The Optimist Circuit

Mapping Communities for Social Impact

• Ellen • Season 1 • Episode 4

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What happens when communities invisible to the digital world suddenly appear on a map? Lives change. Resources flow. Possibilities emerge.

Rebecca Firth, Executive Director of Humanitarian OpenStreetMap Team (HOT), leads a global movement that’s reshaping how we think about maps, data, and community agency. With 750,000 volunteer mappers across 90 countries, HOT has illuminated regions home to nearly a billion people—bringing them out of digital darkness.

Maps aren’t just for navigation—they’re critical tools for disaster response, public health, climate resilience, and social equity. Yet millions remain unmapped, unseen by systems meant to support them.

HOT is changing that by putting mapping power directly in the hands of local communities. Through initiatives like their FAIR project, they’re training neighborhood-specific AI models with communities, not on them—ensuring data reflects lived realities.

From flood planning in Liberia to forest conservation in Guatemala, the results are transformative. In Sierra Leone, a map source reads simply: “I live here.”

Rebecca shares how open data, community ownership, and ethical AI can shift the global map toward equity—and why mapping is one of the most powerful tools for justice today.

🎧 Listen now to learn how you can help build a more resilient, inclusive world—one map at a time.

👉 Start mapping or sign up for the newsletter at hotosm.org

 🔗 Explore more links: linktr.ee/hotosm
📸 Follow along on Instagram: @_hotosm

Sponsored by EVCHARGE4U—your partner for fast, reliable EV charging solutions for homes, businesses, and fleets. Powering a cleaner tomorrow, today.


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Speaker 1:

Welcome back to the Optimist Circuit podcast, where we are connecting AI, tech, nature and people to spark optimism and power real solutions for society. Today's guest is someone who embodies that exact mission, and I'm so thrilled to have Rebecca Firth, the Executive Director of Humanitarian OpenStreetMap, on the podcast today. Rebecca leads a global nonprofit that's using open data and participatory mapping to respond to disasters, empower communities and build more resilient futures. Her leadership under the Humanitarian OpenStreetMap, otherwise known as HOT, has scaled its impact across the world, from launching regional hubs to innovating with AI through Project FAIR. Thank you so much for coming on.

Speaker 2:

Thank you so much for having me today.

Speaker 1:

So I wanted to get started by understanding a little bit more about the big picture. So what is Humanitarian OpenStreetMap and what is the impact that it's having?

Speaker 2:

So HOT was created to address a problem. The problem we were addressing was that disasters around the world were affecting, displacing and killing millions of people, but a lack of data, a lack of precise data about the locations that were being affected was hindering efforts to respond to those disasters, so aid wasn't reaching communities. Things like logistics, which are a huge part of disaster response, were incredibly difficult, and all this meant was kind of missing data, was really causing human suffering and loss of life, and we came together to sort of solve that problem. It was an initial idea that, as you can tell, was very based around disaster response, but since then has really broadened out into so many different impact areas. You need data for everything from climate adaptation to public health, to gender equality, to also still kind of you know, anticipatory action for disaster and disaster response, which is still a huge part of who we are.

Speaker 2:

But this kind of one initial intervention use case of data for disasters has really broadened out into kind of an explosion of data to solve human problems. There is something really unique about, about HART, which is that the data that we we support is it's not created by us. We have a small team of 75 staff and we have 750,000 mappers around the world that are creating that map, that are creating the data needed, and they're working across 90 countries. So it's a really incredible geographical footprint and all kind of very much led by that people power. You know hundreds of thousands of volunteers working with hundreds of partners to make sure that data is available at the right time and can be used to improve decisions in a humanitarian or development context.

Speaker 1:

And I think that's so interesting because I don't think when people think of natural disasters or these other sorts of human problems, they don't think of mapping as being one of the main issues, because for most people who live in maybe more developed countries, they just open their Google map and they don't think twice about it.

