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Revue des autres blogs (non sélectionnés)

ค่ะ - sam 7 jui 2018 - 21:31


Subdivisions of Turkmenistan's Provinces - sam 7 jui 2018 - 20:25

I have been unable to find an authoritative official list of the districts (''etraplar'') in Turkmenistan's five provinces, so have cobbled together a list for each province based on whatever official press releases, news items, and other sources (including personal travel to some of them) I could find. The results have now been typed into the wiki pages for each of the provinces:

If anybody has corrections to any errors of omission or commission, please make them and let me know! These lists are accurate and up to date so far as I can tell, but nobody's perfect.

ติ้งต่อง - sam 7 jui 2018 - 20:25


weeklyOSM 415 - sam 7 jui 2018 - 17:14


Map Kibera moved to map Mbooni, a second ward in Makueni County, Kenya 1 | © Photo: Zack Muindre

  • With casinos as an example, Carlos Cámara started a discussion on the mailing list about the responsibility of OSM to render content on the map, that might be difficult from an ethical or social point of view.
  • Mateusz Konieczny proposes a mechanical edit to change FIXME=* to fixme=*. Both tags are in use, in a few cases on the same object with different values; however, the uppercase variant is less common and actually predates the lowercase one. The long discussion on Talk mailing list involves both the editing procedure and ways to prevent the uppercase variant to be used in the future.
  • Mateusz Konieczny asked the tagging mailing list about how to tag bakeries not selling bread or other bread-like products.
  • As you probably know Germans map a lot, but did you know that they delete a lot, too? A mapper complained that (among other tags) his bicycle=no on highway=footway was deleted without any notice. A discussion of reasons for or against adding default values was started but ended without an agreement.
  • Map Kibera moved to map Mbooni, a second ward in Makueni County, Kenya. They produced a video that shows the challenges they had to face, and the involvement of local representatives and volunteers.
  • The voting for the OSM Awards 2018 is now open.
  • The company Agisoft donated a license of Photoscan Professional that can be used to rectify aerial images. In case you have aerial images that needs to be stitched and rectified, you can contact user dktue.
  • dieterdreist wrote a diary entry regarding OSM Wiki edits and how a mechanism to evaluate them might be needed.
  • Facebook is recruiting mapping experts, reports CNBC. Analysing and improving OSM seems to be one of the purposes according to the job offer.
  • Nathalie Sidibé published a blog post about her participation to the workshop that was held last month in Togo to “train the trainers” from West African OSM Communities with the support of the International Organization of Francophonie (OIF). An article about it was published in WeeklyOSM #410.
  • Nicolas Chavent publishes a Twitter moment about two weeks of OSM and free Geomatics training in Antananarivo (Madagascar) targeted to the local OSM community and stakeholders from the research, development and humanitarian fields. The courses were facilitated with the help of members of Magalasy community and an Ivorian mapper with the support of the International Organization of Francophonie (OIF).
OpenStreetMap Foundation
  • Grab, a technology company in Singapore providing ride-hailing, ride sharing and logistics services, is now a Gold Corporate Member of the OSM Foundation.
  • The FOSS4G Belgium 2018 call for papers is open for submission until September 4th.
  • Registration for SotM Japan 2018 in Tokyo is open and the program has been published .
Humanitarian OSM
  • Satoshi IIDA asks for mappers and validators after the Osaka earthquake. This is the task.
  • Many mappers are surprised when they stumble onto the low-quality mapping in Africa or Asia. This often relates to HOT mapping activity. Jean-Marc Liotier has asked “Why the HOT obsession with low-quality buildings in Africa?”. (picture one, picture two). Another mapper pointed out that buildings serve as proxy for the local population. The question why low quality buildings or thousands of duplicate buildings dumped to OSM rather than counted somewhere else is still not answered. To address the known issues a duplicate building validator was recently added to HOTOSM fork of OSM Lint, a validator to identify geometry and metadata problems.
  • Using lessons learnt in health mapping in Dar es Salaam, a team from HOT Tanzania have been working on the latest Data Zetu project in Mbyea, which is in the southwest of Tanzania. The team have been training and working with the local community in mapping local administrative boundaries and collecting data about access to health services.
  • In HOTOSM News there is a writeup of new hardware demonstrated at the recent Amazon Web Services Public Sector Summit. This box is a fully offline, 100TB storage, field-deployable version of OpenAerialMap and Portable OpenStreetMap for offline mapping for disaster areas.
  • Wikimedia wikis can now include interactive and embeddable maps presented in the reader’s language. OSM data is used, but wikidata is not.
Open Data
  • Microsoft released 125 million building footprints in the United States to OSM. The resulting data and source code to detect buildings from the imagery is available on GitHub. See this thread for a wider discussion on the imports list.
  • User miurahr sent a pull request adding 360 degree panorama photo viewer mode to JOSM ‘s Mapillary plugin.
  • Hyperloop, a project that aims solving performance problems for web applications, made a publication available about how to avoid performance bugs in database-backed web applications and how to solve common issues. A minor suggestion was made with regards to OSM’s database design.
  • The stable JOSM version 13996 (milestone 18.06) has been released. Major enhancements are performance improvements while dragging the map by hiding labels and a new keyboard shortcut to switch layers. The new version closed issues from 42 tickets and came with a lot of other improvements.
Other “geo” things
  • Apple Maps is being rebuilt, reports TechCrunch.
  • BikeRadar reviewed the Garmin Edge 520 Plus cycling computer.
  • The Daimler subsidiary Moovel published a visualisation called Flights to Rome, showing the global road and flight network based on data from OSM and Flightradar24.
  • Engadget reported that Niantic is opening its AR platform (used by “Pokemon Go”, and soon to be used by “Harry Potter: Wizards Unite”) to third party developers.
  • Space News reports that Planet and Airbus Defence and Space’s geospatial division have agreed to co-develop imagery products that leverage both companies’ satellites. Planet operates more than 150 satellites, mainly Cubesats, with a resolution of 3 to 5 meters and five former RapidEye (5m) and 13 former SkySat satellites (90cm). Some of Planet’s images are provided under CC-BY-NC for non-commercial use (incompatible with OSM).
  • Tank & Rast, an operator of motorway service stations, and IONITY opened the first so called High Power Charging station to decrease the charging time of cars with electric drives. According to ABB, the 350KW that the new stations are able to provide, would allow to charge 200 km of range in 8 minutes.
Upcoming Events Where What When Country Ise 世古をマッピング!:伊勢マッピングパーティ 2018-07-07 Lyon Rencontre mensuelle pour tous 2018-07-10 Munich Münchner Stammtisch 2018-07-10 Cologne Köln Stammtisch 2018-07-11 Berlin 121. Berlin-Brandenburg Stammtisch 2018-07-12 Sakai 厄払い!オープンデータソン in さかい 2018-07-14 Cologne Bonn Airport Bonner Stammtisch 2018-07-17 Lüneburg Lüneburger Mappertreffen 2018-07-17 Moscow Schemotechnika 17 2018-07-17 Karlsruhe Stammtisch 2018-07-18 Mumble Creek OpenStreetMap Foundation public board meeting 2018-07-19 Essen Mappertreffen 2018-07-21 Tokyo 東京!街歩き!マッピングパーティ:第21回 増上寺 2018-07-21 Greater Manchester More Joy Diversion 2018-07-21 Nottingham Pub Meetup 2018-07-24 Dusseldorf Stammtisch 2018-07-25 Lübeck Lübecker Mappertreffen 2018-07-26 Milan State of the Map 2018 (international conference) 2018-07-28-2018-07-30 Dar es Salaam FOSS4G & HOT Summit 2018 2018-08-29-2018-08-31 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 (effective date to confirm) 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 Anne Ghisla, Nakaner, PierZen, Polyglot, Rogehm, Softgrow, SomeoneElse, Spec80, SunCobalt, YoViajo, derFred, jinalfoflia, k_zoar, sev_osm.

