Several years ago, Esri made its World Imagery map accessible through OSM editors such as iD and JOSM. The goal of this was to give OSM mappers some additional options for high-res imagery when creating and editing features in OSM.

The Esri World Imagery map is compiled from multiple sources, including Maxar satellite imagery as well as aerial imagery from various GIS organizations (e.g. cities, counties, states/provinces, etc.). In general, the World Imagery map is curated to feature the most recent imagery we have available for a given area, though we do retain some older imagery for a while if it is better in other respects (e.g. currency, clarity).

The World Imagery map is updated every few weeks with the latest imagery that we’ve assembled and processed. During a release, we replace the current set of image tiles with a new set of image tile for several areas where we have updates. For example, in our most recent update, we updated the imagery in Western Europe with the latest Maxar imagery.

For most users and use cases, these updates are a good thing because they provide access to more current imagery. In some cases, however, it may be less desirable. The latest imagery may be more current, but it may also be more cloudy for a specific location, or the imagery might have shifted a few meters relative to existing features in the OSM data. In these cases, the previous or even older imagery may be preferred for editing. The older imagery may include the features you are editing with better clarity or relative accuracy. This is why many of us compare imagery background layers when editing OSM data for a given area.

World Imagery Wayback

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Introduction

Data they say is the “new oil”. This phrase, although seemingly far fetched is nothing short of the truth in today’s ecosystem.

I’ve written an overview of the patterns of major roads across Australia, based on OpenStreetMap data.

https://littlemaps692810600.wordpress.com/2021/06/21/australian-roads-in-openstreetmap/

There’s lots of colorful maps, charts and tables. It’s a deep dive that breaks down the total length of motorways, trunk, primary, secondary and tertiary roads in all Australian states, and the proportion of each that is paved and unpaved. (With a couple of local exceptions, virtually all of these roads now have surface tags.) It focuses on the patterns of the roads themselves, not tagging patterns.

So, if you’ve ever wondered: (1) what proportion of all major roads is unsealed and unsealed; (2) how the Tasmanian road network varies from that in other states; (3) where to find the longest, unpaved, trunk road in Australia; and (4) many other nerdy road facts, please take a read.

My March 2021 in OSM

sorry for the delay

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I used a couple of services that sent me emails when a change was made in my area

They disappeared

Is there any alternative ?

Larger End Goal: To have a map embedded within an app or a website where people are able to easily view where the nearest postbox or post office is, and also be able to add new post offices and postboxes to the map if they aren’t yet recorded in the map.

Step 1: Display both post offices and post boxes on a uMap, using the Overpass API. These items should be on two separate layers, as the icons need to be styled differently.

Current issue: There are lots of post offices in the world, and even more postboxes. This causes the uMap to time out with a 429 error of “problem in the response.”

The current map: http://umap.openstreetmap.fr/en/map/postboxes_528331#13/53.5705/10.0067

Current suggestions:

• Use an alternative public instance: osm.wiki/Overpass_API#Public_Overpass_API_instances
• Run an Overpass API instance locally (really seems to be overkill for what’s needed for this project
• Download the information of all post_box’s and post_office’s points and store that locally. Not sure what would be the best way to do this though. It looks like you can download all the OS data following the instructions here, but 1.5TB is a bit much to handle.
• Maybe use the Overpass API itself to download the data (perhaps in chunks), although it seems we either already reached some limit or it is perhaps already overloaded and we don’t want to make it worse unless we’re sure it’s ok.

Current Questions:

• What is the best way to get all (amenity=post_box OR amenity=post_office) points from OSM without overloading anyone or downloading the whole OSM dataset?
• If we do go the route of downloading a copy of the data and querying this locally, what is the best way to get a snapshot of the relevant points from OSM without us causing unwelcome load to someone’s server?

What we hope for:

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Thanet is a very old place and sometimes it hard to make out individual buildings in built-up areas.

To help solve this, moving forward I will be tagging businesses/addresses on the main entrance node unless it’s easy to say for certain what physical space that address takes up in that structure (houses are a lot easier locally).

I’m starting with Holly Lane area of Northdown Road

When preparing geodata for smaller scales, you need generalisation algorithms. Douglas-Peucker and Visvalingam-Whyatt algorithms are usually used for lines (polygons). Unfortunately those are mathematical/geometrical algorithms which do not take into account cartographic requirements.

Wang–Müller algorithm tries to do exactly that - generalise lines (of natural objects) according to cartographic requirements, such as saving or even exaggerating characteristic features.

Original Wang–Müller article has a quite concise description of the algorithm and as there is no open source implementation of this algorithm (to my knowledge) it is impossible to use this algorithm in wider scale and there are a number of unanswered questions on how some particular aspects should work.