Speaker 1:

So could you explain a little bit more about how you guys are actually creating these maps in these areas that are unmapped and how that is helping in these natural disasters and maybe some of the other use cases that you have?

Speaker 2:

Yeah, I'd love to and I think that's such a great point. Right, if you live in an area of kind of data overload, it's really hard to imagine data scarcity, but a lot of the locations we're mapping. It's not just that there's no map, there's also no regular census. The last census might be 30 plus years ago. There is no accurate digital birth registration. There is no accurate digital birth registration. There is a lot of data missing and a map is often not just a map but it's a proxy for other missing data. Sources like that can tell you how many people live in this area, for example. Also, if you live in an area where kind of you know maps are really common, you forget how often you actually see a map in your life. Right, and actually when you start to think about that, you watch the news or something like that and you see maps everywhere.

Speaker 2:

Maps describe the problems that we experience and from anything from COVID to wildfires to you know really basic things, like you know a train line being down for a few days. You see those problems on maps. They are problems that are happening in a particular way in a particular place at a particular time, and the way that we understand and digest those problems is to map them. So you might not think initially of maps being this kind of massive part of the jigsaw problem like puzzle of solving problems. But then actually, when you kind of consider, well, how do we understand these problems and where they are and therefore how to fix them, so much of that is kind of via the medium of a map.

Speaker 2:

Um, so while we map physical things like infrastructure, buildings, roads, rivers, we also map problems. We map things like the vulnerability of certain housing. We map things like the like temperature change or air quality, things that kind of go on top of that map to help you actually answer questions and get real insights to improve decisions. The mapping process it can be complicated or simple, depending on what the problem is. But at the most basic level we get satellite imagery or drone imagery either. Then there's a simple process where you draw over the top of that of that satellite imagery to draw buildings, roads, things like that. You kind of trace the map. I call that and it's a bit like using Microsoft Paint for anyone of my generation who grew up using Microsoft.

Speaker 2:

Paint a lot yeah it's like circles and squares and squiggly lines. Now we have a really cool AI tool that can help to take that kind of that initial mapping you've done and use that to train, uh, making the map on like a larger part of that neighborhood, um, which I can talk more about um later on. Um, but, uh, you know, that gives you your, your basic map. But then you want that local detail. Right, you want place names. You want to know what is this building used for? Is this hospital? Is this a school? Is this the home? Is this a shop? Um, that information all comes from the local community. Um, so we spend a huge amount of our effort growing that local community of mappers.

Speaker 2:

Um, obviously, the remote contributors are a huge part of kind of where we've been and the journey to date, and you know, drawing those maps remotely is important. But that local detail is the thing that we're really focusing on growing as much as we can at the grassroots level to make sure that communities are part of the mapping process, that they understand that their map, that they can use and advocate with their map for those services that they need. You have to remember that you know indigenous communities, low-income communities, informal settlements like slums. Traditionally, those are communities where maps and data are not things that are common in those communities, but they may well have been used against those communities. They may have been used to ignore that that community exists. They may have been used to justify taking resources off that community. So it's super important to us that you know community buy-in and community-centered mapping is kind of you know, part and parcel of every project that we do.

Speaker 1:

Yeah, that's very true.

Speaker 1:

I watched the TED talk that you gave about humanitarian open street map and it really illuminated to me how important this is.

Speaker 1:

When you talked about when you were living in Peru and you needed to get places, and the gentleman just like drew a map and you had to like follow his squiggly lines and like try and estimate his time of how long to get from one turn to the next, and it that like really illuminated to me.

Speaker 1:

Oh, like they're like mapping is extremely important. And then you connected it with the hurricane in Puerto Rico and how. And then the map that you worked with the people in Puerto Rico lit up with all the homes and all the locations of the streets and everything for how to get them access to food and water. Talking about of getting community buy-in is also extremely important because, as we've seen historically, like with redlining and other things, mapping can be used negatively against people, and so I'm curious what inspired you to get involved in this work? Francesca, my co-host of this podcast, used to work with you and she mentioned that you guys would have after work pizza parties and talk about mapping or something along those lines, and so I'm curious what inspired you to get involved with this type of work yeah, I am.

