Részletek:

https://wiki.osm.org/Hungary/20220813

Back in March I was amused to see Amanda tweet that the UK OpenStreetMap community ran a solar power mapping project “several years ago”. This was the theme of the [quarterly project(https://wiki.openstreetmap.org/wiki/UK_2019_Q3_Project:_Solar_Power) in Q3 2019, but the project keeps trucking on.

All small-scale Solar Power in Wales (i.e., excluding solar farms of more than 1MW capacity)

In the past few days we reached a milestone of having comprehensive solar cover for Wales, one of the constituent countries of the United Kingdom. I think this is the first country to have solar power mapped at such a fine level of detail either on OSM or anywhere else.

The rest of this post discusses aspects of the how & why of this work.

Solar Mapping in the UK

Active mapping of solar farms had been proceeding for a while prior to 2019, but it was Jack Kelly of OpenClimateFix who posed the question about mapping rooftop solar photovoltaics. His problem is nowcasting changes to power generation from solar which could reduce the need for some carbon-based standby power. When clouds pass over, solar generation can drop, leading to transient changes at a local level. Rooftop solar is a not insignificant part of the mix & both highly dispersed and clustered (at all levels: groups of houses, streets, estates, local authorities and even countries).

Current Status

Solar farms were pretty much all mapped by the time Dan Stowell talked about the project at SotM-19. Ongoing mapping is mainly about rooftop solar, with a much smaller number of ground installations, ranging from 4kW to 300kW.

We reached 40% coverage for Great Britain at the end of 2021, 400,000 individual solar panels in January this year, and currently there are 449k panels, 396k installations (an installation can have more than one panel) representing 46% of the total government figures (or 39.6% if we use a higher estimate of 1 million installations). Mapping in Northern Ireland has been more limited, but VictorIE has made decent inroads recently.

Over the 3 years since we seriously started mapping rooftop solar we’ve benefited significantly, in terms of visual feedback from Gregory Williams website and Russ Garrett’s OpenInfraMap. It has also been a good activity during the pandemic when regular mapping has been curtailed. For some of us this is still the case. Aerial imagery has also improved significantly, with installations going form black blobs to ones with clearly delineated individual modules. We can also use property boundary open data to align imagery more precisely.

At the outset we tried many different areas, but ended up focusing on a relatively small number with good aerial imagery & readily observed solar. Even then it was only possible to exceed 50% coverage in a few areas. Particularly noticeable was that visual search in rural areas was pretty hit-and-miss. I personally gave up on Anglesey, having only found 20% of the installed base and most of that in one estate in Llangefni.

Eventually, after a bit of trial and error with various open data sources, I found that clustering buildings from Ordnance Survey Local gave a small enough set of areas to search. A geojson of centroids can be used either in the Josm “To Do” plugin, or the Potlatch 3 task option. Using a Lower Super Output Area (LSOA) as a unit of work proved convenient as most can be searched in under 30 minutes, and as this is the lowest reporting unit of Gregory’s map, feedback is available in 24 hours. I first applied this in parts of Devon, but had long wanted to try it in Powys (the most rural area of England & Wales).

Overall Goals

Our initial goals in mapping solar were therefore:

• Investigate the practicability of mapping with OSM. Initial results were encouraging as the current status confirms.
• Map enough solar to allow investigation of nowcasting and other predictive approaches. This required large numbers of installations to be mapped, unlike many other OSM use-cases where usefulness either does not have a threshold, or the threshold is much lower.
• Document preferred building types used for rooftop solar. This is possible, but probably requires a better attributed & more consistent set of building data than currently available in OSM.
• Enable the creation of a solar cadastre, as was carried out with the OpenSolarMap project of Etalab in France.
• Document a significant change in the appearance of both urban & rural landscapes.
• Provide enough data in specific areas for research & case studies around domestic solar power. A comprehensive data set for Wales represents a big milestone in this regard.
• Provide good training data for automated recognition of roof-top solar from aerial imagery.
• Automated recognition of solar from aerial imagery. Although Tyler Busby did some initial work on this back in 2019, this is probably the one goal where I feel we have not made as much progress as I had hoped.

The current Project : mapping solar in Wales

Last December proved to be a convenient time to return to mapping in Powys. I spent that month adding solar, and a few other things, to Powys. This proved to be productive and met my initial goal both with respect to comprehensiveness (see below regarding comprehensiveness) and level of detail (q.v.)

