes.

Exige conhecimento da vegetação típica básica, e sobretudo poder identificá-la em imagem de satélite em cores naturais. Além de evidentemente poder distinguir os demais elementos, como área urbanizada, campo ralo, trilhas e corpos de água.

A identificação dos tipos wood e forest é possível de fazer somente sobre imagem Bing. Neste método, deve ser feita ainda preferencialmente sobre a imagem TCI do conjunto Sentinel-2, uma vez que esta apresenta o conjunto dos mesmos objetos no mesmo instante de tempo (áreas de florestas podem ter sido cortadas em diferentes períodos). Basta saber distinguir os tipos, o que é viável com algum pouco treinamento do olho.

O que o presente método propõe é usar uma seleção amostral, feita sobre as imagens em cores naturais, de objetos dos quais se tenha seguro conhecimento, que sejam representantes destes 2 tipos, wood e forest, bem como dos demais objetos que não são estas 2 classe, para, nas imagens dos demais sensores e ínidices Sentinel, avaliar os valores típicos de pixels dos objetos selecionados, testando se os mesmos limites de valores são encontrados em matas típicas em algumas diversas porções da imagem.

Os valores típicos em uma imagem podem apresentar alguma variação em outras imagens e outros locais. Por isso todos os passos do método deve ser repetidos em trabalhos com outras imagens ou territórios.

Uma vez que se encontre os valores limites típicos na imagem, usá-los como parâmetros para deduzir os objetos dos tipos wood e forest na imagem toda.

-Áreas de mata cultivada isoladas são mais fáceis de distinguir na imagem em cor natural. Não tanto quando plantadas em meio a mata natural, o que exige um olhar mais treinado aos detalhes das feições típicas (ver na imagem abaixo).
Assim é preferível procurar primeiramente áreas destacadas de plantação, em campo aberto. Como lotes mais ou menos regulares de mata plantada.
As espécies plantadas mais comuns nesta região de enfoque são sobretudo o Pinus, para indústria de celulose, e menos comumente Eucalipto, mais para construção e lenha.

As florestas de Pinus, e florestas plantadas em geral, costumam apresentar espécimes regularmente espaçados, formando uma malha regular densa, com uma superfície mais regular, como um tapete plano. Procurar por estas feições. São plantadas em campos planos e também em encostas. Nas encostas podem ser detectadas por várias trilhas de serviço para extração, escalonadas. Eucalipto costuma ser plantado ao redor de sedes de fazendas em campo aberto. A superfície de florestas plantadas de modo geral costuma apresentar maior homogeneidade de tamanho de indivíduos.

-As matas nativas ou mais velhas, com menor atividade biológica, e por isso índices diversos nas imagens, são o restante de matas a identificar. Em geral apresentam a superfície da cobertura mais irregular quanto ao tamanho dos indivíduos. Mais facilmente encontradas em locais onde não haja estradas ou trilhas de serviço indicando acesso constante, e em faixas variáveis ao longo das margens de cursos d'água, devido ao estatuto legal de proteção das matas marginais.

Estas áreas exclusivas com respectivos objetos-tipo servirão de amostragem de elementos das classes para avaliação dos valores de pixels. Podem ser feitos polígonos para marca-los, em layer à parte.
Com estes objetos-tipo, serão verificados e anotados os valores típicos dos pixels nas imagens de referência a serem examinadas alternando, sobre o mesmo objeto amostral, os respectivos layers: B11, NDVI, CRE. E anotados os limites de valores em relação aos objetos das demais classes. Estes valores limites típicos serão usados para distinção automática de tipos de vegetais na imagem toda.

CASO: Matas nos municípios de Bom Jesus e Monte Alegre dos Campos, RS:
QGIS: View menu : Identify feature : testar valores de pixels individuais

SENSOR / ÍNDICE :: B11 :: NDVI :: CRE
Min. :: 511 :: -0.87 :: 0.2
Max. :: 2709 :: +0.87 :: 0.75

MATA NATIVA (n.=wood) :: 1200-1800::0.30 /0.80 ::
MATA PLANTADA (l.=forest) ::150-1200 :: 0.70/0.80 ::
Mata na sombra(encosta.Sul) ::150-400 ::0.30/0.65 :: 0.4 - 0.5
Mata no sol (encosta.N.) :: 1200-1800 :: 0.65/0.80:: 0.25-0.3

campo ralo (null) :: 1500 -3000 :: :
urbanização :: 1700-3000 ::-0.10/0.20 :

água parada (açude) :: 50-150 ::-0.10/0.10 :: 0.8-1.0
água corrente (rio) :: 150-300 :: -0.30/0.60 :: 0.5-0.8
valor limite para água :: < 150 :: < 0.3 :: >0.5

