Semi-Automated Tree Additions

This is the question that should be obvious to everyone. Hide this post if you will, but it’s just nuts ( and obviously not from the tree type).

That does not really sound like a good enough approach to me. Due to the height of most trees, every photo of almost every tree will be off by 5 to 20+ meters. Because it applies to almost every single tree, the GPS correction should be on a somewhat mandatory and at the same time intuitive basis.
Otherwise I can see many (especially new) users who either don’t know about the manual tree location correction or they don’t care about it and just want to take photos or think it’s too complicated.

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One idea would be to ask users to take three photos: overall shape, a leaf, and a close-up of the trunk. That will help with species identification and could be a very useful database generally. You then take the co-ordinates of the bark photo as the tree location. “Enforcement”, but actually useful, too.

Proposing to fork this thread here: aside from the technical stuff, and community acceptance, can you say who is behind this project and what the motivation is? What is the useful data that will result?

Asking because I’ve had exactly this idea for a while, with zero time, money or skill to do anything about it. (Fwiw my own motivation is to map the trees in my city to promote their conservation, and track climate change.)


Hi, thanks for the response, I’m not sure I follow this though.

  • How does your app know that the GPS location is wrong by ±5 to 20 metres?
  • What known accurate reference is being used as a comparison to the GPS location?
  • What motivation do the users have to correct the location manually, if this step is not being enforced?

My other question is still to be answered:
What are you doing to conflate your data with existing OSM data?

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Hello again,
To answer your questions:

  1. The GPS service provides a real time feedback on how strong it’s signal is and that is represented based of it’s approximate error
  2. We’re using a GPS service that relies on fused location providers that determine location through available on-device hardware, including (but not limited to) Wi-Fi, GPS, and cellular networks. As far as the working of the GPS goes, I am not completely sure of the architecture of their work is.
  3. The step is optional of course, before submitting the pinned location, we show the user their pins on an Imagery map, so that they can confirm their points. Since the person sees the imagery map with pins on, they are motivated to move the pins to the right locations if they see that the accuracy is off.

To answer your main question:
With this data, we aim to map trees in onto OSM(which is not as popular as mapping buildings roads and utlities) so for this mapping is helpful for:

  • More accurate 3D modelling of the world (w.r.t. OSM 3D mapping)
  • More relevant mapping information for first responders (a tree in the way of a disaster struck area could be an unseen problem for first responders)
  • A way of keeping in check the ecological changes through mapping too, etc.

That sounds very hit-and-miss to me.

  1. An “optional” invitation to “confirm” the (inaccurate) locations the app has suggested is an invitation to do exactly that: the default effect in human behaviour is very strong.
  2. Before re-positioning their pins, users will also need to overcome the expectation the app has itself set for them, that this is an “automated” process. So in the same process you’re asking them to trust, and not trust, the app. That’s doable, but difficult.
  3. Setting an accurate location freehand on a phone seems to be very difficult for novices: e.g., many (maybe most) Note pins are off-target, and this can be by 100s, even 1000s of metres. I think this is because (a) accuracy depends on zoom level, and (b) novice users don’t realise how precisely the database records location.
  4. Even allowing for this, how will users determine the “better” location? Aerial photos are often offset from ground truth. Existing map features can be better than aerial, but also worse: the user won’t know which. And many trees will be in open ground where there are few reference features anyway.

Did you see my suggestion that you have users take a close-up picture of the bark of the trunk to complete the workflow, and take reference coordinates from the photo of the trunk? This helps with identifying species, age and health of tree anyway, and would build a “true” GPS location* into the primary workflow.

*or at least, best possible

P.S., my OutdoorActive app shows the GPS error: I can watch it improve in real-time, so when I need a highly accurate location I can wait for it. This is immensely helpful.

Would still be good to know which organisation is behind this.

But some of my questions here have been answered elsewhere on this thread:

I agree with all of this. ^

Honestly, no answers from the project have made me confident that an automated/scripted approach is the best way to add this data. It would be much better to make the geotagged photos available to OSM so that users can manually review and add data. See as an example.

