Not quite the last hurrah, but close. With three weeks left in Turkmenistan, Ann and I went to Koneurgench to see the marvelous architectural monuments, to the Dashoguz American Corner to give presentations (mine was on the Apollo 11 moon landing and U.S. space program; Ann’s was on White House chinaware). While moving around, we of course collected geodata and imagery.

We had a couple of spare hours during the return, so we went north up the P-1 highway past Ruhubelent to cover a section never before imaged (I will upload the Mapillary imagery after returning to the United States this month, where the Internet is much faster). The road was horrible! It took an hour and a quarter to go 50 kilometers, and at times we drove on the sand because it was smoother than the broken asphalt. Upon return to Ashgabat I logged into OSM and downgraded the P-1 north of Ruhubelent from trunk road to tertiary road. We identified several villages and other POIs, corrected one mistagged object (it was not a residential area, it was a cotton yard), and so on.

The month of Ramadan comes with mercy, Forgiveness and safety from the hell to the human beings by the almighty Allah. This month teaches us humanity, brotherhood and harmony. Every year in this month of holy Ramadan OSM Community Bangladesh arrange a Iftar Party for a get together of the community volunteers.

Iftar is the evening meal with which Muslims break their fast at the sunset and is one of the religious observance of the month of holy Ramadan. Iftar is often celebrated as a community, with people gathering together to break their fast.

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More on Public Houses, Insane Asylums & Cholera

Another Diary as part of mapping Nottingham’s Creative Quarter.

Continuing the joyful themes of my last Diary, which contemplated the Duke of Devonshire (Public House), the General Lunatic Asylum (Dakeyne Street and now closed, the inmates having transferred in Victorian times to Nottingham Borough Lunatic Asylum) and, finally, the 1832 Cholera outbreak that lead to both St. Mary’s Rest Garden and the explosion of humanity as it flooded out of Nottingham Town and (initially) into St. Ann’s.

Above is The Vine Public House, and the first in recent times that we have met that is both historic and still trading.

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it was a bit challenging from the start up into the mapping world but at the end of the exercise i was able to pull through

Hi to everybody seen this article, Happey eid fetr to all of moslem around the world

It has been almost one week since coding officially started with Google Summer of Code. As mentioned in my previous post, I have been selected to work as a Student Developer for OpenStreetMap through Google Summer of Code.

I have been working on my project and the experience so far has been great. In this post, I’ll be explaining what I’ve done and you can find the code in the development branch of this repo.

The Action Class

First, I had to create an Action class. The methods of this class will be called when an action occurs. For my plugin, I wanted the user to be able to select an area in the MapView and this area had to be processed. For this functionality, I had to extend the MapMode class (Makes the Action a MapMode and implement the SelectionEnded Interface (this provides the area selection functionality) and override the necessary functions.

Once this was done, I had to add the action class to the MapMode menus in JOSM. I did this by overriding the mapFrameInitialized method of the Plugin class.

Utilities

Next, I created classes that can provide utilities for the working of the plugin. The following classes were created: * DataUtils * NetworkUtils * MapUtils These classes provide functions that can prepare data to send to the server, communicate with the server, add points from the DataSet on the MapView.

Tests

Once all this was done, I started writing unit tests to make sure that the classes are doing what I expect them to do. For this, I used Junit and JOSM’s test utilities. For some tests that involved the methods that communicate with the server, I created a new endpoint in the server for the tests to communicate with. Right now, all the tests are passing.

What I’ve learned

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I use an artificial intelligence approach to detect plastic in the sea in freely available Sentinel-2 satellite images. My work already won an award in mid-May 2019: my submission was awarded second prize in the Marine environment category at the Sentinel Hub Custom Script Contest!

But I need much more time to perfect it until it is really useful.

With this goal, I’m starting a campaign to raise funds to offset my cost of living. I promise to improve my algorithm and to make it freely available for all to use if the campaign succeeds. Depending on how much time I can devote to this, I plan to document the process and release the source code for the community to improve it.

https://www.gofundme.com/OceanPlasticDetector

Please share this link widely and add some personal comment to reach as many as possible with my request! Thank you,

Bence Mélykúti, DPhil

I’m happy to release RoboSat v1.2.0 — RoboSat is an open source end-to-end pipeline for feature extraction from aerial and satellite imagery seamlessly integrating with OpenStreetMap for automated dataset creation.

This release is powered by major community contributions: state of the art Lovasz pixel-wise segmentation loss, better metrics, a road extraction dataset creation handler, and bug fixes! Thank you to our contributors!

v1.2.0 is also the first release shifting from a Mapbox sponsored development process to a community owned development process (after I quit Mapbox end of last year). New features, bug fixes, training and prediction all happens on my personal GPU rig now.

6 x GTX 1080 TI. Yes, the LEDs are necessary - they make it go fast

Here is an overview of what you will find in the v1.2.0 release

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I have finished mapping out Barra, The Gambia, using Bing map images to map out the city.