Speaker 2:

I have some really happy early memories of my early mapping days with Francesca in very kind of different lives for us back then, different careers, both working in social impact consulting, but probably, I think, fair to say, also both looking for that thing that really kind of set our hearts on fire that we really wanted to dedicate ourselves to. And back when I was working with her, that was when I found mapping um. I'd been working in social impact consulting I a lot of that was around digital um and I had studied geography um prior to that. So I was, you know, I was really wanting something that kind of connected these interests of mine and I wandered along to my first mapathon. It was the very first ever mapathon, in fact, in 2014, from something called the Missing Maps, which was a big kind of partnership that Hot launched with several other major NGOs Doctors Without Borders, american Red Cross, british Red Cross and the goal of it was really to get people like me into mapping. And you know, for me it really worked, because I have now been working in mapping for the last 11 years as a result of going to that one event.

Speaker 2:

I think the thing that really struck me in that first event, and that fuels me now is is two things. The first thing was the shock at the lack of data about the places we were working. So that very first, mapathon, I googled the place and the first hotel recommendation that came up up on google was 1400 kilometers away, in another country. Oh my, which just like, shows you the scarcity of the data in that place. Right, that is a phenomenal lack of data, you know, and we've solved a lot of that, those problems in the last 11 years, right, but there's still more situations like that. There's still hidden, hidden problems that we need to get data for.

Speaker 2:

The second thing is, like a real focus on do no harm. So do no harm is like, you know, it's a, it's a principle in the medical professional, medical profession, it's a principle that a lot of people work by in the development space, which is that you know it can be really hard to do positive work in the international development space. You're working at the intersection of market failure, political failure. You know you're solving the world development space. You're working at the intersection of market failure, political failure. You know you're solving the world's difficult, most difficult problems that no one else has been able to solve your customer like your client. The community you're serving and the person who's paying you to do that work are not the same person, so you have kind of conflicting objectives on all sides and you have a lot of programming that you know is accused of things like replacing local services, and then the program winds down and those local services cease to exist. The population's actually in a worse position than they were before you did your work.

Speaker 2:

There are so, so many examples of where international development work, done wrong, harms local populations. And one thing that just really struck me with mapping was like I think it's really hard to do serious harm with a map. We're providing data so that other people that are already taking decisions can do those decisions better. Maybe we'll make a mistake and it won't be the best possible decision they could make, but it will still be better than if they took a decision with no data at all. And I just really felt like what a great thing to contribute your time to. It's so needed, it's so obvious. There's such a strong kind of justice element in just the visibility of being on a map allows service delivery, disaster response every kind of element of that makes a life good. Right health, waste management, um, water, like all these things need maps, and so it just felt like such a kind of positive thing to spend time on um, and it's been really productive as well.

Speaker 2:

This is a movement of people. There are 750 000 mappers. We've worked across 90 countries. We've mapped an area home to nearly a billion people, which is incredible. And you know we're just getting started. That's been, that's been the simple work up until now, just making those base maps in that giant area, and next we're looking at much more complicated mapping for the world's most kind of sort of troubling problems around climate adaptation and things like that. So, yes, it's been a great ride so far.

Speaker 1:

And I remember in your TED talk you said like you're literally putting these communities on the map, because there's that saying, like you know, put putting something on the map and that's the powerful work that you're doing. And so I was wondering if there's a specific example of a community that you have worked with to help map their area that's maybe related to climate resilience that you could share, of how you guys began working with the community, how they adapted the process for their area and any benefits that you may have seen from that.