A student of Vilnius University, Motiejus Jakštys, has done a perfect job - he has not only implemented the main part of Wang–Müller algorithm using open technologies, but also described the algorithm in his paper, which you can find here:

https://github.com/motiejus/wm/blob/main/mj-msc-full.pdf

Besides describing the algorithm itself (in more detail that original paper), this paper also includes recommendations on values of parameters to use on different scales, a lot of pictures as well as propositions for future work on finishing and refining the open implementation.

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Last week, the U.S. federal government finally agreed to retire a number of racially insensitive names of landforms and bodies of water in Texas, 30 years after the state legislature petitioned the federal government to rename them after accomplished Black Texans. (Most of them had originally been named with an even worse racial slur.) OpenStreetMap has been updated to reflect the new names that are in GNIS, the federal government’s official gazetteer. The corresponding Wikidata items have also been added or updated, to prevent data consumers from overriding OSM names with the offensive old names, but also to link to more information in Wikidata and Wikipedia about the person whom the feature now commemorates.

Most of the features had to be mapped for the first time, because GNIS doesn’t have enough detail about linear features like valleys and creeks to import them, and NHD had only been imported in a few parts of the state. Except for Emancipation Pond, which is in a residential development under construction, a traveler is unlikely to ever stumble upon these remote features in the vast Texas landscape. Even so, much more accessible locations get renamed practically every day. Hopefully these OSM and Wikidata entries will serve as a handy model for mappers to follow as we find more efficient ways to track and respond to name changes.

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I use to explore and map the area on the border between North and South Tyrol, and I have noticed that part of the border between Austria and Italy is wrong in openstreetmap. I noticed the error, because some abandoned Itallian military infrastracture had moved into Austrian territory.

Here is an example (before fixing): I know that the house with the geodetical tower is in Italy. The boundary stones are located some meters northern. The border between North and Tyrol runs on the watershed, so the border is exactly on the Sandjöchl Pass.

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There is going to be an OSM-Carto map reading workshop run by me at this year’s virtual State of the Map conference on July 11 at 12:15 UTC. This is going to be a new, experimental format i would like to try where i will explain the cartography and the reasoning behind styling decisions of the standard style shown on openstreetmap.org based on case examples you can submit beforehand.

For that purpose i have created a wiki page as well as an Etherpad where you can submit such examples. In the simplest form such an example is just a link with the URL pointing to a certain place on the map you would like to see discussed and explained. Ideally you should add a few words on what specifically you would like to see discussed because often there will be other things visible in the map as well.

I plan to make this an interactive workshop allowing those who submit an example to participate in the discussion if they would like to (though this is strictly optional of course). But to allow me to prepare for the case examples it would still be desirable to submit those in advance.

You can submit your case examples on any topic and any place on the map but of course i reserve the right to pick those examples from the submissions i consider most interesting for a wider audience (and you can also indicate if you are interested in examples submitted by others on the wiki/pad). If there are more examples submitted than can be covered in the workshop i will try to discuss them on the wiki after the conference.

Feel also free to add any comments and thoughts on the workshop idea and format here or on the wiki talk page.

I have a few passions in life. Two of them overlap in an interesting fashion…

A number of years ago, I decided to look at how engineering (and specifically full-lifecycle analysis) could support improvements in road safety. While many road safety issues are primarily behavioural (speeding, awareness, road rage, etc), I strongly believe that infrastructure should guide road users to making better decisions over time through feedback, reinforcement of good behaviours, etc. Anyway… I decided to buy a dashcam and figure out if there was a way to collect data in a way that would provide strong arguments for change at both a local and national / international level. Due to other commitments and a lack of interest from the general public I haven’t made much progress. :(

My other rationale for getting a dashcam was to allow imagery capture to support mine and others’ OpenStreetMap activities. I drive quite a bit on my way to climb mountains and explore the south coast of Ireland (sometimes to play MMORPG Ingress). Capturing imagery and contributing it to the global community seemed like a good way to get additional “mileage” out of my travels!

But dashcams aren’t setup for sending data to Mapillary I here you say… True… but I never give up!

The OpenStreetMap Ireland community recently decided to capture imagery and authoritatively map the #WildAtlanticWay, a 2500+ km route along the west coast of Ireland. While there is some existing imagery of the route, there are some notable gaps and we wanted to fix that.

Being one of the primary contributors in Cork (Ireland’s largest county by land area), I was motivated to try and contribute more imagery in Co. Cork and Co. Kerry. So I started digging into the tools available for processing and uploading data again (to see what improvements had been made since I last tried).