Here I am at the last day of our Internship. From the first day to the last day, I gained a lot of knowledge from the Geo-mapping field. Our training help me gain more interest in the field. I am happy to say that it was worth taking this Internship because it allowed me to deepen my skills in editing, it made me more confident on every task I do, and It made me productive.

I would consider the program as continuing chapter in my field of interest. It added more choices or opportunities in my pathway. From combining what I learned in this program and on what I will be learning in the future, it creates a great combination of knowledge on what I’ll be doing for the better good in the future.

This isn’t the end, I am looking forward to use and continue my training in the upcoming days, months, or years. Whats important is that I stay in that pathway of knowledge and extend my learning towards to others.

I am looking forward for more opportunities in the future. Thank you for this wonderful and grateful opportunity.

Here we are enjoying our stipend (Im in the left side)

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All throughout in this internship, I’ve gained a lot of knowledge, experienced and improvements in my mapping skills, specially when using the application and analysing the problem regarding with tags, imagery and other used data’s. This week was more challenging for me because I’ve encountered so many tricky problem with the imagery and problem in adding or aligning the roads, but in the end, I fixed it and realising the things that I need to enhance more in order to be accurate in editing and tagging. Working in this field was stressful for me since this is my first job and first experience in this kind of job yet I enjoyed this because it helps me to be more logical and confident with my decisions.

This could be the end of my first job but I’m looking forward for some opportunity with this skills I’ve acquired. Hoping this could be helpful for me in the near future.

This is our last day in kaart internship time really flies so fast it feels just like yesterday when we started but this is our last day but before I say goodbye in this internship I would to say thank you, I am very grateful in this opportunity cause I didn’t expect that I would able to catch up.

Participating in this internship really benefit me cause while we’re learning new techniques in mapping we’re also helping our community or even the world, so its a win and win situation. We even improve our knowledge when it comes to mapping while enhancing our logic and common sense our mindset becomes more accurate when it comes to mapping.

If ever there’s a chance for another internship I’m still looking forward to it and I am humbly voluntary for the next events may happen.

This is the last day of my internship with Kaart and I could say that obviously I had learned a lot of things including different techniques and logic tracing on how to deal with an image based from my mentors. In just one month I had improve my overall skills in mapping specially on tackling roads , I had familiarise different tags and error validation. Since I was had a poor skill in road mapping and tagging as well, this internship taught me to be more conscious with what I do specially with the accuracy and quality of my work. By now I am more confident on my work since I have the knowledge and the capability to thoroughly analyse things. Even thought at first I had trouble absorbing lessons that was given to us and even though the application of what I had newly knew was hard to apply in my recent work thus making my work inaccurate. Also the workshops that happened like the introduction of Leaflet and Mapbox. This workshops made me knowledgeable about the field of mapping is much more broader than mapping itself, the possibilities and what skills will be needed in this field.

I am looking forward to be working on mapping even if its not an internship anymore and just for humanitarian purposes. I would like to thank my mentors, co-interns, directors and the organisation of Kaart for giving me the opportunity to map for them this summer.

Regarding this summer’s project, for the first period, the main focus will be implementing Bicycle Routing Helper .this will help the user to route bicycle routes and help them fixing the routes according to the bicycle traffic rules. In past routing helper was available for helping to fix the routes related to Public transport and It will be very helpful for mappers to have the bicycle routing helper as well. after completing this there are many changes we have to make in pt-assistant in other areas such as subroute creations, left-right traffic detection, etc. I have started my work and its progress you can see in TaskManager Gsoc .

OpenStreetMap.org is a wonderful community driven maps portal. We can create maps in our own language too.

Demo

https://api.mapbox.com/v4/srikanthlogic.714e671e/page.html?access_token=pk.eyJ1Ijoic3Jpa2FudGhsb2dpYyIsImEiOiJuQ1RYS3pjIn0.7YUMcAQAc4A7T703-yAu2g#4/13.03/80.07

I am much inspired by the Tamil maps provided by OSM. Only thing we have to do is to translate all the strings to tamil.

I had a discussion on this with my friend Srikanth Logic long time back. Here is the chat notes from that discussion.

How to translate the strings on OSM to Tamil?

We have to select a region in OSM
Query for the nodes/paths that dont have tamil translation
Get them in a google sheet
Translate the strings to Tamil using Google Translate
Translate the strings using wikidata (not implemented)
Verify the translations manually and fix any errors
Upload the translations to OSM.

Here is more detailed tutorial on how to do these? https://goinggnu.wordpress.com/2019/05/31/how-to-translate-openstreetmaps-to-other-languages/

As some of you might know, Facebook has been using machine learning algorithms to help human mappers edit and validate geometry faster. Over the past year, we have completed mapping all the roads in Thailand and most of Indonesia. Watch the video below to see the progress.