Garage sales - sam 7 jui 2018 - 8:50

Nothing today

Introducing MapRoulette 3 - ven 6 jui 2018 - 20:32

This and future diary posts also appear on my blog.

MapRoulette lets anyone contribute to OpenStreetMap by fixing small mistakes on the map. It works like a roulette wheel: once you select something you want to work on, MapRoulette will give you a random task to work on. Once you fix it, you return to MapRoulette for the next task. Do as few or as many as you like. Be careful, people have been known to get hooked on it!

Since 2013, MapRoulette has been used by thousands of mappers to complete well over 1.5 million small tasks to improve OpenStreetMap. We have put all the feedback we have received and lessons we learned into the latest version: MapRoulette v3.

In a series of posts, I will highlight some great new features of this new version. This is the first post. Enjoy!


I heard from some mappers that they couldn't find anything interesting to work on. We spent a lot of time making that easier, by adding different filters.


You can quickly narrow down the list of available Challenges by zooming and panning the map, and selecting 'Within Map Bounds' or 'Intersecting Map Bounds' from the 'Location' dropdown menu. 'Within Map Bounds' will only show you Challenges that only have tasks within the current map view. 'Intersecting Map Bounds' will also show you Challenges that have some, but not all, their Tasks in the current map view. Additionally, you can narrow down the list to Challenges near your current location. This requires allowing browser access to your location.


Challenges are now grouped into Categories, like 'Roads / Pedestrian / Cycleways', or 'Buildings'. You can filter the list of available Challenges by selecting a Category from the 'Work on' dropdown menu.


You can enter any text in the search box, and MapRoulette will only show you the Challenges that have a name or description containing that text.

You can clear any filters you have set with using 'Clear Filters'.

Stay tuned for future posts about the all new MapRoulette 3! I hope you will give it a(nother) try and let me know what you think.

High School Geographers - ven 6 jui 2018 - 17:51

Students from Forsyth Central High School utilizing their geography skills and creating maps through their travel in Europe. The students utilized their gps watches to construct these maps.

/Users/michaelmeyer/Pictures/Photos Library.photoslibrary/Thumbnails/2016/06/23/20160623-143505/j4E7HIzLRamNBnnJsdsMBA/thumb_IMG_3640_1024.jpg

Redland Property Services - ven 6 jui 2018 - 14:28

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Моря коричневых квадратиков - ven 6 jui 2018 - 7:10

Вы, наверное, слышали об инициативах HOT, особенно о серии картовстреч Missing Maps. Добровольцев сажают за JOSM и учат обклацывать домики в Центральной Африке. Результатом становятся сотни тысяч контуров с building=yes, которые аналитики используют для планирования акций помощи.

