My last diary entry, which linked to some initial results from the survey, generated a comment about different contributors undertaking different types of mapping activities and the impact this could have on the data produced. The content of my most recent conference presentation with Peter Mooney at AGILE 18 in Sweden, discussed exactly this.

The results of this particular analysis showed that although women are more likely to be engaged in mapping for humanitarian purposes (i.e. HOTOSM), the prescriptive nature of these tasks mask the natural mapping behaviours and interests of those that complete them, i.e. many women.

Please see below for links to both the paper and powerpoint presentations.

http://www.cs.nuim.ie/~pmooney/websitePapers/AGILE2018PM_ZG.pdf

https://www.slideshare.net/ZoeGardner1/investigating-gender-differences-in-openstreetmap-activities-in-malawi

Our ongoing slum mapping project is part of a large-scale international collaboration entitled the National Institute for Health Research (NIHR) Global Health Research Unit on Improving Health in Slums. Our research unit will focus on health services in slums.

Our objectives are:

1. To map current health services and facilities and understand how these are used in nine slums across Asia and Africa.
2. To build on these maps to identify costs associated with how health services run in each site including informal providers, such as traditional healers, and the quality of these services as well as identifying costs to the patient and their households.
3. To systematically update our knowledge on: a) Disease prevalence in slums; b) Current healthcare provision models in slums and elsewhere in cities to include services which help to prevent disease; and, c) General literature on financing and the design of health services in low and middle income countries.
4. To generate viable options for health service delivery in slums and cost these options relative to likely benefits.
5. To work with decision makers and slum communities in designing the models of health service delivery, with a view to a subsequent evaluation of effectiveness and costs of viable options.

Further to the above plan of work we aim to:

1. Build a research programme which survives after our NIHR funding ceases; and to create a platform of funded activities into the future.

2. Develop capacity and communities of practice by involving people who can change things in slums, including politicians, civil servants and people who live in slums.

The following are some links to the project:

1) Brief summary of the project: https://www.nihr.ac.uk/funding-and-support/documents/global-health/units/RLilford-Plain%20English%20summary.pdf

2) Website for the slum mapping component of the project: http://blogs.cim.warwick.ac.uk/slummapping/

3) Slum mapping team at the University of Warwick:

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I have noticed that as soon as highway=path is tagged together with bicycle=designated and foot=designated it turns the highway into a cycleway. Must be a programmer’s dubious preference!

During making documentation of OSM tools and software it is frequently useful to add screenshots. Especially tutorials are often featuring multiple ones. I always disliked that such images are frequently getting outdated. It may caused by interface changes, map style changes and edits to OSM data.

Sometimes it requires more significant update of documentation, sometimes updating images is enough. But it may be very time-consuming.

When I was making my Overpass Turbo Tutorial for newbies* I wanted to find a way to make such images easy to recreate.

I found an interesting solution. Selenium is a software usually used for testing websites. It has plenty of features but interesting here is is that one may write a simple script that will

• open a website
• wait for it to load
• take a screeenshot

This way it may be possible to generate and recreate screenshots without manually taking them.

There is plenty of way how Selenium may be used, I am using Python bindings connected to Firefox using geckodriver.

Time-saving code is relatively simple.

imports:

from selenium import webdriver
import time
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

Lets say that I wanted an example of a simple website. This code will take a picture of googling results and save it to a specified file.

driver.get('https://duckduckgo.com/?q=wetland+OSM+Wiki&ia=web')
driver.save_screenshot('wetland-search-results.png')

Following code sample will modify vieport size and interact with page to hide irritating banners, then take a picture of site (note that such use is also subject of Tile Usage Policy like other automated tile downloading, but for regular tutorial updates use will be not higher than human doing the same):

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It seemed like a good idea on the face of it. Stops on the highway became public_transport=stop_position, stops next to it public_transport=platform.

But then it was suddenly necessary to add both these objects to the route relations. Already the fact that more than one object became necessary to describe a single bus stop, should have raised alarm bells.

There are, of course, some positive things to say about to say about the new scheme. A route_master relation for a whole line. route relations to describe the various itineraries and stops.

But the stops, that’s where it goes seriously wrong. Why on earth is it necessary to add 2 objects to those route relations? As they describe all the variations, there are a lot of them. Lots of work to maintain them. And they break easily.

After the scheme was voted, I was trying my best to map stops according to it. I had my highway=bus_stop nodes which were all nicely positioned next to the highways, so it’s clear on which side of the road they are located.

So I asked on the mailing list: hey what public_transport to use for them? The answer was public_transport=platform. So far, so good.

Since I had 40000 stops to add for half of a small country, I was not going to bother with adding stop_position nodes as well. So I trudged along for a few years.

In the mean time in Germany and France they had a different idea, let’s convert those nodes next to the highway to platform ways (regardless of whether there was an actual platform present in some cases), oh and those stop_position nodes, let’s duplicate all the details for the stops.

So, for the sake of simplicity, I would like us all to come back to our senses. Map each bus stop, exactly once on a node next to the highways and only add these nodes to the route relations. That’s how I will continue to do it in Belgium, although unfortunately in Brussels somebody already started adding stop_position nodes everywhere and adding them to the route relations. Oh well.