Having got around 85% completion of Powys, my thoughts turned to the other rural areas of North Wales, Gwynedd and Anglesey. I started on these two in the New Year, and before I knew it realised I’d embarked on covering the whole of Wales. Originally, I anticipated this would be a six-month project. However, a few things derailed that timescale:

• City Nature Challenge 2022: without Muki Hakaly’s prompting I might have missed this after participating for the previous two years. This in turn led me to spend a lot more time recording nature on iNaturalist & other platforms.
• Degraded imagery. Bing imagery started being replaced whilst I was mapping Cardiff in April. The new imagery is much less well orthorectified than the old and is considerable more oblique. This made the process of mapping & tagging panels rather more tedious, and meant that many more installations needed close attention.
• Well-mapped areas needed more time. The councils which were already at 60% (basically along the N. Wales coast and Wrexham) took rather longer to work though than I anticipated. Similarly earlier work by brianboru, Gregory Williams, ZenPhil, and myself often required not just adding module counts & direction, but repositioning.

However, I did reach my target about a week ago, just dipping into August, making 8 months altogether.

Apart from being a country in it’s own right, Wales also encompasses a very broad range of urban and rural areas from dense cities and post-industrial towns to sparsely populated valleys with a few scattered farms in upland areas. It’s therefore a decent microcosm for mapping solar elsewhere in Europe.

Next Steps

Of course there is still a lot to do with the Welsh data. Some basic tidying up will be the necessary over the next couple of months;

• Convert any remaining larger solar mapped as nodes to areas.
• Ensure all larger rooftop solar has a building underneath it.
• Review some LSOAs where less than 70% of solar has been captured. Often imagery has been updated.
• Split or add buildings where the algorithm used by Gregory to calculate installations either under or over counts installations. (I have had to do a lot of work with the Terracer plugin in Wrexham for this reason).

Other people are mapping buildings, for instance around Merthyr & Rhondda Cynon Taf, so gradually this mapping will also improve solar by improving the level of detail, but ultimately remaining buildings will need to be added, so this may be the next major project.

Further afield, the use of clustering of buildings can be used anywhere that OSM has a decent number of buildings: the OSM-IE buildings project & OSM-FR mapping from cadastre both offer opportunities to improve solar mapping now.

Appendix: Some Terminology

Comprehensive vs. complete

I use the term comprehensive rather than complete. We can rarely claim any subset of OSM data is complete, but it is not unusual for the data to reach a level where it is quasi-complete (i.e., perhaps missing a small number of features, and requiring some updating). Limitations to achieving true completeness are : continual change; use of aerial imagery which is a few months to a few years old; difficulty of interpretation of imagery; ordinary omissions & commissions which occur in mapping (fortunately much less than 1% in this case). Good examples are the highway network, where typically we miss new residential developments and may not update changes in one-way systems., or mapping shops where change is continual.

Wrexham : a comparison of how relative complete mapping (80%) compares with comprehensive mapping (at 96%). Note greater dispersion of elements showing a more successful search approach.

My initial target for comprehensive mapping of solar was to achieve at least 80% of the known installed capacity, but with one or two exceptions well over 90% has been reached. Continued mapping & roll-out of new imagery, and even a bit of on-the-ground surveying can then incrementally increase this (as can be seen in places like Watford & Canterbury).

Level of Detail for rooftop solar

In terms of Level of Detail (LOD), there are perhaps 5 stages of mapping rooftop solar:

1. A node tagged with location, locational accuracy perhaps 10 m root-mean-square (rms). (This proved the most practical LOD for all our initial mapping. Vagaries of aerial imagery quality, alignment & recency made it too hard to do extra detail, plus the optimum zoom for finding solar does not resolve detail
2. A node with additional tags for direction & module count ( a proxy for power rating), but also more accurately located (5 m rms). An area without these detailed tags is equivalent
3. A level 2 node with an underlying building, ideally topologically accurate, but certainly slightly more accurate location (rms 2.5 - 3 m). A specific issue is that in some places building locational accuracy itself is in the order of 3-4 m rms ( buildings had been mapped before the cadastral data was available to improve alignment)
4. As for level 3, but converted to an area, building alignment refined.
5. As for level 4, but the underlying building is mapped with S3DB.

