Thank you all for coming here, you're in for a ride because we're talking about maps and maps of the best.
So welcome Petia from Humanitarian Opposite Map Team talking about drones.
Thanks.
Thank you everyone, thank you for joining this session from drones to data.
So you hear a lot about mapping and you hear about humanitarian open street map teams.
So I'll just start with a raise in terms of how many of you know or use open street map.
I knew there would be a lot. I think it's the best conference.
I go to a lot of events and it's less people.
So thank you for joining today.
So my name is Ilya Set, Petia Congalova.
You can see my long job title, but as you can see it's not a developer.
So you very much hear a lot about practical examples and some of the open source tools that we use.
Yeah, I'm not a developer, but I'm somebody who's worked in international humanitarian development sector for quite some time.
And I'm very passionate about the role of tech for good and open source tools that are really usable.
And that's where I feel very passionate about what we do at hot, because I think it's a alliance with a lot of my mission.
And it's my first time at post them. That's why I added this a little logo.
So it's been overwhelming, but also really really exciting and really nice to see so many passionate people in one place.
Thank you.
What I'll try to do in the next 20 minutes is tell you a bit about the organization and talk about what we're saying this open mapping ecosystem for all.
So what it means, what are the these examples and disaster response in humanitarian action and talking about the tools, which I'm sure you'll be excited to hear how you can get involved and hopefully time for questions.
So hotter the humanitarian open street map team started 15 years ago, basically response to the earthquake in Haiti.
So when the earthquake happened, there were no maps available for a lot of humanitarian respondents to reach.
At that time, hot was the number of volunteers that developed at that time one of the tools, how the task manager you hear more in a bit.
So we grew and I'll tell you about the team and the volunteers, but at the core of it is why we exist is very much as this mission statement you can say that we have local community source map data is accessible.
So people who live in the regions which I'll talk about basically have can generate this open map data and that data is used for decision making.
So that means sometimes local governments making decisions about the specific infrastructure, sometimes it's around like specific routes and sometimes it's about reaching people specifically in like poster quick and in disasters.
What is the core of it? I'm not going to go longer in this slide, but often get off. What's the difference between hot and open street map?
Obviously open street maps, you know, you're over where it's been existed for over 20 years and it's at the core. It's one of those core platforms of what we use and what we do.
And this is the difference which is kind of in simple terms trying to explain that.
A bit of the role of when we started like 15 years ago is strengthening the open street map communities and open data focusing on those regions.
So you can see Asia Pacific, Eastern South and Africa, Western Northern Africa, Latin American, the Caribbean.
So as our organization, we are an NGO, we have around 70 people, hopefully remote most of my colleagues are based in the regions and doing all the implementation of projects.
And I'm today also joined by SAHA, our tech leaders at the front and she'll be able to answer all your very very very nice questions as well.
So these are the places where we operate.
So what do we mean by this open mapping and maybe sounds abstract, open mapping ecosystem for all?
So one of the core things that we believe is that when we speak about disaster response or people having access to data is that they really need accessible technology.
That's open and free for anyone to use.
So it's a very kind of low barrier to access to people who maybe don't have GIS knowledge or have more detailed experience.
And what we say in terms of like vulnerable communities.
So they're able to contribute to the map and also use the map once it's generated.
Free simple steps in terms of explaining this like mapping process and that also then relates to the tools that I'll talk about.
So for those who've been in the geospatial truck crew probably noticed very well, but the first step is very much imagery.
Like if you have no imagery, you're not able to create a map.
So that's normally satellite or drawing imagery.
The second step is the digitalization or a lot of it is now that manually with a lot of volunteers of drawing this lines.
Polygons of where the buildings are where their roads or waterways.
And the third and very kind of simplified terms component is actually really critical, which is the local knowledge.
We can also see, okay, this is the building, but what is it?
So if we're here in Belgium and this is maybe a place in Kenya, we wouldn't know what that building is.
So having the tools where local communities can undone knowledge is that a hospital is that a local sense or maybe it's a critical component.