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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:

• Ahal Province
• Balkan Province
• Dashoguz Province
• Lebap Province
• Mary Province

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.

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!

Filters

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.

Location

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.

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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

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.

Goal

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

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I’ve set up a Mastodon instance for OSM (english speaking)!

https://en.osm.town/ - 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 fr.osm.social 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.

• @openstreetmap@en.osm.town
• @sotm@en.osm.town

More on Mastodon:

• https://joinmastodon.org/
• Mastodon User Guide
• Wikipedia on Mastodon
• OSM Wiki on Mastodon

Follow me on mapstadon: @rory@en.osm.town

No more of this…

(Image by zerebubuth)

I was using the default Bing maps satellite imagery, until I found “Background Settings” (B) on the right toolbar. For Ojai, CA 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.

The 18.06 version (13996) is out!

Notable changes in this release:

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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..!!!

Bình Châu Commune of Ba Ria Vung Tau Province launched new breakwaters in 2015, sadly the bay and waters of the port hadn’t been mapped until… 5 minutes ago. Finally they appear here, clear and beautiful, as if it’s Mona Lisa in a fine arts museum.

Before mapping:

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As of today I have broken a quarter of a million images uploaded to Mapillary that any OSM user can use for helping map Turkmenistan. I still have over 20K more images to upload from the Serhetabad trip and last Monday’s day trip to Magtymguly (Garry Gala).

Sometime this year I will depart Turkmenistan, so am feeling some pressure to collect as much data as possible before moving back to the United States.

Wikipedia’s work on Maps. See: https://blog.wikimedia.org/2018/06/28/interactive-maps-now-in-your-language/

OpenStreetMap Mexico’s data governance strategy

In Mexico, a working group of 8 people from the OpenStreetMap Mexico community has been formed since 2014, with varied profiles and several axes among which [1] the community training to add more users to OpenStreetMap, [2] the management with local and federal public institutions for the integration of OpenStreetMap and other geographical open data sources to their planning actions as well as for the opening of public data, and [3] an evolution of their methods to include a greater participation of the citizenship in the production and use of information.

One of the most consolidated topics of the international OpenStreetMap community is the generation and distribution of data in humanitarian contexts, in a preventive manner or after a crisis. The high frequency of natural cataclysms that affect the Mexican territory has given this group many opportunities for dialogue and collaborations with federal institutions and organizations (National Digital Strategy, CENAPRED, Red Cross, Doctors Without Borders, Greenpeace, Techo, Observatorio de la Vivienda, etc.), as well as towards affected states, for the generation and opening of up-to-date territorial data that make it possible to speed up rescue and reconstruction operations.

The first type of effort was training officials and organizations to familiarize them with OpenStreetMap and its potential for sustainable local management of territorial data. In response to various disasters since 2014, we have acted more directly to collaborate by providing information in real time to these institutions through tasking managers.

In parallel, we have had a constant effort of long-term linkage to establish a critical path where OpenStreetMap remains in the panorama of the actors of the data ecosystem for post-disaster interventions.

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It seems I have gained a cyber fan.

I have been adding previously unmapped roads to the database using satellite imagery, but mainly in areas I have traveled, as recently as within the past two weeks. There is a particular user who seems to be chasing and changing my recent edits around the map. I say “chasing” because I moved to a totally different area of the map I don’t usually work on and this user still found and changed my work.

My main goal is to map roads that aren’t in the database yet, but it seems this user is mainly just tracking my changesets and changing what I tag when I add the roads.

Is it frustrating for the mapper? Yes indeed. Frustrating is someone else changing the classification of a road I just drove on in a regular car. I might not be as frustrated if this user was also working at adding to the road network but the bulk of their edits are changing and deleting other users work.

Rhetorical question, but if everything I classify and tag will be shortly changed after I save, should I just add the road geometry with no tags at all and let this other user do all the tagging work?

I understand the OSM project is open to all and every user has the right/ability to change information they deem to be inaccurate. But isn’t it rather rude to single someone out and chase their work around the map, rather than add new information to the database?

Not really looking for comments - I’m just venting and sharing in case others are experiencing the same issue.

Recently I picked up a GoPro HERO5 Black camera for use in street-level imagery. It’s been a really great platform that has displayed fairly steady GPS acquisition and tracking. (Though I may not necessarily be the person driving when I am capturing footage.) I’m also big fan of contributing to as many projects in the OpenStreetMap ecosystem as possible.

To that end, I’ve found a workflow that has worked fairly well for taking photos from my GoPro and resulting in photo contributions to OpenStreetCam and Mapillary. Here’s how (note this is a compilation of not only trial-and-error but also advice scattered throughout the internet):

Capture street-level imagery with the camera

As mentioned, I use a GoPro HERO5 Black because it has GPS, and I capture in time lapse photo mode with a linear frame and 1 second timing (I have found that 0.5 second timing causes some issues with upload backends). Unfortunately glare from the dash is a major issue unless you’ve situated the camera dead on the windshield, so I use some fabric I picked up at my local Walmart to cut down on the reflectivity of the dash.

Extract photos

I usually plug my card from the GoPro into an SD card adapter that I use on my MacBook. Once downloaded, I then scp the files to a Linux box. You could most likely also complete the following steps on another platform that has a Python interpreter, but I haven’t tested it out.

Separate and filter photos

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