Processing dashcam videos so they are usable my mapillary

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Due to ill-considered from the Wikidata community using old and outdate 2017 dataset from Directorate General of Budget, Accounting and Statistics (DGBAS), Executive Yuan, Taiwan, the newly found villages in 2018, like the villages mass modification in Taiwan in 2018, or split villages due to increase population, these new villages was not included and imported into Wikidata. So there are some OpenStreetMap village relations could not link to Wikidata.

For the OpenStreetMap part, typebook using relative new and up-to-date shape files from National Land Survaying and Mapping Center, Ministry of Interior Affairs, to add the Code of Household Registration and Conscription Information System (HRCIS Code) and the followup Wikidata item. There are only mssing Zhengxin Village, Dauliu City, Yulin County on OpenStreetMap. OpenStreetMap Taiwan community could just add relation to fix the problem on OpenStreetMap.

For the Wikidata part, there are several users start to import villages data to Wikidata, which started from 2019. If you look closer to some of the Wikidata items, the reference they used link to files from the DGBAS, which is out-of-data and the last update time is 2017-01/31. DGBAS decide to switch to use the HRCIS Code. It indicated that Wikidata users using old dataset to import villages, so the 75 new villages was not included, is missing on Wikidata database.

When Wikidata Taiwan community start to deal with the villages data in 2020, there are many villages already combined in disappear. But due to the fact they used DGBAS out-of-date dataset, they could not found out their are missing mass amount of villages. The DGBAS also missing the villages English name, so we see the imported item missing English label.

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In the year of twenty-twenty one, I still map using an external Bluetooth GPS receiver unit (yes, and yes, GPS explicitly refers to the American constellation only).

The device in question is Qstarz BT-Q818XT. It was updated with a firmware that kinda fixes the Y2K problem of GNSS.

Of course things can work with Android devices

Purchased in 2014, I used to hook it up with the BlueGps app with a tablet running Android Jelly Bean.

Latest Android versions shipped with devices come with time. The BlueGps app stopped working in Android KitKat. Luckily, there’s another app in the Google Play Store that still could do exactly that: it’s simply called as Bluetooth GPS. The APK copy of it is still able to function independently in devices without Google Play Services.

With latest dessert release, things start breaking up

Fast forward to the dessert release of Pie. Significant changes in more recent releases of Android means apps that no longer patched, with several legacy API calls begin to cease functioning properly. At least the app would just still work on Android 8.1.0 Oreo.

What other alternative is available?

Browsing through the Google Play Store appeared to be hopeless at first. Similar apps like Bluetooth GPS are common - they stopped received any updates for quite a long time. I don’t want to take the risk to give those shady apps a go, until I stumbled upon Bluetooth GNSS - GPS, Galileo, GLONASS and BeiDou. What a long, descriptive name for an app.

No adverts? Cool? More like cooler! I took a dive.

Yes, it works on Android 9.0 Pie. As the time of writing (the app received an update on 26 May 2021), the app simply works on Nokia 2.2 running Android 11 (warning: a 6MB GIF!). I suspect it would run OK on devices with near vanilla Android (Pixel, Android One, etc).

Some disclaimer: I don’t receive anything to share this.

Custom ROMs and the Bluetooth GPS receiver unit thingy

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Some words that I left at the MapComplete Telegram Group:

Several years ago, there are some mappers start a drinking water map in Taiwan. These mappers are planing to change the map subject, using the code of the drinking water map to deploy more theme map to promote OpenStreetMap. But unfortunately we couldn’t get the resource to make it possible to get plenty of theme maps to get Taiwanese people interested.

Glad to see you guys got supported from OpenStreetMap Foundation, Open Knowledge Belgium, Trage Wegen, OSGeo Belgium, Humanitarian OpenStreetMap Team, Missing Maps.

Some background information about the Drinking Water Map: 1, 2.

Slow going on the mapping project. Hoped to complete the Otago Dam Track today, but the stream had clearly flooded parts of the track recently which made it muddier than usual. Since this track has multiple stream crossings and a risk of flash floods it seemed unwise to carry on.

Track has been mapped up to the first major stream crossing, where the first flash flood area ends.

I ran into other walkers who had turned back and was able to direct them to Munro’s Gully walk which is of similar difficulty and length without the wet feet.

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I opted to explore the part of the Gabriel’s Gully Interpretive Track that goes to Pollards Dam. Someone previously traced the trail into Open Street Map, but I had noticed the Pollards Dam section on OSM seemed much longer than the actual track.

It turns out there is a track that goes behind Pollards Dam. This track is not visible on any map I have of Gabriel’s Gully. It starts out along a ridge which overlooks Greys Dam to the right. At first I wasn’t sure it was really a trail, but there was a stile where a fence clearly used to be and a bench a short while later. Once past the overlook you enter into pine forest with a clearly marked trail that runs alongside Glendhu Forest Road. Unlike the Interpretive Track, this mystery trail uses plastic arrows. Unsure of how long the trail was and where it was going I did not complete it, but wondered if it linked up to the Munro’s Gully Track.