Jean-Marc Liotier намедни задал вопрос, столько неожиданный, сколько и очевидный: а зачем это всё? Добровольцы оставляют жутко неточную карту, на которую опытный осмер без слёз не взглянет. Дороги недорисованы, POI отсутствуют, будущим африканским мапперам будет проще удалить это всё и начать с нуля.

Зачем вообще рисовать контуры? Для анализа населённости достаточно нарисовать полигон landuse=residential с атрибутом плотности. На крайняк — вместо контуров ставить в центре домиков точки со всеми нужными тегами. У аналитиков будет способ посчитать население, а карта не пострадает. Зачем тратить время и силы ценного человеческого ресурса на работу, которую осмеры захотят удалить?

Для зарождающегося африканского сообщества эти недокарты вообще мина: здесь не кривые линии дорог нужно выправлять, как после импорта TIGER американцам, здесь каждый домик нужно скрупулёзно передвигать на правильное место. Любой, кто этим занимался, схватится за голову.

В рассылках вопрос поддержали опытные осмеры, а члены HOT ответили, но как-то не по существу. Про пользу да про валидаторов, которых всегда не хватает, потому что это не так интересно. Нет вопроса о пользе карты для кого-то, кроме аналитиков HOT, в справочнике команды. Никто не подумал. Проблемы не белых людей.

Один из вариантов ответа: встречи Missing Maps — отличный способ завербовать далёких от картографии людей в OpenStreetMap. Научить их редактированию и показать осязаемую пользу от работы. Да, качество их домиков будет ниже плинтуса, но если мы берём за цель их участие в сообществе, то то, что они нарисуют, можно удалить. Главное, что человек завяз.

Работа гуманитарной команды поднимает сразу несколько вопросов. Например, чем сотни тысяч нарисованных добровольцами домиков лучше автоматического импорта домиков из сторонней базы (тут как раз Bing пожертвовал 125 миллионов)? Наши правила запрещают массовые правки без обсуждения, но здесь же живые люди обклацывают снимки. Да, качество так себе, да, придётся большую часть удалить. С другой стороны, сообщество не приемлет автоматические правки и импорты. Главный аргумент — потому что данные из сторонних источников всегда уступают тёплым ламповым нарисованным вручную домикам.

Впрочем, организованное редактирование скоро тоже зарегулируют новым регламентом. Он гласит, что если вы собрались больше трёх, то посчитаем это импортом: косо посмотрим, сплюнем, спросим за район.

Почему появился этот регламент? Некоторые компании начали платить мапперам за улучшение карт густо населённых районов, и осмеры из этих районов возмутились. Импорты и организованное редактирование никого не волнуют, пока не нарушают главное правило: только не на моём участке. Стоит задеть территорию опытного осмера, как узнаешь про много разных правил и ограничений. Классы дорог не трожь, используй contact:website вместо website, каждый POI должен быть проверен на местности и подтверждён нотариально заверенной фотографией. Правило «Не на моём участке» в сообществе идёт первым, до лицензии и проверяемости. Гуманитарной команде позволяют работать в Африке только потому, что там нет увлечённых мапперов, которые от очередного набега придут в ужас.

Наконец, проблема сотен тысяч домиков поднимает через Жана-Марка тот же вопрос, какой должен задавать себе каждый из нас: зачем мы картируем? Чем мы руководствуемся при выборе тегов и объектов для картирования, и какой конечный результат покажет нам, что мы выполнили свою работу хорошо? Линия на стандартном картостиле? Слово «МакАвто» в результатах поиска на смартфоне? Размер файла с выгрузкой города? Числа в валидаторе? Какой бы ни была ваша метрика, не бойтесь поделиться ею на форуме или в чатике: вам не только помогут найти лучшие источники и модули редакторов для работы, но и объяснят, почему ваша работа не имеет смысла. Мы всегда рады помочь.

RoboSat ❤️ Tanzania - jeu 5 jui 2018 - 22:00

Recently at Mapbox we open sourced RoboSat our end-to-end pipeline for feature extraction from aerial and satellite imagery. In the following I will show you how to run the full RoboSat pipeline on your own imagery using drone imagery from the OpenAerialMap project in the area of Tanzania as an example.


For this step by step guide let's extract buildings in the area around Dar es Salaam and Zanzibar. I encourage you to check out the amazing Zanzibar Mapping Initiative and OpenAerialMap for context around the drone imagery and where these projects are heading.

High-level steps

To extract buildings from drone imagery we need to run the RoboSat pipeline consisting of

  • data preparation: creating a dataset for training feature extraction models
  • training and modeling: segmentation models for feature extraction in images
  • post-processing: turning segmentation results into cleaned and simple geometries

I will first walk you through creating a dataset based on drone imagery available on OpenAerialMap and corresponding building masks bootstrapped from OpenStreetMap geometries. Then I will show you how to train the RoboSat segmentation model to spot buildings in new drone imagery. And in the last step I will show you how to use the trained model to predict simplified polygons for detected buildings not yet mapped in OpenStreetMap.