So what that looks like, like those three steps and this is what we've been focusing is this end-to-end mapping journey.
So what we think of it is what if local communities have no, there's no imagery.
So from nothing to actually being able to create a map that's being used.
So this is the journey which I'll talk you through.
The first step is basically the imagery, satellite or drawn imagery.
Then moving into remote mapping using the tools AI assistant mapping, adding the local knowledge and then having tools to go and to kind of down being able to download or use the map.
So before I jump into water, the tools that are related to each part of what I've just explained is end-to-end mapping solution.
I wanted to talk some examples when I say local communities can have the tools so that they're used.
So we have tried this journey, so hopefully you stay with me.
Some examples where you can see in Bali, Nepal, Argentina and Sierra Leone, where we've done this whole process of starting from the aerial image of the way to generating a map.
So I'll just give you two examples and a lot more that you can also read on the website.
So this one is in, you can see here the map is pre-town and Sierra Leone.
So what we were doing there is supporting communities in informal settlement mapping.
So working directly with some local organizations, some dwellers international,
basically working with them to collect the strong imagery to the field mapping and generate the maps that are then used in that cases to have insights into risk hazards,
but also what I mean by services in these cases was also disability access.
Another one which I wanted to play a short video, but I don't think that's going to work, so you can look it later in the slides.
There is an example of the Balinese disaster management agency where this whole process was followed to basically map the volcano evacuation routes.
And actually sound, you can talk to him later with also part of working locally with the disaster aid and so apologies.
You can watch that.
I wanted to just give you a bit more local context into that video.
Just refresh.
Hey, so the tools, so hopefully you stayed with me, I'm like, okay, how do we start in this journey of coming from, you know, no data to actually having a map.
So these are the tools that we have developed and are maintaining at the moment.
There are just different stages of development and you can also take a look at them.
So what is the gap that we are filling with the technology development?
So the first one, as I said, is you need imagery, right?
Satellite imagery is very expensive and it's also in a lot of areas where we are working with.
It doesn't have the resolution that is needed for mapping.
So one of the tools that we've developed is called the drone task manager.
It's basically allowing people to create local area imagery.
And it also, the main difference to maybe either the other software that's available is that it allows you to split the flight plan into different tasks.
Why?
So in the example, for instance, in Sierra Leone, you had a lot of local community members that were trained to do that and they can split.
So you have many people that can collect the drone imagery and then that imagery is uploaded, processed and stored.
And then it goes into open area map, sure, maybe you're familiar with that, which is basically a platform where you can search, share that open imagery.
So in the project in Sierra Leone, when this imagery was collected, it's also plotted into an open platform that's then used by others as well.
So what happens next? You have the imagery in order to generate, we use again the example in Sierra Leone, those informal settlements, it's the remote mapping part.
So if you're familiar with mapping, maybe it has the tools that you're aware of or if you're new, I would encourage you to check it out because that's your way of contributing to mapping or volunteering.
So it uses that imagery and then the main difference is it creates a project for a certain area that splits it into tasks.
So we have you can see here nearly half 1500,000 volunteers, not active all at the same time, but all of them select tasks and they do manually the mapping, which is whether it's buildings, roads, waterways, depends on the projects.
So this is part of the digitalization, so we have the imagery and then the next step is adding the information related to, like whether it's building on roads or what's specific to the project.
Another thing that we've been exploring of the last few years, it's called Fair, it's not fair for the principles where we've used the similar abbreviation, and like the RNA starts more for resilience, responsibility to the communities we work in.
You might ask why, you know, it's a basically a service to assist mappers, and the main difference to anything else that's available globally is that a lot of like global models say models are first of all not open, but they're also not trained on the data sets and the communities where we work.
If you use that model in a remote village in Kenya, it's not going to support you a lot. So with Fair, the main thing is richly to have open models to train them locally with the communities who are there, who can share the feedback directly in that specific area.
And then support the mapping effort, which was also something I was trying to see earlier.