Video on Thailand Mapping

Based on this work, many NGO’s, local communities, and tech companies have requested the data, which we have been sharing when available, like we did for last year’s Kerala Floods to help with disaster response. In the case of disasters, many enthusiastic OSM volunteers offer up their time to help fill in an area’s missing data. However, new volunteers often encounter two challenges: it’s hard to get started quickly and there’s a steep learning curve to master high-quality edits. To address these challenges, we’ve created a version of the primary OSM iD editor that we call RapiD to helps every mapper make edits quickly using roads suggested by our Map With AI service. It also has strong integrity checks to ensure edit quality. Special thanks to the original makers of the iD tool for building an incredible foundation.

In a previous diary, we shared our internal workflow for mapping using AI. Since that time, we have made some significant changes in order to allow greater accessibility and simplicity when working with the data. These enhancements and simplifications have coalesced into the current version of the RapiD editor. Over the next few weeks, we are happy to announce that we’ll start testing RapiD using the Humanitarian OpenStreetMap Assisted Mapping for Good Tasking Manager with selected partners to gather critical feedback. Based on that feedback, we will be exploring the possibility of opening the RapiD editor source code and how-to service documentation to the entire OSM community.

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Coding for Google Summer of Code officially started yesterday. I’ve started working on it and so far, things are going good. In this entry, I’ll briefly describe what my project is and does.

The Project - AutoBound

I will be working on building a plugin for JOSM that can automatically identify the rooftops of buildings in a given map area. This will make it easier for contributors of OSM to easily mark buildings and add data.

The tool will have two parts: 1. The Front End — This will be written in Java and will act as an interface between the user and JOSM. The plugin will allow users to select an area on the map. The satellite image of this area will be Base64 encoded and sent to the Back End. 2. The Back End — This part will be written in Python. The Back End will have endpoints listening on a port (default: 5000). When it receives data from the Front End, it is decoded and processed. Image Segmentation is done by a Deep Learning model that has been trained to segment rooftops in an image. Then the position of the nodes that can create a boundary for that rooftop is calculated and is returned to the Front End, where it is displayed on the Map View to the user.

The Back End will also be made available as a Docker image to make it easier to use.

The Timeline

GSoC gives students three months to complete their project. For the first month, I will be working on the Front End of the Plugin. I will be pushing all my code to the GitHub repo every day. For the following two months, I will be collecting and preparing data to train the Deep Learning model, training the deep learning model, integrating it with the endpoints, testing the working of the entire stack and adding documentation. The code for the server will be pushed to this GitHub repo. I will also be posting updates on my work every week on Medium and here.

Spent a long day visiting in the Maybury area of Woking. Before going to the pub, I wandered around a few streets and noted addresses and peculiarities. Though some areas of Woking have had houses added, I could find little evidence of addresses. There also did not seem to be any recent local ‘heavy mappers’… In my opinion, it is better to add buildings as approximate shapes with addresses, rather than to spend time drawing the precise shape of each. We are not town planners and, I suspect, that one of the main uses of OSM is for finding location, not for discovering who has a curved bay and who has a sun-lounge, or garden shed??? My general approach is to draw the residential building: if detached house then add address; if semis then draw two attached houses; if more multi then decide between terrace, apartment, or just ‘residential building’ and add address points using interpolation lines where survey or evidence is vague. What more does the User want?

Accelerating Sanitation and Water for All (ASWA II) project aims to achieve sustained access to basic sanitation and safe water supplies, as well as the long-term adoption of hygiene practices, across 10 focus countries. ASWA II builds upon the momentum achieved through ASWA I.

The project aims to improve the health, nutrition and wellbeing of the vulnerable population in 14 rural districts especially women and girls through sustained use of safe water supplies, sanitation service and sustained adoption of hygiene practices.

To achieve this humanitarian goal, OpenSteetMap Bangladesh Foundation (OSMBDF) is collaborating in creating GIS based WASH Inventory by facilitating training on data collection process using aswa ii apk.

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I have been mapping the little village Tchankada of my friend Cyprien in Benin lately. It’s not much work, but it was exciting enough for them to have their village mapped for the first time, it caused a wild story night.

Shortly after this, I contacted another person that mapped in this region, because having a local person helping can be useful for others as well. His main concern was to know the street names of the many streets around there.

There aren’t any.

My friend’s house is known by the family name inside the village, and the bigger municipality receives their mail at a post box in Cotonou. When he studied, and I mailed something to him, I used the post box of his university, and that arrived well.

For a long time, I was completely unaware about where my friend lived. Any description fell short. It’s also really hard to explain lat/long to local people, and the lat long I did receive was way off. Until my friend started to use my quickly hacked together app https://whenwhere.cf, which is basically a proof of concept for OLC for offline usage without maps. It worked brilliantly! I found his place on the map and he walked to some other places as well to add them to the map. He used it before in Cotonou to find the place back where he was before.

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