I think many doubts and fears have been created with this topic. Most derived from the initial post of this thread which refers to a somewhat ambiguous query to the community regarding the import of tree nodes. They have described the process that will be done for mapping trees and that it is basically similar to what other tools do for mapping specific elements. MapComplete and MapContrib do the same process described= see a tree, map it, correct the position manually using satellite images if it has been misplaced. I wonder if the same fears have been expressed before the announcement of other mapping tools coming from well-known developers in the OSM world? On the contrary, before the announcement of new OSM mapping tools by those who make a first approach to this world, it is possible to read a cascade of comments related to the fear of doing something wrong. Regarding the correct positioning of any element, it must be taken into account that it is a process that has already been overcome with the different developments implemented to do so. And mapping a tree is not the most difficult thing to do in OSM, I think that was my first edition , a tree in the middle of a meadow, I don’t remember anyone writing to me asking if the GPS was ok, what was the margin error of my device or to take a photo of the bark of the trunk to later take the coordinates of that photo and thus know if it had been well positioned.

Suggesting that 3 photos per tree be taken, I don’t think it’s the best suggestion I’ve heard, saturating the OSM database even more with unnecessary elements and that I think are not the objective of the tool moderately described on this site, the usual would be a single image well taken.
How many images can be linked per node?
One: image=* (unless you collage and edit all 3 photos to fit in one frame); plus Wikimedia and Mapillary, that would force developers to upload the images to other databases or use existing ones, a difficult process for one tree and almost impossible for thousands of trees, which would force the use of a generic photo per species and that would annul the initial thesis of the project. I think the photos are going to be used to recognize the tree species by a recognition engine, but not to be linked to the OSM node. What engine will be used? Will it be based on Google images?


My understanding is what is being proposed here is different. With MapComplete, etc. each user is logged in under their own OSM username, they are making edits while looking at existing OSM data (avoiding duplicates and topological errors such as trees on top of buildings [not entirely impossible, but unlikely]), and the resulting edits are uploaded to OSM rather quickly. My understanding of the proposal is that the edits from all of their users will be grouped together, and then uploaded at some later time by one of their employees, presumably under an OSM account dedicated to this task. Further, the user will not be directly interacting with OSM data, so cannot reasonably avoid duplicates nor topological problems, and those things will have to be dealt with through the bulk upload script.

Even if their users did a perfect job of collecting trees, the bulk upload process provides a lot of opportunities for things to go wrong, which is why we want to see the full script/program and the data.

If an individual OSM mapper uses something like MapComplete to add a few trees, the community has a chance to review before too much damage is done, and has the ability to provide feedback directly to that user through change set comments, but that would not be the case with what is being proposed here.

So based on this whole conversation, we would like to confirm that we are using GeoTagged photos, the photo taken of the tree will have a default corresponding lat. long.
Based off our testing, the location of image is quite accurate to where it should be. We also have a manual intervention option for the users to change the location pin if it is not that accurate.

The current version of our app is saving such info onto a feature layer on a private map, just like the openbenches website, we currently are hosting trees sent in by users in a database.

I was preparing a second block to address this topic but you have described and summarized it better than what I had prepared, in all the conversation of this topic it was not clear correctly how to enter data into OSM, except for a reference to the script.
The method of creating OSM data (collection of data from different users to be injected into OSM) has already been clarified, new questions open=

  • What would be the verification and validation method before uploading data to OSM?
  • How to verify that the data is not already present in OSM?
  • How to avoid duplication of data?

I live in a medium-sized city (500,000 inhabitants) and the issue of trees caught my attention, the municipal administration makes certain data of public interest available to the general public. I got the database of all the trees registered in the city during the last 2 years.
This I face a number of problems already expressed in my previous questions. What bothered me the most is that the duplicate validation process became quite cumbersome and complicated, And this was only the first step focused on the geographical position, to which was added the verification of TAG’s related to the species of each tree. And the questions keep growing=

  • Who does the verifications and validations knows about labeling of tree species in OSM?
  • Does this person have enough experience to determine by means of a photograph if the labeled species is correct or, on the contrary, it must be corrected?

When reading “worldwide” in the title of this thread I imagine that an infrastructure has been created corresponding to what is intended to be done= this would be both the mapping tools and the validation and verification tools, training of a work team that corresponds to the challenge being addressed, …
I hope that this challenge will be addressed responsibly.