Speaker 2:

So, to offer just a slight build on what you said, I think what I'm really focused on is how can we get as close as possible to the community, putting themselves on the map. So we want to map every community, but we want every community to be like an active agent in building the map of where they live and therefore representing the issues that they care about. So I sometimes think you know, if you think about data like with a Western lens, you consider data standards, where data should look exactly the same in all places, but if you consider data from the community perspective, you think what is the minimum data we need to solve this problem and you come up with really different solutions. So I'm going to rattle off a few just to give a taste of kind of how this varies around the world. Kind of how this varies around the world.

Speaker 2:

So one example in Liberia we were working in a coastal region that was really vulnerable to increased flooding as a result of climate change and they needed to understand the vulnerability of the local population to that flooding and went around with a team mapping the area that was, mapping the depth of the foundations of the buildings. By how many fingers deep they are. That doesn't sound like data in the way in which you might study it at university, right when you've got, you know, four decimal places and really advanced measurement system. You know my finger is probably different to your finger, but it's true that there's a very big difference between four fingers and one finger, no matter who you are. So this is minimum data that you can gather really quickly that can help you solve actual problems in real life. Um and we were talking about this and one of our colleagues, um, from kenya, actually said in her village when they grew up, when there was flooding, in order to go to the village, go, don't go kind of up the chain and tell people about the flooding. They would put a child in the flood and then be able to say this flood is the height of a four-year-old or the height of a six-year-old or the height of an eight-year-old, right. So this is a very common, tried and tested community method of gathering information and what we're looking at is we don't want to do that, we don't want to go putting children in a floodwater, but how can we take that concept so that we can radically gather scaled data to solve actual problems really quickly? Because if you're trying to get a team of people to go out there with rulers, it's going to take you 25 times as long and cost so much more.

Speaker 2:

So that's one example from Liberia, another example from Guatemala.

Speaker 2:

Another example from Guatemala we did some amazing work last year supporting indigenous communities who wanted to use open mapping to manage the forest area that they lived and to monitor fires and illegal logging in that area and, quite incredibly, they managed.

Speaker 2:

They mapped the area, they got information and open data on those challenges. But then we're able to use that to access government funds that could be unlocked when that kind of key conservation data was provided, so that community then could demand more resources from the government to support their own kind of sustainability. So it's a really amazing example of open mapping, because you not only have this kind of localized challenge that is now being surfaced and can be visualized, but you also have that map leading to resources to solve that problem being unlocked and the community power increasing in that experience, because they're now the ones that can direct those funds to better sustain their lands. So those are a couple of examples, but I could go on and on about this forever. There are literally thousands of mapping projects all over the world that do incredible things, especially in the climate adaptation area at the moment.

Speaker 1:

Wow, that's incredible to hear that the work that you guys are doing was literally empowering the community to better manage their resources in their area and really help them get themselves on the map, as you said. You know, hot is, by nature, very community-driven. As you said, like the way that you map based on a community versus the way that you would map in a Western sense sounds very different. Like I would never think of using four fingers to measure anything, I mean, I guess, unless I was in a hurry or something, but that's so interesting to hear. So what are some other lessons that you've learned in like really building this from the community up and empowering them? Our previous guest, sydney Thomas, talked about her lessons on community building, and I think building community through technology is a really powerful thing, so are there any other lessons that you could share around that?

Speaker 2:

I think in our context, the most tricky challenge, and where we're kind of constantly looking at solutioning, is maintaining that community engagement in locations of severe resource limitations. Um, and that is a really tough balance, right, because we need to, uh, fund gaps in community resourcing in order to kind of enable work to happen, but we also have to manage, you know, political contexts, natural disasters, etc. That are way beyond our remit to necessarily predict or kind of solve really or kind of solve really. So one like really strong example of that is you know, haiti is a really important location in our evolution because that is where we did our first ever mapping project in 2010 after the Haiti earthquake, and HOT as a kind of concept was sort of established and proven in that earthquake response. And it's also the area of kind of the greatest, some of the greatest humanitarian suffering and humanitarian need in the world, which means it's a location we really want to invest in and work. But it's obviously incredibly difficult to do that in. You know, the government of Haiti has been inactive since 2019. It's almost entirely controlled by gangs. It's a very, very difficult location to work, and so what we try to really focus on is if we're providing technology. It needs to be technology that is native to that community. People need to be able to use their own devices to do that.