Data Preparation

The Zanzibar Mapping Initiative provides their drone imagery through OpenAerialMap.

Here is a map where you can manually navigate this imagery.

To train RoboSat's segmentation model we need a dataset consisting of Slippy Map tiles for drone images and corresponding building masks. You can think of these masks are binary images which are zero where there is no building and one for building areas.

Let's give it a try for Dar es Salaam and Zanzibar, fetching a bounding box to start with.

Let's start with extracting building geometries from OpenStreetMap and figuring out where we need drone imagery for training dataset. To do this we need to cut out the area we are interested in from OpenStreetMap.

Our friends over at GeoFabrik provide convenient and up-to-date extracts we can work with. The osmium-tool then allows us to cut out the area we are interested in.

wget --limit-rate=1M osmium extract --bbox '38.9410400390625,-7.0545565715284955,39.70458984374999,-5.711646879515092' tanzania-latest.osm.pbf --output map.osm.pbf

Perfect, now we have a map.osm.pbf for Dar es Salaam and Zanzibar to extract building geometries from!

RoboSat comes with a tool rs extract to extract geometries from an OpenStreetMap base map.

rs extract --type building map.osm.pbf buildings.geojson

Now that we have a buildings.geojson with building geometries we need to generate all Slippy Map tiles which have buildings in them. For buildings zoom level 19 or 20 seems reasonable.

rs cover --zoom 20 buildings.geojson buildings.tiles

Based on the generated buildings.tiles file we then can

  • download drone imagery tiles from OpenAerialMap, and
  • rasterize the OpenStreetMap geometries into corresponding mask tiles

Here is a preview of what we want to generate and train the segmentation model on.

If you look closely you will notice the masks are not always perfect. Because we will train our model on thousands of images and masks, a slightly noisy dataset will still work fine.

The easiest way for us to create the drone image tiles is through the OpenAerialMap API. We can use its /meta endpoint to query all available drone images within a specific area.

http ',-7.0545565715284955,39.70458984374999,-5.711646879515092'

The response is a JSON array with metadata for all drone imagery within this bounding box. We can filter these responses with jq by their attributes, e.g. by acquisition date or by user name.

jq '.results[] | select( == "ZANZIBAR MAPPING INITIATIVE") | {user:, date: .acquisition_start, uuid: .uuid}'

Which will give us one JSON object per geo-referenced and stitched GeoTIFF image.

{ "user": "ZANZIBAR MAPPING INITIATIVE", "date": "2017-06-07T00:00:00.000Z", "uuid": "" }

Now we have two options

  • download the GeoTIFFs and cut out the tiles where there are buildings, or
  • query the OpenAerialMap API's Slippy Map endpoint for the tiles directly

We can tile the GeoTIFFs with a small tool on top of rasterio and rio-tiler. Or for the second option we can download the tiles directly from the OpenAerialMap Slippy Map endpoints (changing the uuids).

rs download{z}/{x}/{y}.png building.tiles

Note: OpenAerialMap provides multiple Slippy Map endpoints, one for every GeoTIFF.

In both cases the result is the same: a Slippy Map directory with drone image tiles of size 256x256 (by default; you can run the pipeline with 512x512 images for some efficiency gains, too).

To create the corresponding masks we can use the extracted building geometries and the list of tiles they cover to rasterize image tiles.

rs rasterize --dataset dataset-building.toml --zoom 20 --size 256 buildings.geojson buildings.tiles masks

Before rasterizing we need to create a dataset-building.toml; have a look at the parking lot config RoboSat comes with and change the tile size to 256 and the classes to background and building (we only support binary models right now). Other configuration values are not needed right now and we will come back to it later.

With downloaded drone imagery and rasterized corresponding masks, our dataset is ready!

Training and modeling

The RoboSat segmentation model is a fully convolutional neural net which we will train on pairs of drone images and corresponding masks. To make sure these models can generalize to images never seen before we need to split our dataset into:

  • a training dataset on which we train the model on
  • a validation dataset on which we calculate metrics on after training
  • a hold-out evaluation dataset if you want to do hyper-parameter tuning

The recommended ratio is roughly 80/10/10 but feel free to change that slightly.

We can randomly shuffle our building.tiles, split it into three files according to our ratio, and use rs subset to split the Slippy Map directories.

rs subset images validation.tiles dataset/validation/images rs subset masks validation.tiles dataset/validation/labels

Repeat for training and evaluation.

Before training the model we need to calculate the class distribution since background and building pixels are not evenly distributed in our images.

rs weights --dataset dataset-building.toml

Save the weights in the dataset configuration file, which training will then pick up. We can now adapt the model configuration file, e.g. enabling GPUs (CUDA) and then start training.

rs train --model model-unet.toml --dataset dataset-building.toml

For each epoch the training process saves the current model checkpoint and a history showing you the training and validation loss and metrics. We can pick the best model, saving its checkpoint, looking at the validation plots.

Using a saved checkpoint allows us to predict segmentation probabilities for every pixel in an image. These segmentation probabilities indicate how likely each pixel is background or building. We can then turn these probabilities into discrete segmentation masks.

rs predict --tile_size 256 --model model-unet.toml --dataset dataset-building.toml --checkpoint checkpoint-00038-of-00050.pth images segmentation-probabilities rs masks segmentation-masks segmentation-probabilities

Note: both rs predict as well as rs mask transform Slippy Map directories and create .png files with a color palette attached for visual inspection.