And if you're still with me, so we had the imagery, we talked about this kind of digitalization step, and then the last part, remember that slide was okay, the local knowledge, right, if that's a building, then what, what does it mean?
It's the additional information that's added, and that's another to all the field task manager that we have, and all there are so many open mapping, and especially field mapping applications.
The key difference with this one is that it supports the efforts to coordination. So what I mentioned, a bit in the drone part, as well in the task manager is split areas into tasks that allowed coordination, which is really important,
like in disaster response in areas of trying to coordinate and collect data much faster. And yeah, it's used, it's a standalone mobile and when application that uses ODK and Q field, I'm sure you probably joined some sessions about Q field as well.
And another thing that's another tool in that ecosystem and that field mapping that was actually developed very much with the user's user focus is a lot of communities that we work with, they have no audio space and knowledge, and they're like, they don't maybe not considering or like doing the mapping that is explained earlier, and they very much said like, oh, a lot of in disaster communication happens to exist in apps, right, whether it's WhatsApp or signal.
So this is when we develop something called chat map, which basically, it was used and you see one of the apps in response to Hurricane Melissa in Jamaica, so it's a group of people where they just share their location and a photo or a video and then easily this information can then be extracted and visualized directly.
So we talked about the imagery, the digitalization, we talked about like the local data collection, and then in terms of like this end-to-end journey is like using the data.
For instance, the data that goes by the task manager in top and street map, the export to maybe some of you have heard of it.
It's another tool we maintain and it's you can download open street map data, but also some of the ways in which we also connect, especially with a lot of humanitarian organizations that need the data, the humanitarian data exchange, it pulls a lot of open street map data.
There are different data sets there, but open street map is one of the main ones.
And when we think about like the visualizations and the maps, again, there are different open source tools.
We made an instance of you map, which is actually open street map, France is developed, but it's something that our community really needed and visualizing quickly like data that's been collected and that's used.
Maybe that's a bit blurry, but that's again the response for Hurricane Melissa and Jamaica, and you can see kind of photos of before and during the storm.
And I'll have a few, I think, two more slides, but I wanted to again bring back that the open source tool that I spoke about, the key focus of them is really accessibility.
And being able to have access to generate a map that's community-owned and then it's used.
It's in disaster response, like there are so many different projects now in climate action in health and others.
And through these tools, this can happen at the moment.
Okay, so the people before I finish, which I think is a critical part, is everything that I talked about is the huge amount of effort for us.
I wanted to say huge thanks to the tech team, tech and data team, you know, for an NGO, it's a big tech team, but I would say for the number of tools that's like we're maintaining is actually quite a small team, but I want to say huge thanks to the whole team and all their effort.
And also to all the volunteers and contributors.
When I speak about like volunteers, especially mapping is like all the people that contribute to the map, but also the people that contribute to the software, like the open source contributors.
And here is like the cure called is just more recent blog that we share the stories of some of our open source contributors that.
Yeah, I want to say huge thanks to thanks to them and to all the volunteers, because that's especially with open mapping, nothing will be possible without the volunteers and the contributors.
And I hope I'll have to time, but I wanted to end on ways to get involved or things that you can check, so obviously the website, if you want to read more about different examples and use cases, you know, so read about the tools in there.
Of course, GitHub, I'm sure you've been asked 10,000 times right in this confidence about contributing, but we always welcome contributions on GitHub.
And all the projects that I've mentioned, you see the repositories, their current current volunteer projects and in ways to get involved.
We also run different working groups, so they're focusing on kind of some of them are more broad like community, there's governance and others.
One that I lead is our tech and innovation, it's an open space, so if you're interested in joining with sometimes do like testing on some of the tools or discussions, but it's an open space for the team.
Community to get together once a month.
And yeah, it's like an email and I want to say thank you and hopefully this time for questions.
So, thank you, bit, bit, yeah, there's definitely a time for questions, so I already see a fence, so please.
Thank you very much for the talk. Can the tools, et cetera, you've been using, you've been talking about be used for sort of individual research projects in anthropology or archaeology or similar.