Interestingly, as I was leaving I ran into walkers I previously directed to Munro’s Gully. They did the Munro’s Gully trail, but also ended up on the mystery track, confirming my suspicions that it leads there.

This seems preferable to the official Munro’s Gully walk which has adventurers return via Glendhu Forest Rd, which is used for forestry operations and has no sidewalk.

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Walking Track Map Project

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A NEWLY DEVELOP SUBDIVISION

Hello everyone, through this diary I would like to introduce myself to the broader OSM community and share plans regarding my GSoC project

About Me:

I am Zohaib Ansari, an undergraduate student in Electronics and communication from New Delhi, India

I spend my free time learning programming, working on my own small open source projects

I was introduced to OSM by a friend I play geoGuessr with, and coming across Vespucci made me interested in applying to OSM as the project idea seemed pretty interesting

Element history widget for Vespucci

During the GSoC coding period, I will work on creating the element history widget as a library for android apps, the element history widget will bring a feature similar to JOSM’s native element history comparison dialog to android (with certain platform limitations)

The Main features of this library would be:

1. Display information about all the iterations an OSM element has gone through
2. Selection of any 2 versions of the element from the history and allow comparison between them
3. Displaying other information including coordinates, changesets etc

Apart from creating the library, I would also work on providing robust test cases for the library itself and for Vespucci after the library has been integrated with it.

I will keep a detailed account of my work in the github wiki of the project and will also keep this diary entry updated throughout my time working with Vespucci, please feel free to reach out if you have any questions regarding my work or anything else!

Link to detailed Roadmap and Progress for this project: Wiki

Introduction

Hi OSM community! My name is Vuong Ho, an international student at the University of Rochester in the U.S. I am currently living in Ho Chi Minh City, Vietnam, though, since due to Covid-19, I haven’t had the chance to go the States yet. In my free time, I like to tinker away with my small personal projects, play games with my friend, and watch cat videos on Youtube. Once this pandemic is over, I am thinking of going mapping around too!

Though I have never contributed to open-source, I really like the idea of openly sharing software for public use and would like to contribute to a project someday. Therefore, I am really glad to have the opportunity in this GSoC to contribute to OpenStreetMap!

My GSoC project: Opening hours tag evaluator

As noted by Simon Poole, the current opening hours parser lacks a complete evaluator for its input opening hours tag. This project will center around building an evaluator to determine if an input of date and time translates to a location is currently opened or closed based on an opening hours tag (specifications here). This evaluator will also support checking sunrise/sunset and holiday information, if any is provided in the opening hours tag, since these are supported by the parser.

The evaluator will be in the form of a data structure resembling a compact calendar, though this may change over the course of the project since I will be making use of the test-driven development framework to develop the evaluator. The information on sunrise/sunset and holidays is taken externally, using other open-source libraries and data. My full project proposal can be found here.

The milestones for my project in the span of two months of this year’s GSoC are:

1. Creating a simple evaluator that supports checking open/close status of time and day
2. Adding support for sunrise/sunset information
3. Adding support for holidays information

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Hello everyone!

I will be sharing my GSoC’21 journey with OpenStreetMap through this diary. Feel free to ask me questions if you have any, I would be delighted to answer them!

Who am I?

I am Antonin Jolivat, a French student in an Engineering School which specializes in Computer Science.

Passionate about Software Engineering, I spend many time to discover new domains and create personal projects related to them. The rest of my time, I like to read about what others people do, entertain myself with various things and work out to stay in shape.

I came accross OpenStreetMap while working on a project involving maps and it stayed in my mind. I recently got involved a lot more as I have chosen OSM as my organization for the GSoC’21, and I am very happy to have made this choice! :)

My GSoC Project: QA Reports Extraction Tool for Nominatim

This project is mainly concerning Nominatim but it will help to increase overall OSM data quality.

Nominatim is the OSM’s main geocoding software used to process geocoding requests on OpenStreetMap data. The software uses its own database schema which differs from the one used by the main OSM database. As a result, Nominatim processes OSM data in a way that allows to discover a lot of inconsistencies. The idea of the project is to build a Quality Assurance Tool which will analyze the Nominatim database and extract errors from the OSM data.

Those errors would be made available to everyone through a map, so that OSM mappers will be able to correct them easily.

At the beggining, we plan to use Osmoscope to display the data errors through a map. This map may be available under https://nominatim.openstreetmap.org/osmoscope (/!\ subject to change)

In the future we could even imagine to share the data errors through other tools, like in the form of challenges suitable for https://maproulette.org/.

The main goals of the project are:

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