These Slippy Map directories can be served via an HTTP server and then visualized directly in a map raster layer. We also provide an on-demand tile server with rs serve to do the segmentation on the fly; it's neither efficient nor handles post-processig (tile boundaries, de-noising, vectorization, simplification) and should only be used for debugging purpose.

If you manually check the predictions you will probably notice

  • the segmentation masks already look okay'ish for buildings
  • there are false positives where we predict buildings but there is none

The false positives are due to how we created the dataset: we bootstrapped a dataset based on tiles with buildings in them. Even though these tiles have some background pixels they won't contain enough background (so called negative samples) to properly learn what is not a building. If we never showed the model a single image of water it has a hard time classifying it as background.

There are two ways for us to approach this problem:

  • add many randomly sampled background tiles to the training set, re-compute class distribution weights, then train again, or
  • use the model we trained on the bootstrapped dataset and predict on tiles where we know there are no buildings; if the model tells us there is a building put these tiles into the dataset with an all-background mask, then train again

The second option is called "hard-negative mining" and allows us to come up with negative images which contribute most to the model learning about background tiles. We recommend this approach if you want a small, clean, and solid dataset and care about short training time.

For hard-negative mining we can randomly sample tiles which are not in building.tiles and predict on them with our trained model. Then make use of the rs compare tool to create images visualizing the images without buildings in them and the prediction next to it.

rs compare visualizations images segmentation-masks

After making sure these are really background images and not just unmapped buildings in OpenStreetMap, we can put the negative samples into our dataset with a corresponding all-background mask. Then run rs weights again, update the dataset config, and re-train.

It is common to do a couple rounds of hard-negative mining and re-training, resulting in a solid and small dataset which helps the model most for learning.

Congratulations, you now have a solid model ready for prediction!

Here are the segmentation probabilities I got out after spending a few hours of hard negative mining.

Interesting to see here is the model is not entirely sure about building construction sites. It is on us to make an explicit decision when creating the dataset and when doing hard-negative mining: do we want to include building construction sites or not.

These edge-cases occur with all features and make up the boundaries of your feature’s visual appearance. Are house-boats still buildings? Are parking lots without parking aisle lane markings still parking lots? Make a call and be consistent.

Finally, the post-processing steps are responsible for turning the segmentation masks into simplified and vectorized GeoJSON features potentially spanning multiple tiles. We also tools for de-duplicating detections against OpenStreetMap to filter out already mapped features.

I won’t go into post-processing details in this guide since the segmentation masks based on this small training dataset are still a bit rough to make it properly work well and the RoboSat post-processing is currently tuned to parking lots on zoom level 18 and I had to make some in-place adaptions when running this out.


In this step-by-step guide we walked through the RoboSat pipeline from creating a dataset, to training the segmentation model, to predicting buildings in drone imagery. All tools and datasets used in this guide are open source and openly available, respectively.

Give it a try!

I'm happy to hear your feedback, ideas and use-cases either here, on a ticket, or by mail. Announcing a new Mastodon instance for OSM (en) - jeu 5 jui 2018 - 8:58

I've set up a Mastodon instance for OSM (english speaking)! - OpenStreetMap Mastodon

Mastodon is an open source, federated micro-blogging system, with more than a million users. Basically open source twitter, spread across many servers. This is a new instance/server focused on OpenStreetMap (there's already for francophone OSMers). Like email, this server ("instance") talks to other servers, so anyone on the "fediverse" can follow and interact with anyone on this server & vice versa. The "local timeline" will only show toots (= tweets) from everyone on the server, so will be full of OSM related stuff.

Let's ditch twitter for something open, and under our control! No adverts, no analytics, no "algorithmic" promoted tweets. 500 characters! Let's use mapstodon instead!

I'm mirroring some twitter accounts to it. I can turn them over the appropriate people, or continue to auto post from twitter. I'll create more mirror accounts later.

More on Mastodon:

Follow me on mapstadon:

New demo for changeset upload available - mer 4 jui 2018 - 21:10

the changeset upload is known to take quite some time for larger changesets, sometimes resulting in timeouts and unclear overall status. Luckily, JOSM already covers some of those issues via its chunked upload, still there's always room for improvement.

Since a few days now, a new changeset upload implementation to address those issues is available for testing. That's a good opportunity to check if everything is still working as expected. Overall, the new implementation should be a bit more resilient towards uploading large changesets in one go.

Apps like JOSM frequently use regular expressions to analyze error messages. Special care has been taken to make sure this mechanism still works as before. Also, all HTTP return codes should be identical.

Steps to start editing on the demo server
  1. Create a new user on the demo server:
  2. Wait up to 5 minutes, until the user has been auto-confirmed by the system. A dedicated confirmation email will not be sent out and is not required.
  3. Log on to the site
  4. Start testing in iD, Potlatch, or JOSM (see below)


Special instructions for OAuth set up in JOSM Note: configuring JOSM to point to the demo server is an advanced topic and not suited for beginners.