Not, not new results to be released publicly, but not kind of public agency engagement.
So the tools are open and they can be used for, yeah, for any any purpose, it depends on yeah, I think which tool you refer to, but obviously what we do is support like communities with like training specifically in disaster response, but they're open to I know if some I have my colleague for.
More technical questions, but yeah, they are and if you do let us know if you have any questions, it's yeah, they're open for anyone to use.
Thanks for a great talk. I'm curious about this tool field TM, I think it was called it seemed very similar to street complete.
Could you talk a bit about the differences or if there could be some some cross collaboration there, maybe.
Here's the developer.
So yeah, I've been working on the field TM for a while.
Street complete is very much a more of an individual experience right, you go out with your mobile and you collect data as an individual.
The whole point of field TM is to coordinate field mapping, so it's using existing tools like Q field, ODK.
We take a area that you need to map, we subdivide it into field mapable chunks, so trying to avoid like,
traversable non-troversible linear features like roads and rivers and so on.
And then each task is then kind of sent to the underlying tool, so that could be street complete if you know we make an integration there, or Q field ODK, like the underlying kind of survey data collection up.
Sorry, the answer is.
The only thing I'll add is here, some explain the difference, but in our community people use street complete organic maps, you know,
there's a lot of like mobile applications for adding all the street map.
That's not coordinated, but it's.
Quick question here.
So do you use or do you plan to use like different sensors to like, or what are you using, what kind of data you're using right now?
Is it optical imagery, so RGB, classical RGB, radar, radar, radar, maybe a combination of those?
Yeah, it's, it's primarily optical imagery for now.
I think open area map is pretty much like predominantly optical.
Obviously more unlike the, the fair kind of the AI stuff, there's been some combinations of, you know, maybe inside data, maybe we could use thermal infrared,
or other, and particularly with drones as well.
This is all very much more like research, kind of rather than production tools.
So it's all optical for now.
Hello.
Thank you for the great talk. Just relevant to the description as well.
But is it any drone, and don't can be used or like commercial or custom, maybe custom better?
Yeah.
Yes, a good question.
So currently the drones that we recommend using are two specific drones that DJI many for pro and the DJI many five pro.
We do support many of it DJI models as well.
Lots of the workflow is currently built around DJI drones.
We do, we've recently added support for Argypilot, more acute ground controls, like more open source DIY drones that you may build, or, you know, where you load your own software.
We're targeting less that kind of market, right? Because this is more making things accessible for, you know, people in developing countries and tends to be easier to get hold of, you know, commercial.
So it's changing over time.
The main issue here is around APIs that are available, you know, and SDKs, I should say sorry.
So most major manufacturers these days don't release, you know, easy ways for you to integrate with their drones.
So it will change over time, but currently we also support the Potensic atom one, as a very cheap lightweight drone.
And it costs about $300.
So that's the cheapest drone that we support with some caveats.
But this will change.
Yes. Thank you for the presentation and for the amazing work.
I was wondering, how do you address the data protection issues, which may stem from the aerial images, like photography cars or people, how do you do it?
Yeah. So I guess it's more based on best practice.
We tend to fly out shoots between like 120 meters.
That would generate with the sensors used on the drones imagery that you can't identify people's faces at least.
Obviously, you can identify, you know, buildings, that's the whole purpose of it.
You can identify other features on the map, but, yeah, and personally identifyable stuff.
It tends to be, it's not high enough resolution for that.
If it was, then, yeah, we would definitely implement some, like, reduction of the resolution to a point where you can't identify before it's published at least.
How do you go from road, road, road images to geolocated dr. Masek?
Oh, we're just up there. So we use a tool called open drone map.
So we're on very good terms with the team, open drone map, and that's kind of the predominant open source tool that we use to take, you know, thousands of raw image images,
we have references them, and then Mosek's them enjoy single author Mosek.
We also get 3D products as well, so we'll have point clouds, and we can make digital terrain models and various other things from that.
Okay, this is all time we have. Thank you, Bertie and Sam.
Thank you very much.