Resultado observado:
* B11 distingue bem entre os tipos de matas "wood" e "forest".
Para isto ver também a documentação Sentinel sobre as aplicações dos diversos sensores e índices:
https://www.sentinel-hub.com/develop/documentation/eo_products/Sentinel2EOproducts
Em diferentes áreas do terreno abrangido na imagem, as mesmas classes de matas apresentam alguma variação, o que pode gerar ainda algum grau de imprecisão para uma distinção absolutamente exata. No entanto o limite de matas x campos ou urbanização é bem distinto. Por outro lado, não separa rios de forest; para isto necessita contrastar B11 com outros índices mais adequados, a seguir.
* NDVI e CRE destacam mais nitidamente o que é e o que não é vegetação, como corpos de água, trilhas, urbanização, etc.

[IMAGEM-2]

3)CORREÇÕES:

A imagem B11 distingue bem entre os tipos de matas wood e forest, mas não distingue bem matas na sombra (encostas Sul) e água corrente. Os índices NDVI e CRE fazem esta distinção. É necessário então corrigir a imagem B11 acrescentando as distinções de NDVI e CRE.

OPERAÇÃO: B11+(1-NDVI)+CRE
QGIS: Raster calculator: SINTAXE: "T22JEP_20180420T132231_B11@1" + ((1-"NDVI@1")*1800) + ("CRE@1" *2600)
Agregados empiricamente os fatores ponderadores NDVI*1800 e CRE*2600, para que fiquem acima dos valore limites dos tipos em B11 (~2000) , e equilibrar estes índices entre si. Isto é, depende de testes de tentativa-erro-acerto até chegar aos valores de equilíbrio. Precisa ser verificado em cada trabalho com outros conjuntos de imagens diferentes, em outros locais.

RESULTADO: "B11+1-NDVI+CRE.tif" : (10m/px)

[IMAGEM-3]

4)TESTAR CLASSES VISUALMENTE:
-Igual à etapa (2), para B11+1-NDVI+CRE:

Depende igualmente de fazer testes de tentativa-erro-acerto até chegar aos limites significativos e adequados à realidade que possa ser observada nas imagens naturais.
QGIS: Raster Properties :
Style / Categorized Renderer: Equal Interval
Color interpolation: Discrete : testar manualmente classes e valores limites

Resultado para esta imagem do caso-teste:
5 CLASSES :
(1)forest <= 2150 <
(2)wood <= 2600 <
(3)campo alto ou ciliar <= 3500 <
(4)campo ralo ou trilha <= 3950 <
(5)água, estrada, cidade, lavourado

5)PROCESSAR A IMAGEM, REDUZINDO ÀS CLASSES TESTADAS:

CONDICIONAIS "if/then/else":
QGIS: Raster calculator:
SINTAXE: (("@1"<x)* operação ) => se sim=1(opera); se não=0(não opera)

SINTAXE :
(1* ( "B11+1-NDVI+CRE@1" <=2150 ))
+ (2* (( 2150 < "B11+1-NDVI+CRE@1" ) AND ( "B11+1-NDVI+CRE@1" <= 2600 ) ))
+ (3* (( 2600 < "B11+1-NDVI+CRE@1" ) AND ( "B11+1-NDVI+CRE@1" <= 3500 ) ))
+ (4* (( 3500 < "B11+1-NDVI+CRE@1" ) AND ( "B11+1-NDVI+CRE@1" <= 3950 ) ))
+ (5* ( "B11+1-NDVI+CRE@1" > 3950 ))

Resultado:
Imagem com valores limitados às classes: "B11-5classes.tif"

[IMAGEM-4]

6)SIMPLIFICAR ÁREAS, REMOVER PIXELS ISOLADOS:

OPERAÇÕES: ~1min cada
QGIS: Processing / Toolbox :
* GDAl / Analysis / Sieve
* GRASS / r.neighbor

1 Sieve T3 C8 - Remove até 3px(10x30m)
2 Neig Max 3 Ci - Aumenta borda do maior, 1px cada lado (10m+10m)
3 Sieve T3 C4
4 Neig Mode 3 Sq
5 Sieve T6 C8 - Remove até 6px(20x30m)

Resultado:
5 classes filtradas: "B11-5classes-Filtrado.tif" (10m/px)

[IMAGEM-5]

7)REDUZIR A IMAGEM SOMENTE ÀS CLASSES DE INTERESSE (forest e wood; 1 e 2) E IGUALAR DEMAIS CLASSES (3,4,5) A "ZERO":

QGIS: Raster calculator: (( "@1" > 2 ) * 0) + (( "@1" <=2 ) * "@1" )