Speaker 2:

We often talk about local people, local tools plus open knowledge. So what we want to do is, if we're working with local people with local tools they already own, if we add open knowledge, then you can really do something. Because if you have to import that equipment, the second it your work is over. There is no sustainability, no one can afford to fix it. No, no one locally knows how to fix it. No one can afford to replace it. You know it's done. So working with local tools is critical. Um, the second one for us has been creating kind of low friction online spaces. So creating spaces, people can learn, people can ask questions, troubleshoot things with support from other community members, be in community with other community members who are trying to fix the same challenges that they are, and we have so much kind of cross-pollination of you know. We actually had a thing recently where we had a community that we were working with in Tanzania and a community in Brazil who were both working on the same challenge and were able to kind of connect virtually to kind of share learnings and knowledge and advance their work. So those kind of low friction online spaces where your community can really connect has also been just absolutely critical for that. So, yeah, those pieces have been important but there's there's so so much there.

Speaker 2:

It is really hard to build community and I think the word has been, you know, overused and um minimized a lot in recent years. You know, you kind of get to the point where every, everything's a community, right, I bought a pair of yoga leggings a few weeks ago and all of a sudden I'm like in the yoga leggings community, right, that's what they email you saying you know, thank you for joining our community. And you know, I'm not a community, I'm a paying customer. And I think you know it's made it hard for people who really do work in community-led solutions like ours to really kind of articulate the difference between being truly community led, being um, providing a solution that um, yeah, is is kind of all about the community, when that's a word that has just been so like minimized and overused in in recent years. But it's still important and I think we need to kind of dig our heels in and and reclaim that because um, it's, it's an important tool for uh, conveying, describing um to people, uh, how this work actually happens.

Speaker 1:

Yeah, that's very true. I feel like that just shows you the power of the word community, because it really brings a sense of belonging and, um, you know, being a part of a larger group. That makes you feel like you're not alone in this effort and that's really cool to hear that you had different people from different geographies, from like brazil to other places, connecting with each other to learn from each other and, um, like you're saying, different types of low friction technology to learn from each other.

Speaker 1:

So, thinking of from you mentioned the very beginning, where you guys were working in Haiti to where you are now and we're currently speaking of tools in this AI revolution. You guys, I know, are pioneering tools that use AI. I like, I believe, your tool called FAIR that helps people use AI to map. Could you talk a little bit about what that is and how that works?

Speaker 2:

FAIR is something I'm so excited about. So FAIR is an AI tool by which anyone in the world can train an AI model to map their neighborhood. So it's kind of the opposite to the sort of, you know, west Coast based AI that you might be more familiar with, where you kind of have one model with absolutely shed loads of training data behind it. We're creating individual AI models for every neighborhood in the world, trained by people who live in those neighborhoods. So it's a completely different approach. We need for this to work millions of people all over the world to train models to map their area. But our goal isn't that, you know, that AI model is then extrapolated to map the entire world. Our goal is that that AI is mapping just that area. That's important for accuracy. I think some people think you can just train an AI in San Francisco and then just apply it around the world and it's going to work, and that is not the case. It is probably going to work in a capital city where you have, you know, every building is made out of man-made materials and has a really defined right angle and is next to a road which is clearly made of asphalt and has a very you know we think our neighborhoods look different, but they don't look that different. The locations we're mapping you might have kind of a brown hut made of kind of locally available materials, could be anything from corrugated iron to kind of vegetation. It's not uniform in shape, it's an irregular shape and it's on a brown landscape surrounded by loads of brown trees with no leaves on, which also look a lot like those circular huts, right, and loads of rocks, right. You look at that imagery. It is incredibly difficult even for a human to understand what is the building, what is the tree, what is the rock Right? It is really really hard to train an ai in one location and apply it to map another, as once you go into kind of a low middle income setting especially you think about something like a slum you have overlapping eaves on almost every house in a slum. Um, that means how do you tell when one building has ended and another one has started? You might have one roof that's made out of five different materials. So how do you know that that's? Is that all one building? Is that multiple buildings? It is really really hard. But what these locations do have in common is generally the kind of building practices are similar within a community. So there are similarities to this particular slum in this country. There are similarities to the way in which housing in an example I was looking at recently was rural Morocco. The housing has a particular form that is specific to that area, which means you can train an AI to map that area.