I wasn’t proposing those images go into the OSM database: rvaisnavi said the aim is to track ecological changes, as well as get to know tree locations, and as you say, the photos will presumably be processed somewhere else. If you want to know tree health, a distant shot is not that helpful.

Even if the aim were just to locate the tree, then a “useless” photo up close gives a silent GPS fix on the trunk: it works with the grain of human behaviour because it asks a user to do something intuitive that they set out to do anyway.

I don’t think we’ve been told how expert the users are, but I’ve inferred they’re novice (hence a hand-held app). But I think we still don’t know which organisation is behind the project, so maybe I have that wrong. @rvaishnavi ?

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But it would be taking the position of who took the photo, but not the position of the tree. It must be taken into account that there are hundreds of data around and that this data must be preserved so that simply because of a position error a tree is not placed in the middle of an highway or on the roof of a house I read earlier that the position will be corrected manually but here you insist again that the exif data of the photos will be used mostly.
Personally I would not recommend using the exif data of the photos, to photograph a tree correctly the user will be an average of 5 meters away from the tree.

I suggested they take a close-up photo of the trunk [edit: of the bark of the trunk]. That helps enormously with identification, and it puts the user in the same place as the tree.

Interesting on the limitations of EXIF - or did you mean the limitation is that (to correctly cover the tree) the person will be standing at least 5m from it?

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Hello everyone, really do appreciate all the feedback and advice that we are getting from the community to help us make our open science contributions more in line of the standards of OSM.

To answer the common questions, we’re getting:

  1. The purpose of this application is for usage by the environmental enthusiasts to map the trees, to help report on the ecological conditions of an area. So similar to how OSM mapathons happen, we (our organization) too plan on hosting a mapathon to get people to enthusiastically and easily map trees in their surroundings.
  2. We are not zooming in for any of the photos, in fact, while giving instructions for better photo quality of the plant, we urge users not to use the zoom function on their phone cameras.
  3. Reiterating my college: we also have the ability to only allow tree submissions if the Horizontal accuracy is within X (my choice) meters. In my code, I can even make it so that trees that are close but not necessarily at the same GPS will not be added to OSM to avoid duplicates. The way we have it right now is no 2 trees at the exact same GPS can be added, but we can change it to no 2 trees within x degrees lat and long can be added (which we plan on doing soon, based off the responses we got from this community today)
  4. We also received a suggestion from the talk mailing list, to keep a buffer of 49 feet from existing trees in OSM to avoid duplicates. We will look into that too.
  5. We’re using a citizen science project - - to help with the plant identification part. They have a state of the art accuracy approximately 90% accuracy for classifying plants based of plant images alone.
  6. Based off our testing, the location of image is quite accurate to where it should be. We also have a manual intervention option for the users to change the location pin if it is not that accurate, where users can see the point pinned on an Imagery Basemap before submitting the geotagged photo. In this way, it is as easy as pinning your pick-up location on Uber, thus making it very simple for the user to move the point to a real existing tree on the Imagery Basemap.
  7. As for the idea of taking an image of the bark of the tree to get more accurate GPS results like @eteb3 suggested, our app only takes in ONE full picture of a plant for every submission.

Also just wanted to follow up on my colleague’s message. We can turn the verification process on by using the following tag for our changset: review_requested=yes

Therefore, until we can ensure a verification process in our own team, we will keep this tag on. We understand OSM users care about the accuracy of their data. As for our organization, we are associated with the FGDC or Federal Geographic Data Committee.

They do it mainly by caring about their fellow contributors being accurate. The “review requested” flag is not intended to ask other contributors to correct low quality contributions but to ask feedback and help from confirmed contributors so that the next time quality will be better.

In your case, as we cannot contact your users, it is your job to ensure decent quality of their contributions by guiding them, verifying their contributions and providing feedback to them.

Anyway, there will be changeset comments from the OSM community and you should react within reasonable time and in a competent way if you don’t want to loose confidence of the community. This requires probably that you contact your users to get “on the ground” information on controversial contributions. I hope you took into account the effort needed for all of this.


@Harsha-som and @rvaishnavi agree with the comments concerning accuracy lets go with review_requested=yes.