Recently at the Portuguese OpenStreetMap Telegram group we have been discussing the issue of the Pokémon Go! Players that become OSM contributors with bad intentions, by adding fake parks or wrongly to their ingame interest.

We decided to check how many parks existed in Portugal by using overpass turbo back in August (kudos to Luis Forte for the help). To our surprise there was over 7000 parks. We decided to create a map roulette mission to validate the parks. Some are indeed hard to validate by arm chair mapping, some are not. Up til now we have validated 20% of the parks on OSM and acknowledged that more than 12 percent were indeed badly tagged and only 7% were correctly tagged.

As example,gardens of private property or grass in roundabouts. https://maproulette.org/challenge/9053/task/24888572

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Im posting this trying to support someone that contributed a lot to OSM via his street level imagery, either being on Mapillary or OpenStreetCam for others to map on OSM. If you could donate, to help him repair his vehicle to get back on photo mapping, go to his GoFundMe page

Heres the story:

My name is JB Brown. Online I am often known as JBTHEMILKER. I have become something of a legend in the digital mapping world. In just over a year of contributing to Mapillary I became their top worldwide contributor with 8.3 million images and 146 million meters mapped. Mapillary account

I’m also #2 (I was #1 For a while) on the Openstreetcam app I’ve contributed 1.2 million images in less than 4 months and mapped 22,400 miles for them. All this has been done at my own expense.

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For those that have check my previous tutorials, are aware of the benefits of using Mapillary imagery (especially the traffic signs detections), however some crosswalks don’t have traffic signs nearby. By using Mapillary AI we are able to detect them fast and add them to OSM. Here’s the video tutorial how you can use AI Detections for OSM.

Find out more about Mapillary AI Detections

My previous tutorials: ID Editor and JOSM

Interesting reading: https://agile-online.org/images/conference_2017/Proceedings2017/shortpapers/77_ShortPaper_in_PDF.pdf

Abstract

An increasing number of crowdsourced geo-data repositories and their services allows volunteer mappers to utilize information from various data sources when contributing data to a crowd-sourced mapping platform. This study explores to which extent OpenStreetMap (OSM) contributors use the crowdsourced street level photo service Mapillary to derive mappable data for OSM during their editing sessions in the iD and JOSM editors. We cross-check the location of OSM edits with the geographic areas from which OSM contributors loaded Mapillary images into the editors to determine which OSM edits could have been based on information from Mapillary images. The findings suggest that OSM mappers are beginning to utilize information from street level images in their mapping workflow. This observed “cross-viewing” pattern between different datasets indicates that the use of data from one VGI platform to enhance that of another is a real phenomenon, leading to implications for VGI data quality.

After seeing the diary of how to use Mapillary to add building attributes on The state of San Francisco buildingsi decided to create this article on how to use Mapillary as a tool to improve OSM road data. This article will focus on how to use Mapillary traffic sign detection to implement turn restrictions, Mapillary imagery to add lane value and turning lanes. I won’t get into how to capture Mapillary images using smartphone or action cams, as you can find that information on Mapillary website check here and you can request a car or bike mount for your smartphone here

(Please note that from my experience, after uploading the photos to Mapillary, the traffic sign detection can take from 24 to 96 hours to be processed and being displayed on the map).

Editor used JOSM. JOSM plugins needed: Mapillary; RoadSigns; Turnlanes-tagging; Turnrestrictions.

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