My goal for Wales in the current project was to have everything mapped to stage 2, and all ground and larger rooftop installations (more than 30-40 modules) at stage 4. This will still leave many thousands of buildings to map to further improve the level of detail for ongoing work.

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Thanks and please help

Dear all,

Today, v5.6.1 of the OpenStreetMap Carto stylesheet (the default stylesheet on the OSM website) has been released. Once changes are deployed on the openstreetmap.org it will take couple of days before all tiles show the new rendering.

Changes include

• Fixing rendering of water bodies on zooms 0 to 4

Thanks to all the contributors for this release.

For a full list of commits, see https://github.com/gravitystorm/openstreetmap-carto/compare/v5.6.0…v5.6.1

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

I’m adding missing building with RapiD in Irvine, California:

As you can see, there’s existing nodes tagged with building=yes and address info, imported at some point from city data it looks like:

When I add a building with RapiD, it adds the following tags:

I’m inclined to do the following:

1. copy the tags from the node
2. paste them on the new building way
3. delete the node

question: is this the preferred way to go about this?

By the way, lots of buildings to add in this area, please help!

There’s also a couple of new MapRoulette challenges that deal with buildings tagged as nodes. They’re pretty big!

1. building nodes inside building ways challenge
2. lone building nodes challenge

happy mapping!

Ma méthode pour mettre à jour le cadastre FR sous JOSM

Certainement pas la meilleure méthode, mais permet de mettre à jour efficacement un cadastre avec les derniers bâtiments.

Utilisation de JOSM.

Installer le coloriage “cadastre”

Télécharger le rendu CSS que j’utilise via cette URL : https://data.tedomum.net/s/GWcAyNHWjP3oSA9

Ce rendu CSS permet de colorier en rouge tous les polygones qui ont un attribut “source” contenant le texte “cadastre-dgi-fr source : Direction Générale des Finances Publiques - Cadastre. Mise à jour : XXXX”, où XXXX correspond à l’année (pour l’instant jusqu’en 2020). Pensez à le télécharger et le déposer dans son répertoire final avant de l’intégrer à JOSM.

Ouvez JOSM, puis rendez-vous dans le menu “Éditions / Préférences” puis cliquez sur la rubrique “Coloriage”, cliquez sur le “+” à gauche pour créer un nouveau coloriage :

Allez chercher votre fichier puis donner un nom à ce coloriage :

Faire valider, puis une seconde fois valider, votre coloriage est maintenant activé, vous pouvez voir ici la différence entre le coloriage activé ou non :

Préparer votre commune à mettre à jour

On peut maintenant préparer notre mise à jour du cadastre ! Sur JOSM, téléchargez, la commune à mettre à jour puis récupérer la dernière version du cadastre afin d’ouvrir le fichier comme un second calque sur JOSM :

Pensez maintenant à mettre en calque principale le calque du cadastre !

Chercher les nouveaux bâtiments

Il s’agit de la partie la plus fastidieuse : explorez la commune afin de repérer les polygones qui ne sont pas colorés. Vous pouvez alors basculer sur le calque OSM pour vérifier si le polygone est vraiment absent :

(À droite, quand le calque OSM est sélectionné, à gauche, quand le calque cadastre est sélectionné).

Toujours depuis le calque cadastre, sélectionnez les bâtiments à importer (aidez-vous de shift+clic pour en sélectionner plusieurs) :

Vous pouvez maintenant utiliser la combinaison CTRL+SHIFT+M afin de fusionner (merge) votre sélection sur le calque OSM :

Voilà ! Votre bâtiment se trouve sur le calque OSM, il ne reste plus qu’à l’importer dans OSM :

Astuce pour l’importation

Si vous travailler sur une grosse commune et que vous ne souhaitez importer que quelques nouveaux bâtiments, sélectionnez-les sur votre calque OSM avant de les envoyer sur OSM : cela vous évitera d’avoir l’a liste de toutes les erreurs et autres avertissements.

La suite

J’ai présenté ici l’importation de bâtiment “simple” sans trop de problème. Il me reste encore à traiter de :

• l’ajout extensions sur des bâtiments
• Les erreurs après importation (même sur des bâtiments neufs
• la gestion des conflits

ceci fera l’objet d’un second article.

.