For JOSM, all relevant instructions are outlined on the following user page

OAuth is mandatory at this time.

If you encounter any issues with the new implementation, feel free to send me a message or leave a comment on the respective Github issue

Happy testing!

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Bing Streetside imagery now available in OpenStreetMap iD editor - mer 4 jui 2018 - 0:10

Interactive Bing Streetside viewer embedded in the iD editor © CC-BY-SA

We are excited to announce that you can now use Bing Streetside photographs when you edit OpenStreetMap using the web-based editor iD! This is the same imagery currently visible on Bing Maps. You can activate the Bing Streetside layer in iD by opening the Map Data pane (shortcut F). The new layer provides 360-degree panoramic imagery across large regions of the United States, United Kingdom, France, and Spain. The massive imagery dataset covers approximately 1.6 million kilometers and takes nearly 5PB of storage! Thank you, Microsoft.

Go on – try it!

Other street-level imagery datasets in iD
This street-level imagery dataset in an addition to the existing ones provided in iD by OpenStreetCam and Mapillary, which you can also activate by opening the Map Data pane (shortcut F).

If you find street photography helpful for OpenStreetMap editing, you can also contribute your own photographs, using the Mapillary and OpenStreetCam smartphone applications. These are developed by companies independent from the OpenStreetMap Foundation.

A reminder about photomapping
Are you a new mapper excited about photomapping? Please remember that on-the-ground survey is always superior, as photographs represent a specific time snapshot. Feel free to improve the map using photographs, just keep in mind that the photos might be old. Before changing someone else’s edits, consider contacting the mapper first.

Street-level imagery in other OSM editors
Street-level photographs are also available for improving the map in other popular OpenStreetMap editors, such as JOSM. The Bing Streetside imagery will probably become available in some of these editors soon, so stay tuned!

Happy mapping!

About iD
The iD map editor is an open source project. You can submit bug reports, help out, or learn more by visiting the project page on GitHub.

Mapbox Satellite - lun 2 jui 2018 - 22:08

I was using the default Bing maps satellite imagery, until I found "Background Settings" (B) on the right toolbar. The Mapbox Satellite imagery is much more clear; however, the alignment can be slightly different for roads and buildings, compared with the default Bing maps imagery. I don't want go back to using lower quality imagery, which makes tracing more difficult, so I hope people will join me and use Mapbox.

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Wochennotiz Nr. 414 - lun 2 jui 2018 - 13:53


Darstellung von Biergärten in Deutschland 1 | © Nikolai Janakiev, Geodaten © OpenStreetMap-Mitwirkende, ODbL