8)CRUZAR O RASTER PROCESSADO COM OS VETORES EXISTENTES NO OSM, DE RIOS E ESTRADAS, PARA MELHORAR RECORTES NAS MATAS:

-Baixar dados do OSM/overpass: highway=* or waterway=* and type:way
-Criar um novo layer vetor SHP contendo as linhas de highway e waterway que servirão para recortar.
-Adicionar um campo de número real, com valor "0"(zero) para todas as linhas/polígonos a serem filtrados
-Gerar um buffer sobre todas as linhas:
QGIS: Vector / Geoprocessing / Buffer : fixed distance
O buffer deve ser maior que a resolução da imagem destino e menor que os menores grupos de pixels após as filtragens. Para não gerar pixels ligados só nos cantos nem elminar a mais do que as filtragens anteriores. Utilizado buffer de 20m; suficiente para imagem de 10m/px. Contendo valor classe=0.
-Salvar o buffer como SHP

RASTERIZAR O VETOR SOBRE A IMAGEM:
-Fazer uma cópia simples do raster anteriormente processado, a ser alterada, como destino; -Adicionar o layer vetor do buffer sobre a imagem destino:
QGIS: Raster / Conversion / Rasterize : SHP com o buffer, campo p/ valor "0"(zero) : imagem destino

ÚLTIMA FILTRAGEM:
simplificar; remover últimos pequenos clumps de até 20 pixels isolados (4x5 pixels) que sobram em alguns recortes:
Sieve T20 C4

Resultado: "B11-0-1-2-Filtrado+OSM.tif"

Não é objetivo do processo captar áreas com menos de 40x40m nas distinções, o que equivale a pouco, o que gera muitas imprecisões. Somente áreas de matas com maior homogeneidade, que possuem dimensões muito maiores.

[IMAGEM-6]

9) ELIMINAR DEMAIS CLASSES NÃO UTILIZADAS - Igualar "zero" a "null":
(ajuda a reduzir a quantidade de polígnos no poligonize):

QGIS: gdal_translate / -a_nodata < x > : (x = valor da classe p/null)

Resultado:
Raster Classes 1+2+null : "B11-1-2-null+OSM.tif"

10)VETORIZAR - Gerar polígonos do raster:

QGIS: Raster / conversion / poligonize (manter mesma CRS no projeto)
Gera SHP com polígonos para as 2 classes somente.

11)SUAVIZAR POLÍGONOS - despixelizar:
QGIS: GRASS commands -> v.generalize.smooth:
(https://grass.osgeo.org/grass64/manuals/v.generalize.html)

* Sliding Averaging : look_ahead = 3 (mín=3, sempre ímpar) : slide = 0.7

Resultado: ~5min "Sliding-Average-L3-SL07-OK.shp"

[IMAGEM 7 e abaixo]
LEGENDA:
Verde claro: mata nativa (natural=wood)
Verde escuro: mata cultivada (landuse=forest)
Branco: null

12)ADICIONAR TAGS OSM E LIMPAR:

ELIMINAR DO SHP POLÍGONOS TRUNCADOS DE BORDA DE IMAGEM ou RESTRINGIR POLÍGONOS A MUNICÍPIO

Salvar como novo layer: "B11+OSM-limpo.shp"

-Selecionar todo polígono que encosta nas bordas do layer e remover manualmente (pode fazer um buffer menor que os limites do layer);
-Adicionar tags:
QGIS: Table Manager + Field Calculator : landuse=forest; natural=wood;
-Remover tags sem uso (area; classe).
Salvar com CRS para OSM: WGS84-EPSG:4326

13) Opcional: Selecionar por máscara de município
(pois o JOSM fica muito pesado se abrir mais de 10.000 polígonos)

QGIS: Vector / Spatial Query: "SHP-origem" + Intersects + "SHP-máscara" - selection / invert / remove

PROCESSOS NO JOSM:

1)ABRIR CADA SHP SEPARADAMENTE :

-Sem baixar dados do OSM
-salvar como .osm

Já entra como polígono ou multipol(outer/inner); curvas com ~1nó/10m (mesma resolução das imagens raster)

Monte Alegre dos Campos:
QGIS: SHP 5,2MB; features/polígonos= 3.519
JOSM: "B11-MA-4326.osm" : 23,8MB; nodes= 243.070

2)SIMPLIFICAR:
Select: type:way / Symplify way
Resultado: nodes= 136.853

3) VALIDAR SEPARADAMENTE
-Sem baixar dados do OSM

Resultado:
Errors: (0)
Warnings: (28)
Self-intersecting polygon ring (3)
Self-intersecting ways (25)