Speaker 2:

So our process is it's human in the loop AI, which is something that we really believe in. There is a human role in kind of training and validating that AI. So you'd get, say, a set of images of the location you want to map. You would do your kind of microsoft paint drawing map over those areas, um, and then the ai will use that to map the surrounding neighborhood, um, and then you'll have the chance to verify that. So it will. It will show you that map and say, is this good? And you can say, no, that's rubbish. And you can show it kind of what it should be, and it can have another go, and it can have another go again and again until you get to a level of quality that you feel happy with.

Speaker 2:

I'm really excited about it because this puts AI in the hands of communities, the communities that we work with, that we care about, that we want to increase power for are communities that tools like AI are applied, applied on them. They are not active stakeholders in um, so they are training the AI and they can set the quality threshold that they're happy for that to reach, which I think is something that, like, a lot of people would like to be able to say. Like I feel like my data has been effectively considered by this AI, so I'm going to trust its results. That's not a power that we have over many of the AIs that are applied to us, and the goal of this is to kind of accelerate mapping speed and make kind of mapping something that can be faster, mapping something that can be faster.

Speaker 2:

We had a great go with it in Monrovia last year, a project we were doing with Slum Dwellers International, which is a local partner of ours, and they were doing a very large house-to-house survey across informal neighbourhoods in Monrovia and they but they were planning this massive survey but they didn't have a map to plan it with, to say sort of which surveyors are going, where have we captured all the households, how will we know? When we finished our survey, and so we gathered drone imagery of the area. Then we manually mapped 650 buildings which trained the model, and that model will then take a couple of hours to map five and a half thousand buildings, which gives you that really high quality map of all the buildings which can be used as a basis for that survey. And that survey was something that was assessing how to improve vulnerability in that neighborhood, provide a map of a whole area really quickly that can then go on to inform, inform kind of vulnerability improvements. It doesn't always work so easily.

Speaker 2:

We were using it a couple of weeks ago in Sierra Leone in also an informal neighborhood, a slum, where the housing wasn't that uniform. So it created, I'd say, more of a medium quality map that needed more ground truthing and more working with. And you know that's what we're really working to improve is you know how can we get that quality as high as possible, as frequently as possible, so that this can be a more usable tool? But yeah, we're getting great results so far and finding it to be a really a really exciting way to kind of provide, provide AI in the mapping, in the mapping sector.

Speaker 1:

Okay, so that makes sense why it's really important that the AI and the model that it's based on is built locally, because the patterns that you might see of the houses in one neighborhood are different than the patterns of roofs that you might see in a different community, and then also that way you get community buy-in. The people are like we made this model, so therefore I trust it Because I think that's a large part of the mistrust in AI is we don't know what went into it.

Speaker 1:

We don't know how it was built. I don it Because I think that's a large part of you know. The mistrust in AI is we don't know what went into it. We don't know, how it was built. I don't know if I can trust it.

Speaker 1:

How are they using the data? And that you kind of answered my question that I had of you know how do you balance this innovation of AI with ethics and equity? And that kind of answered that question, but I don't know if there's anything you want to add with that in terms of how your organization thinks about the use of AI in general, with that balance.