  • Jyri-Petteri Paloposki schlägt aeroway=highway_strip für Landebahnen bzw. „Autobahn-Behelfsflugplätze“ vor, die normalerweise als Autobahn genutzt werden, aber entweder für eine militärische Übung oder eine Notlandung geschlossen werden können. Das Proposal findet man im Wiki.
  • Ihr wolltet schon immer ein besseres Schema, um Rennstrecken zu kartieren? Diese Tagging-Abstimmung könnte für Euch interessant sein.
  • Der Vorschlag für eine detailliertere Kennzeichnung von Saunen und heißen Bädern führte zu einer internationalen Diskussion auf der Tagging-Mailingliste. Die Abstimmung im Wiki ist seit dem 25. Juni geöffnet und endet am 9. Juli.
  • Die Schweizer Community einigt sich auf der Mailing-Liste relativ schnell darauf, den Hütten-Betreiber SAC/CAS als Namenszusatz zu entfernen und als operator=* zu erfassen.
  • Indoor- und Untergrund-Kartierung ist kompliziert. Im Forum bittet User N76 um Hilfe. Als Mappinghilfe gibt es eine speziell gerenderte Indoorkarte und eine umfangreiche Wiki-Seite.
  • Die umstrittenen Pläne der EU-Kommission für Inhaltsfilter im Internet schlagen auch große Wellen bei der OSM-Community. Proteste gibt es auch in der Politik. Diskussionen in der Mailingliste Talk-de sowie im Forum zeigen sehr große Bedenken gegen diese Pläne auf, die zu einer Protesthandlung der OSM-Community führen kann, die aktuell diskutiert wird.
    Außerdem kann man sich an einer Online-Petition beteiligen.
  • OSM US sucht immer noch nach einem Geschäftsführer. (wir berichteten)
  • Datendelphin schlägt vor, ein Symbol für die OSMF-Mitgliedschaft auf den Benutzerseiten hinzuzufügen. Die technischen Details werden auf GitHub diskutiert.
  • Der 60.000.000. Änderungssatz wurde am 20. Juni 2018 vom Benutzer mkačer hochgeladen. Die Nummer 50.000.000 wurde vor fast einem Jahr am 3. Juli 2017 hochgeladen.
  • Heather Leson veröffentlichte einen Blogbeitrag, der nach Erfahrungen mit Diversity-Themen in der OSM-Community fragt. Heather Leson wird zusammen mit Kate Chapman einen Workshop zu diesem Thema auf der State of the Map in Mailand (28. – 30. Juli 2018) halten. Es gibt eine Reihe von kontroversen Kommentaren unter ihrem Blogbeitrag.
  • Auf der Mailingliste Tagging wird vorgeschlagen und diskutiert (1, 2), die Taggingvorlagen vom Editor iD abzutrennen und als separates Projekt zu entwickeln. Die Befürworter stören sich daran, dass die Entscheidungen von einem einzelnen Entwickler getroffen werden.
  • User mapeadora veröffentlichte einen Blog-Post über die Wahrnehmung der Geschlechtergleichstellung im OSM-Umfeld. Während die endgültigen Ergebnisse einer Umfrage zu diesem Thema später auf der SotM in Mailand veröffentlicht werden, hat sie bereits einige vorläufige Ergebnisse veröffentlicht. 33 Männer und 24 Frauen aus 14 Ländern nahmen an der Umfrage teil. Wer möchte, dass diese Umfrage relevanter wird, kann die Umfrage in 7 Sprachen beantworten.
  • The International Journal of Geo-Information veröffentlichte das Paper „Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics“.
  • Nach einem langen Prozess hat das Team von uMap Latam den uMap-Dienst erfolgreich migriert und aktualisiert, ohne dass es zu einem Verlust oder der Notwendigkeit zur manuellen Migration der Karten bei UMAP Latam kam. Diese Instanz von uMap enthält mehr als 1400 Karten für Lateinamerika und die Welt.
OpenStreetMap Foundation
  • Weil in der vorläufigen Tagesordnung der OSMF-Vorstandssitzung am 21. Juni ein nicht öffentlicher Tagesordnungspunkt zur Diskussion von Regeln über die Aufnahme von Regionalvertretungen enthalten war, bittet Michael Reichert den Vorstand darum, diese Kriterien mit den Mitgliedern zu diskutieren, bevor sie beschlossen werden.
  • Nakaner hat im deutschen Forum ein inoffizielles, deutschsprachiges Protokoll des öffentlichen Teils der OSMF-Vorstandssitzung vom 21. Juni veröffentlicht.
  • Das Protokoll der Sitzung der License Working Group vom 14. Juni ist veröffentlicht worden. Diskutiert wurden u.a. die neuen Nutzungsbedingungen für
  • Martin Koppenhoefer beschwert sich auf der Mailingliste OSMF-Talk im Rahmen einer Diskussion über eine nicht ganz aktuelle Liste kommerzieller OSMF-Mitglieder über Mängel bei der Quellenangabe bei Apple und Facebook.
  • In der Schweiz finden Anfang Juli gleich zwei Veranstaltungen mit OSM-Bezug statt. Den Anfang macht die am 03. Juli 2018. Danach folgt am 05.-07. Juli die WikiCon. Beide Veranstaltungen finden in St. Gallen statt und OSM bzw. der Schweizer Verein SOSM wird voraussichtlich auf beiden Veranstaltungen vertreten sein.
  • Die FOSS4G Hokkaido fand am 22. und 23. Juni in Sapporo, Japan statt. Das Video des Core Day und eine Zusammenfassung von Beiträgen auf Twitter ist online. Auch einige Sessions aus der Programmtabelle sind verlinkt. OpenStreetMap wurde mehrfach thematisiert.
  • Die diesjährige State of the Map US findet vom 5. bis 7. Oktober in Detroit, Michigan statt.
Humanitarian OSM
  • OSM in Sambia wuchs von einer Hand voll Mapper im Jahr 2016 auf Hunderte an, wie HOT berichtet. Microgrants (finanzielle Zuwendungen, die nicht zurückgezahlt werden müssen, Anm. der Red.) von HOT, die Nethope Device Challenge, sowie Mittel von GIZ und der Weltbank ermöglichten es OSM Zambia Mappingausrüstung zu kaufen. Die Mappingtätigkeit umfasst die Sammlung von Geobasisdaten sowie Daten zur Vorbeugung von Krankheiten im Rahmen des Lusaka Sanitation Program.