Representing the federal government of traffic I would like to upload the 12 Routes of national cycling routes, representing the Cycle Network Germany. All data ist quality checked and free to use (Data licence Germany – attribution – version 2.0)

Looking for a contact to provide a boost for OpenCycleMap and OSM in general

Geodata is already published: https://www.radroutenplaner-deutschland.de/veraDaten_EN.asp

opened my map account on August 10, 2022.

How to appeal ban from OSM discord.

40度

K

JM CS

Wstęp

Podczas wizyty w Krynicy-Zdroju udałem się za drogowskazem znajdującym się na reklamie Ogrodów Żywiołów ponieważ zaciekawiła mnie ona wodnymi eksperymentami. Oczywiście strzałka na plakacie była mało precyzyjna a miejsce zupełnie nieoznaczone. Google mapsy nie potrafiły doprowadzić bezpośrednio do ogrodów. Na osm nawet nie było ich zaznaczonych. Pomocna okazała się opinia na google maps która sprecyzowała chociaż z której strony szukać wejścia. W lesie od strony ulicy Doktora Henryka Ebersa zauważyłem małe tabliczki z logiem parku które doprowadziły już na alejki ogrodów.

Jednym słowem o parku

Cóż. Pomysł na park fajny. Jednak z tego co przeczytałem w internecie został on otwarty wiosną 2022 roku a już niektóre eksperymenty są uszkodzone. Ludzi nie ma (pewnie poddają się szukając wejścia do parku).

Mapowanie

Zbieranie danych

Parku na osm nie ma to warto byłoby go zmapować. Sprawdziłem, że nie ma dostępnych zdjęć satelitarnych czy też lotniczych przedstawiających to miejsce. Przydałby się więc ślad GPS. Niestety ale OSMTracker strasznie niedokładnie rysował ślad oraz teleportował mnie po okolicy. StreetComplete ślad rysował lepiej ale co jakiś czas go przerywał. Nie chcąc tracić czasu na szukanie innych alternatyw postanowiłem, że będę w strategicznych punktach (aka zakręty ścieżek) dodawał uwagi z cyferkami w opisie i zdjęciami jeśli w danym miejscu okażą się pomocne. Do dodawania uwag użyłem StreetComplete. Szkoda tylko, że nie widać już dodanych uwag oraz, że nie można dodawać zdjęć zrobionych wcześniej. Po skończonym oznaczaniu potrzebnych mi punktów osm wyglądało tak: Dodałem też jedną uwagę ogólną aby w razie czego ktoś ją znalazł i nie myślał o mnie jak o malarzu uwagami po całej Polsce. Łącznie wyszło 55 uwag.

Faktyczne rysowanie

Włączam JOSM, pobieram obszar wraz z uwagami i zaczynam łączyć kropki kreskami jak kiedyś na analogowym papierze czy też jak w filmach agenci próbujący rozpracować wzory namalowane przez kosmitów na polach w Ameryce. Po zaznaczeniu wszystkich ścieżek i dodaniu każdej możliwej atrakcji wraz z podziałem na tematyczne obszary gdzie dzięki podpowiedzi @Catra na discordzie co do sposobu tagowania obszarów z podziałem na żywioły (place=locality raczej sprawdza się tutaj najlepiej) zaznaczyłem jakie ścieżki są dedykowane ludziom na wózkach inwalidzkich. Informacje o nich wziąłem z mapy znajdującej się w tym parku którą odkryłem pod koniec spamowania uwagami. Mapa ta pozwoliła mi również wytyczyć granice parku. Teraz czas na wysłanie zmian i sprawdzenie za jakiś czas jak wygląda wyrenderowana mapa.

Efekt końcowy

Na tej skali nie wygląda to super szczególnie, że nazwy atrakcji się zasłaniają. Na większej skali jednak widać podpisy wszystkich obiektów jak i również ławki oraz śmietniki. Przydałby się render leżaków.