Speaker 2:

Well, I mean, ethics and equity are absolute critical challenge for us because if we work in the intersection of humanitarian development, where ethics is critical, data where ethics is critical and AI, where ethics is also critical, you know, this is a really, really important space and there are locations where we've decided previously we're not mapping there because there's a risk that the map could be used for negative intent, and so we have a very careful kind of um process to go through those considerations. It's. It's usually in places of kind of um, of conflict, um, in terms of the ethical consideration, uh of ai, I think one thing that we're really hoping to evolve our AI into is, you know, right now it's a tool to map one thing in the world, right, it's a tool to create that kind of building base map in the world buildings and roads. We want to move it into more kind of complicated identifying, more complicated features like building materials, looking at the business road of passable quality, etc.

Speaker 2:

But the real long range goal for us is to be a home for all geo AIs to come together and we kind of see that as like a unique role that we could play in.

Speaker 2:

You know, our strength is boots on the ground in communities all around the world and AI's weakness is not having that right. And so one kind of, I guess, unique role we think we could play to try to address that is evolve our AI tools into a platform where kind of anybody who's making a geo AI can put it there and benefit from communities around the world training it and really sort of try to lean into encouraging people to do that more, encouraging the quality of AI to be better locally represented by that kind of a play. But obviously that's it's's. It's a big goal and it would be a kind of complicated, probably multi-year step to to develop, um, that from where we are now but, um, kind of ethically, that's very much the direction we want to go in and how we see that kind of we could play. Uh, we could play a role there that that's quite unique, that I think you know this sector really needs and addresses, yeah, the criticisms that a lot of people have about AI right now.

Speaker 1:

Wow, that would be really cool because, yeah, it sounds like you guys do have the mixture of what's needed with, like, boots on the ground to map, and then bring in that the local AI, modeling and mapping, um, and it seems like to get that sort of thing done on a global scale. Um, it could be helpful to partner with others, and so I was wondering if there's like a dream partner that you would love to work with, or somebody you already are working with, um to sort of achieve that type of goal.

Speaker 2:

Yeah, you're absolutely right. We do nothing alone, literally nothing. The point of mapping is it's made by the community and it's used by the community and partners who are working in that community to do their work better. So, yeah, partnerships is critical for us. We have about 90 active partnerships, but we have hundreds of partners that download our data every month and use that in their programming. If you were to ask me about a dream partner, we have one right now that I am super enthusiastic about kind of pilot and small projects, stages and I I can see a kind of space in which this is going to flourish and be a really critical part of um, our future um.

Speaker 2:

I talked earlier about slum dwellers international um and they are. They're an affiliate network of community-based uh organizations of slum dwellers around the world and they have dozens and dozens of kind of major cities as part of their network. They've been doing a project for about 10 years called Know your City where they're trying to get kind of more locally generated data about these slums and we have been working with them to develop open mapping on a massive scale as kind of part of that solution and it's just proving absolutely incredible. With quality open mapping you can do things like create and provide land rights for slum dwellers who don't have land rights and therefore are not incentivized to invest in their home because it could be taken away from them. You can help city governments not just acknowledge that those places exist but provide services to them in a meaningful way, and it's really hard to provide services in a slum right. Think about things like rubbish collection. Doing rubbish collection on unplanned roads is really high because they're not wide enough for your rubbish truck right. Um, it's like so, so basic, the kind of logistical challenges they're. They're so, um, hard to overcome and you need to have detailed information to figure out. Like, what would the right routes be? Um huge for climate adaptation.

Speaker 2:

We know that um, heat especially, is going to be a massive issue in urban slums. Um, because you have kind of buildings made usually in the most, uh, most marginal areas with the worst quality materials. Um, but things as simple as, um, you know, painting roofs white, um, and or kind of upgrading roof material from metal to another another material can make a huge difference to indoor temperatures. Um, and so I'm, you know we're just at the beginning of this journey with um slum dwellers international. We've been working for it kind of a year or two now on kind of pilot piloting, pivoting.

