My team at Grab and I got an opportunity to experiment with the RapiD – AI-assisted mapping editor. We found this tool to be useful in mapping missing road networks. It is also helpful in increasing the mapping speed, as:

• Missing features are already highlighted – helped us focus on the exact location where the feature is missing than going through an entire area.
• Once selected, they are already traced, thus minimising the tracing efforts and help us better the quality of tracing
• Help in increasing mapping speed plus give us full control of the data that can be uploaded and the ones that need not be uploaded.

This would be a great tool for the community in SEA as the folks here are new and would be willing to explore this to map the unmapped regions here.

Here are our major takeaways post using the tool:

• The AI detections are pretty clear and are correctly detecting the roads.
• The user has the option to accept or reject a detection, clearly establishing that a mapper intervention is necessary even if the AI is pretty good in identifying.
• It automatically connects the new roads to the preexisting ones or warns when a new segment that we accept has been connected or not, which is great for new mappers to understand and validate.
• The detections stand out but also doesn’t take up too much space, making it easier to spot the roads without having to disable to rapid assist.
• The detections already had predefined tags, which makes it quicker for the mapper to add the roads and since the tags are already defined, it eliminates the cases of having or uploading untagged ways.
• The AI detects the features until the end of that grid and allows the mappers of the other grid to connect the roads, which makes it easy and conflict-free.

… See full entry

I mapped out minor unclassified roads. For now there are no challenges.

I learnt how to map buildings, roads and pathways. The challenges i faced was blurred imagery which made it hard to delineate buildings and pathways.

Task was to map vulnerable communities in Gokana in Niger Delta Region which is facing the problem of oil spillage.The tasks was to map buildings and roads.Today i succeeded in mapping some footpaths that i assume that lead to farms.

My name is Tuan and I am living in Melbourne Australia. I have been contrbuting to OSM for over 7 years and I’ve learned to use JOSM, iD effectively. I spent almost 1-2 hours every night and most of my weekends doing onsite surveys, adding points and drawing OSM maps in parts of Vietnam, and many cities and suburbs in Australia.

After years of mapping I have advanced myself to an Advanced Contributor, which has allowed me to be a mentor and advisor to many GIS developers in Vietnam.

Overall I have mapped OSM with a passion and wouldn’t mind spending longer for OSM.

My current job is advertising coordinator. However, the job has become harder recently and I am afraid that I cannot keep the job in the next few months.

At the moment I am seriously considering a career change into the GIS field.

… See full entry

English/Japanese

Aren’t you tired of mapping endless number of buildings and roads?/建物や道路のマッピングもきりがないですよね？ OK, I will give you an interesting mapping mission /そこでこんなマッピングはいかがでしょう:

Map your neighbourhood in detail /ご近所の詳細マッピング

It will help for the completement of the final 50 feet for /こんなことの役に立つかもしれません:

• unmanned/automated vehicles /無人あるいは自動運転の乗り物
• wheelchair drivers /車椅子利用者
• postmen /配達人
• detailed routing for on demand bus / caregiving bus /オンデマンドバスや介護バス向けの詳細な経路案内

Typical mapping objects seen around residential area in Japan /日本の住宅街でよく見るマッピング対象

Road objects /道路関連

… See full entry

The second evaluation of Google Summer of Code is complete and the results came out a week back and I cleared it. This article explains what I did during the second phase of GSoC building AutoBound.
The main two main tasks I focused on during the second phase of GSoC were:
1. Data Collection
2. The plugin-server pipeline

Data Collection

… See full entry

I was able to map my immediate environment and also engaged in the mapathon battle for vulnerable oil spill communities in Nigeria

This is actually the first time am mapping complete thanks to God

today was actually stressful due to trying to map in the nigerian mapathon battle but we scale through keeping the Absu mappers team name up and special thanks to surveyor Arungwa Ikenna Donald for helping us out with data for this mapping and also to the national coordinator Mr VIctor Sunday God bless you sir for opening our eyes on this

… See full entry

Before

This was originally posted by myself on 12 May 2019 as a response to the then–current astonishing volumes of “wfgz spam” & “vip spam” affecting these Diary pages (upto 30,000 spam-posts each day, starting 25 April). That existential threat appears to have receded (see the first part of the Bot-Flood Stats page), but the continual background trickle of spam– & nonsense–posts (upto 10 a day) continues.

It should be possible to easily shut off that vile flow without badly affecting valid OSM users. This post reflects on the current situation, and seeks to present my own suggestions and to provoke comments to perhaps obtain even better methods.

Institute a “No-edit, No-diary” rule.

All current spam & abuse would stop the instant that the privilege of posting to this Diary was restricted to those that have added to the map. My suggestion would be a parameter of ‘10 changesets’ to relax that rule.