Todos foram casos de auto-intersecção em cruz (quina) - imagem abaixo.
Talvez possa evitar previamente no QGIS com
QGIS: GRASS commands -> v.generalize.smooth / Displacement
(https://grass.osgeo.org/grass64/manuals/v.generalize.html)

Solução:
Alteração manual: "unglue(G)" nos nós de cada intersecção; afastar nós e ways
Cuidar: testar todos os nós da intersecção após o unglue; manter polígonos e multipolígonos fechados ao final

VALIDAR NOVAMENTE:
Resultado: VALIDAÇÃO COMPLETA (0)

4) TESTES DE CONFLITO COM O EXISTENTE:
Baixar dados do OSM;

VALIDAR:
Resultado: VALIDAÇÃO COMPLETA (0)

Pronto para aprovação e upload

5) PARA UPLOAD DOS NOVOS DADOS AO OSM:

Se aceito o método como válido para o OSM , proposto salvar em changesets exclusivos, com a nota:
"adição de polígonos de matas (wood;forest) semi-automatizados com controle de parâmetros e validação manuais"

O arquivo .osm resultante deste teste para verificação pode ser baixado neste repositório (não feito upload ao OSM).

Comentários:

Onde acho que ainda pode melhorar:

-A área da imagem cobre cerca de 100km x 100km. A imagem classificada tem 10m/px. O método permite obter cerca de 250 novos nós de polígonos por km2 (exemplo Monte Alegre dos Campos: 548km2, 136.853 nós), o que para a área inteira significa cerca de 2.500.000 nós. É algo que levaria muito tempo para ser mapeado no OSM somente manualmente sobre imagem Bing (ou muito possivelmente nunca viesse a ser mapeado). É possível ainda pensar em reduzir esta quantidade de nós, simplificando sobretudo linhas mais retas.

Na minha opinião de todo modo é muito recomendável simplificar e eliminar pequenos clusters de pixels, como <40x40m (~16px), que estejam "internos às matas", ou inclusive descartar todos destas dimensões "fora das matas", pois:
-o OSM usa simplificação de áreas de terreno, exemplo maior são as coast_lines;
-manter áreas pequenas de <40x40m, tumultua o resultado com objetos pequenos sem alta precisão para a imagem, objetos que não são objetivo do processo;
-o objetivo é mapear as áreas grandes de mata densa e mais homogêneas, sem invadir áreas de campos ou urbanização, o que já é suficientemente bem obtido pela limitação dos índices, não sendo necessários portanto este tipo de pequenas "ilhas".

-Na filtragem, seria melhor manter o mesmo buffer de aumento e retorno de limites de áreas vizinhas. Ficou um pouco a mais, 10m maior, pra dentro da mata. Por um lado evita invadir outras coisas. Mas usando os vetores do OSM para buffer de estradas e waterway, isso já resolve

-A distinção "matas" x "o que não é mata", é bem precisa.
A distinção matas "wood" x matas "forest", por outro lado, é muito boa na maior parte, mas nem sempre permanece exatamente a mesma, sob os mesmos valores limites, em todas as porções da imagem. Há poucas situações em que uma é classifica no lugar da outra, por variações dos índices. Uma distinção absoluta poderia ser comprovada somente com uma análise completa de campo. O que provavelmente nunca será viável para o OSM, e pouco provável de ser feito mesmo em qualquer órgão ambiental governamental.

Em resumo, somente por mapeamento totalmente manual e sobre imagem natural como Bing, sem recorrer aos recursos de maior precisão de distinção de tipos vegetais que sensores e índices como do Sentinel proporcionam para uma classificação mais precisa, e sem contar com ferramentas de desenho automatizado (vetorização) sobre classes previamente determinadas, com controle do usuário, para desenhar grandes áreas, como o QGIS oferece:
-dificilmente se mapeará semelhantes grandes áreas de matas;
-se ainda assim forem manualmente mapeadas, dificilmente terão a precisão de distinções que sensores específicos de satélite e indices derivados oferecem como possibilidade.

Resta a comunidade avaliar:
-se é desejável e/ou aceitável esta forma de mapeamento semi-automatizado de matas;
-se é aceitável o grau de imprecisão inerente por generalização e simplificação.

Mesmo com o limite de precisão inerente ao método, pode-se observar aí melhores resultados de mapeamento, tanto em qualidade da geometria como em distinção dos tipos, do que comumente em mapeamentos manuais existentes no OSM, em geral bastante simplificados, pela própria limitação dos recursos de imagem natural.

Por favor, fiquem à vontade para tecer comentários se desejarem, como se vale ou não vale a pena investir neste tipo de processo para desenhar áreas de mata densa, em regiões de interior, para o OSM.