  • Die Veröffentlichung der Version 4.12.0 des Kartenstils OpenStreetMap Carto wurde angekündigt. Die einzige große Änderung war die Entfernung der Subpixel-Genauigkeit für Flächen. Kleinere Änderungen beinhalten das Rendering für verschiedene tourism=information-Subtypen, place=quarter und historic=city_gate sowie weitere kleinere Änderungen.
  • Der OpenStreetMap-Blog enthält einen neuen ausführlichen Artikel für potenziell wechselwillige oder neue Benutzer von OSM-Daten.
  • Die Firma Telenav beschreibt in einem Blogeintrag ihre Ansicht, dass die Zukunft des Mappings durch die Omnipräsenz von Sensoren, künstlicher Intelligenz und maschinellem Lernen angetrieben wird. Telenav veröffentlichte drei Schlüsselkomponenten ihres OpenStreetCam-Stacks, nämlich ihren genutzten Trainingsdatensatz von Bildern, Machine-Learning-Technologien und die Erkennungsergebnisse, da sie davon ausgehen, dass der Mangel an verfügbaren offenen Tools bisher den Durchbruch automatisierten Mappings verhinderte. Im selben Blog-Post starten sie einen mit 10.000 US-Dollar dotieren Wettbewerb zur Verbesserung ihrer Trainingsdatensätze und der Mustererkennung. Telenav schließt sich damit Mapbox und Facebook an, die die Zukunft in automatisiertem Mapping sehen.
  • K_Sakanoshita hat seinen „Machi-aruki Map Makeraktualisiert. Mit diesem Tool kann eine Karte mit Vektoren in konfigurierbaren Strichen erstellt und diese als PNG oder SVG gespeichert werden. Die Benutzeroberfläche ist japanisch, kann aber überall auf der Welt verwendet werden. Er erläutert die Nutzung in seinem Blog.
  • Nikolai Janakiev schreibt einen weiteren Artikel über Analysemöglichkeiten von OSM-Daten. Unter dem Titel Compare Countries and Cities with OpenStreetMap and t-SNE beschreibt er, wie man die Verteilung von amenity= nutzen kann, um Städte und Länder zu clustern und zu vergleichen.
  • [1] Nikolai Janakiev veröffentlichte ein Tool zur Visualisierung von OSM-Daten durch Heat-Maps mit Blender und Python.
  • Die Version 2.9.1. des Editors iD wurde veröffentlicht. amenity=shelter impliziert nicht mehr building=yes, das Preset beinhaltet nun das Tag als Option. Neben vielen Preset-Änderungen und Ergänzungen, zwei Bugfixes, kleineren Usability-Änderungen und einer Tutorial-Änderung wurde die Auflösung von Bing Streetside verbessert.
  • Version 3.2 von QGIS wurde am 24. Juni unter dem Codename Bonn, wo 2016 das 16. QGIS-Entwicklertreffen stattfand, veröffentlicht. Ein visueller Changelog wurde ebenfalls erstellt.
OSM in der Presse
  • Das IT-Magazin Netzwelt veröffentlichte einen Artikel über die Vorteile von OSM gegenüber Google. Das Hauptargument, der Zugang zu den Rohdaten, wurde mit einer OSM-basierten Studie einer Reise-Website über die grünsten Städte begründet.
Weitere Themen mit Geo-Bezug
  • Heise berichtete, dass Google seine Kartendaten für Deutschland aktualisiert. Obwohl sie im vergangenen Jahr die Kameras an ihren Autos verbessert haben, ist nicht geplant, die Street-View-Bilder zu aktualisieren, da das deutsche Datenschutzgesetz dies nach Aussage von Google verhindert. Stattdessen sammeln sie Daten zur Verbesserung von Straßennamen, Straßengeometrien und POI-Daten.
  • Das schweizerische Bundesamt für Landestopografie fragt nach Hilfe bei der Georeferenzierung von historischen Bildern des Landes. Auch wenn man nichts beitragen kann, können immer noch die über 50.000 historischen Bildern der Schweiz durchstöbert werden.
  • StreetCred, die zum Teil durch die Schließung von Mapzen entstanden ist, schätzt, dass es weltweit 1 Milliarde POIs gibt. Die Firma kündigte an, dass sie einen Marktplatz aufbauen wolle, um POI-Daten von Benutzern zu kaufen, die diese Daten sammeln und an Unternehmen verkaufen. Die sammelnden Benutzer werden mit einer Blockchain-Währung namens StreetCred Token belohnt.
  • Die Firma ESRI veröffentlichte 33 Fußball-bezogene statistische Karten unter dem Titel Die Weltmeisterschaft in 33 Karten.
  • Diese Fußball-WM-Saison feiert Mapbox mit der Austragung der World Map Cup Challenge, einem 4-teiligen Wettbewerb im Juni und Juli. Man muss sich anmelden, um in den nächsten vier Wochen gegeneinander anzutreten, um tolle Karten zu erstellen und Preise zu gewinnen.
Wochenvorschau Wo Was Wann Land Stuttgart Stuttgarter Stammtisch 2018-07-04 Mannheim Mannheimer Mapathons (MAMAPA) 2018-07-05 Bochum Mappertreffen 2018-07-05 Urspring Stammtisch Ulmer Alb 2018-07-05 Lyon Rencontre mensuelle pour tous 2018-07-10 München Münchner Stammtisch 2018-07-10 Köln Köln Stammtisch 2018-07-11 Berlin 121. Berlin-Brandenburg Stammtisch 2018-07-12 Bonn Bonner Stammtisch 2018-07-17 Lüneburg Lüneburger Mappertreffen 2018-07-17 Karlsruhe Stammtisch 2018-07-18 Mumble OpenStreetMap Foundation public board meeting 2018-07-19 Essen Mappertreffen 2018-07-21

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 Nakaner, Polyglot, Rogehm, Spanholz, SunCobalt, derFred, doktorpixel14.

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JOSM 13996 released - lun 2 jui 2018 - 1:44

The 18.06 version (13996) is out!

Notable changes in this release:

For more details about the other changes and bugfixes, see the summarized changelog.

Happy mapping!

MY 1ST ACHIEVEMENTS AS A UNIQUE MAPPER - lun 2 jui 2018 - 1:31

WOW...:!!! Its a very Exciting moment for me as i was able to map over 5123 buildings,in about 15 countries with over 1299 changesets in 2 WEEKS...!!!!!! That's the best experience i have had so far as a mapper of about 3 months old. Shout Out to my Team #UNIQUE MAPPERS TEAM# For We Are UNIQUE..!!!