There are further steps that would need to be taken to make sure that such a rule would not cause collateral damage to the map:

1. Moderate New-User Map Edits
   Map edits by new users should be human-moderated by being placed into a hidden queue. A team of volunteers that are seasoned mappers can then be assigned Moderator rights to accept/reject map-edits within the hidden queue. Such moderation should only be lifted once the user has shown themselves to be a human with (say) 10 useful map edits. Diary posts would be auto-prevented until the statutory number of Map edits was completed. Once completed that restriction would be auto-removed.

2. Auto-remove Unused Accounts
   Auto-remove all those that acquire an OSM account but never update the map within (say) 2 weeks.

Background

OSM Diary pages both share features in common with blogs, forums, etc. and at the same time are different. The features shared in common are:–

… See full entry

https://cycling.data.tfl.gov.uk/

Does anyone know if this open licence is open enough to be used in OSM?

After facing some situations where I (or somebody else) had to update a bunch of tags or even bring new ones to OSM, without having to modify geometries themselves, I decided to create a tool that was able to automate the process.

OSM CSV Appender is a simple tool that merges a CSV file into an OSM file. It can be used to import data to OSM, or even to map tags associated to a set of geometries in offline mode that, when ready can be appended to OSM and sent in only one changeset.

There are two ways to join the files. One is matching tags between files and another is matching the id of the geometries.

This tool was developed to be used with JOSM, to prepare OSM files before the appending process. After the process it is used to assess the OSM file quality and to upload the changeset. Now talking about the CSV file, you will need a CSV file editor like LibreOffice Calc and/or GIS programs like Qgis to prepare data and generate CSV files for you.

Although OSM CSV Appender joins files for you, it is very important to assess the quality of the results. To help you with this process the tool also generates a CSV file which contains the usage count of each row in CSV file by each geometry type (node,way and relation).

This tool is documented in OSM CSV Appender on OSM Wiki

Suggestions and feedback of errors are welcome.

… See full entry

Lion mappers held @University of Nigeria Enugu Campus today being 02/08/2019

Mapathon Training for Vulnerable communities coordinated by Mapathon coordinator Nigeria. University of Nigeria, Enugu Campus.

Introduction

The completion of the Traffic sign catalogues task brings a long-desirable feature in OSM2World: being able to define and configure materials solely through the configuration file, without the need for them to exist in Materials.java!
During this main feature’s implementation, the Human Readable Traffic sign Values task also came to a more complete state by adding lines of code related to it, while progressing, to cover needs that came up.
This entry will be split into 3 parts, separately covering Human Readable (H-R) values-related additions to avoid possible confusion. It is worth mentioning that there are of course more changes than the ones explained here but as changes are being constantly made it would be practically impossible to include every detail that has changed since the last diary entry. Plus, it would turn out to be boring for the readers.
That being said, sample sign images with the material definitions/configurations that create them included can be found by the end of this entry!
Let’s get started:

Human-Readable values related additions

I will try to keep things short and briefly present only the most important points as this is not the main focus of this entry:

• A clear distinction between H-R values and non H-R values is now taking place in TrafficSignModule.java. Its simple implementation may seem obvious at first but it had to be validated by looking through a lot of Overpass data with a query like

[out:csv(traffic_sign;false)];
nwr[“traffic_sign”][traffic_sign~”:”]({{bbox}});
out;

that looks for traffic sign values that include “ : “ in their name i.e. are country prefix - sign ID pairs like DE:267 . Using that, one can see that there were no cases where a sign contained both a country-prefixed value and a human-readable one together (e.g. v=’DE:254, maxheight’ ) so the code below was deemed to be the way to go:

… See full entry

HI DIARY,

This is our first entry. Actually, this is a joint account of me and Angelica. We are here at Foundation’s University attending the first ever Pista Ng Mapa. We’ve got a little problem, we are not officially signed up for this and we don’t know if we’re going to learn anything today. We are just here to eat. Hays.

Just kidding!

Yours truly, Raya

I updated the “Long Names Of OpenStreetMap”.

Now there are 722 long names, that is, objects with a name longer than 150 bytes.

I haven’t run a full update since October 2017. Back then there were 464 long names. So I guess we’re getting worse at keeping our names short.

It looks like bus routes with very long name values are becoming more widespread. For example here is the “391 “B”: Veresegyház, Misszió Egészségügyi Központ => Veresegyház, Dukát utca => Veresegyház, Cserje utca => Veresegyház, Eötvös utca => Veresegyház, Újiskola utca” bus route. Snappy name!

Maybe I should’ve limited by character count rather than bytes. Cyrillic scripts are at a unicodey disadvantage, not to mention chinese.

Not all of them cause wacky rendering like the theatre example in the screenshot above (node).

I do think they all have something in common though. Using the name tag for something which isn’t really a name.

… See full entry

Organized a training for youth mappers team Abia State University, Uturu (ABSU Mappers team). It was training to prepare local mappers for the an upcoming Mapathon for communities in Nigeria vulnerable to flood.

Had over 40 Local/Youth mappers in attendance.