Porównanie mapy przed i po edycji

Hello please help me i make three places and i need to delete them please help me please

Toronto Police Service has major, systemic problems. But they also have this one:

This is my hastily-georeferenced version of the image from this Toronto Police Operations tweet over OSM tiles in QGis. Note the extreme similarity between the streets and building detail. But also note the complete lack of OSM attribution on TPS’s map …

Sentieri CAI con criticità: http://micheleosm3.atwebpages.com/Map_Sentieri%20CAI%20con%20criticit%C3%A0_4.html

Class - Color - Title	Description	surface	smoothness	sac_scale	trail_visibility	Incline (peak rise/run)	MTB : YDS : NFS : AWTGS : IMBA
Class 0 - Grey - Accessible	“Experience, Gear, and Risk: No hiking experience required. Suitable for wheelchair users who have someone to assist them. No risk.”	“Asphalt, Chipseal, Fine Gravel, Paved, Concrete, paving_stones, Compacted, metal, wood”	“Excellent, Good, Intermediate”	N/A	Excellent	< 8%	MTB 0 : YDS NA : NFS C5 : AWTGS G1 : IMBA 0
	Surface and Visibility: Hardened or surfaced and clear.
	Obstacles: Flat even surface with no steps or steep sections. No obstacles present (<1”).
	Hands: Hands not required.
Class 1 - Green - Easy	“Experience, Gear, and Risk: Hiking on a well groomed trail. Athletic shoes sufficient. Map not needed. No Risk.”	“Sett, unhewn_cobblestone, cobblestone, stepping_stones, unpaved, gravel, pebblestone, grass, grass_paver, sand, woodchips (potentially unpaved, rock, ground, dirt, earth, mud)”	bad	hiking (T1)	Excellent	< 40%	MTB 1 : YDS Class 1 : NFS Class 4 & 3 : AWTGS G2 :IMBA 1
	Surface and Visibility: Surface hardened or compacted. Vegetation cleared outside trailway.
	Obstacles: May have a gentle hill section or sections and occasional steps or small obstacles such as roots and stones (<2”)
	Hands: Hands not required.
Class 2 - Blue - Moderate	“Experience, Gear, and Risk: Hiking on a trail. Hiking boots recommended. Limited potential for falling.”	“unpaved, rock, ground, dirt, earth, mud,”	“very_bad, horrible,”	mountain_hiking (T2)	good	<70%	MTB 2 : YDS Class 1: NFS Class 2 : AWTGS G3 : IMBA 2
	Surface and Visibility: Continuous established footpath. Vegetation may encroach trail. Partially steep terrain.
	Obstacles: Obstacles common (<8”). Steps and flat stairs. Short steep hill sections.
	Hands: Hands not required.
Class 3 - Red - Difficult	“Experience, Gear, and Risk: Simple scrambling. Average orientation/navigation skills. Sturdy hiking boots recommended. Some potential for falling encountered.”	Same as Class 2	very_horrible	demanding_mountain_hiking (T3)	intermediate	<100%	MTB 3 & 4 : YDS Class 2: NFS Class 1 : AWTGS G4 : IMBA 3
	“Surface and Visibility: Usually marked, but trail not always visible, and signage not always visible.”
	“Obstacles: Obstacles common including large boulders, logs, and large steps (<15”). exposed passages can be protected with cables, Steep grades”
	Hands: Hands required to maintain balance.
Class 4 - Black - Very Difficult	“Experience & Gear: Specialized hiking skills and gear, including navigation and emergency first aid. Weather exposed portions, rock ledges, fields, and slopes. Potential danger is encountered and fall protection may be recommended.”	Same as Class 2	impassable	alpine_hiking (T4)	bad	>100%	MTB 5 : YDS Class 2: NFS Class 1 : AWTGS G5 : IMBA 4
	Surface and Visibility: Tracks are likely to be very rough and unmarked.
	“Obstacles: Characterized by blocky terrain with counter climbs, scree fields and landslides, high steps and fallen trees and extremely steep grades.”
	Hands: Hands required to advance.
Class 5 - Orange - Expert	“Experience, Gear, and Risk: Scrambling with increased exposure. A rope could be carried. Good terrain assessment and orientation skills required. Falls could be fatal.”	rock	impassible	demanding_alpine_hiking (T5)	Horrible	» 100%	MTB 6 : YDS Class 3 : NFS N/A : AWTGS N/A : IMBA N/A
	Surface and Visibility: Occasionally pathless.
	Obstacles: Basic/easy climbing sections.
	Hands: Handholds necessary to bear weight.
Class 6 - White - Climbing	“Experience, Gear, and Risk: Basic to advanced climbing.”	rock	impassible	difficult_alpine_hiking (T6)			MTB 6 : YDS Class 4-5 : NFS N/A : IMBA N/A
	Surface and Visibility:
	Obstacles:
	Hands:

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