Speaker 2:

This approach, um, and and I really think it's going to be, uh, something that's gonna gonna provide kind of the basis for a huge amount of positive change in the world. And, yeah, I'm really excited because they're they're locally embedded in so many places. Um and um, yeah, the work coming out of that, that of there so far, is incredible. We had this um saw an amazing screenshot of some mapping that had been done by a slum dweller in Freetown. Uh, and when someone maps, they, they do the mapping and then you have to put the source of the information, and the source just said I live here and I thought that's amazing.

Speaker 1:

Right, like that's, that's incredible, like that is that's what we want oh my gosh, that's so cool and that my next question was going to be like can you share a story of success from that? But honestly, that sounds incredible of you know, the homeowner themselves putting themselves on the map, and sounds like a really exciting partnership, because those are areas of critical need for support and so the work that you guys are doing there is.

Speaker 2:

Yeah, well, our team has done incredible things there in Freetown. Just last week, we had a couple of people from our West Africa hub there training a mixture of city government and local slum dwellers and members of a local NGO to be drone pilots and to fly low cost drones across the whole of their city, and it took them a couple of weeks to fly drone imagery of their whole city. Um, and it costs between 8 and 25 and 25 times less than doing it with kind of commercially available uh drone stuff right now. So, um, we're really excited about where we're going to be able to take that, but it's cool, right like.

Speaker 2:

Our goal is, we want to bring drones, ai and kind of cutting edge technology to people who are excluded from that right now, and we're doing that in increasingly cool ways. These are technologies that can have huge impact. They just need to be applied in a way that's appropriate and affordable, and a huge part of that is developing that local capacity, those local drone pilots who you know they can, I hope, continue to be drone pilots, and we've created a whole. You know, our goal is to create these kind of new skills in the economy that are available locally, rather than what happens right now, which is those are all flown in from the US and Europe.

Speaker 1:

So our goal is to shift that, yeah, build local capacity and then give them the skills that could lead to, you know, other job opportunities potentially or, you know, increase what they're doing now to other areas and increase the economic opportunities there. That's just really incredible and that definitely gives me hope and makes me more optimistic about our future with AI and how people, because I feel like right now we're at such a critical time where we can shape how it's used and and how people you know the future of where it goes, and so I was wondering for you what makes you optimistic or what are your thoughts about the future of humanitarian tech and open data and that sort of thing?

Speaker 2:

Well, I love that question and I love your name because I would I would describe myself as a pragmatic optimist. Okay, I want to be. I want to be really optimistic about the future, but I also want to make sure we have like pragmatic and realistic plans to get there. And so, yeah, I was really excited to kind of have a chat about, yeah, kind of optimistic and positive steps in the tech space. For me, what makes me optimistic is, I think there's a growing recognition that open data is critical for sustainable development and crisis response, and there was some research done by the UN that found, for every $1 invested in data systems, that generates a $32 return just in the increased efficiency of the decisions that are taken because of that data. And we're really seeing people start to understand that and invest in it, and I am very optimistic that this is going to be kind of a trend that's just going to keep increasing.

Speaker 2:

Secondly, I think you know advances in AI, imagery generation et cetera. They're making it faster and easier to map the world and it offers us a huge opportunity to scale, but this work is never going to be done. I feel like we're going to be able to scale to be able to match that need, though. So the world is changing all the time and we're mapping in the most vulnerable context right, which means there's more civil war, there's more displacement, there's more natural disasters, which change the map, but there's also a huge population explosion, like Africa is going to need an additional 400 million homes by 2050, which means there is always going to be kind of population change, population movement and a need for mapping.

Speaker 2:

I think what makes me optimistic is, until now, we've been struggling to meet the demand for mapping. I think what makes me optimistic is until now, we've been struggling to meet the demand for mapping. We've had so much demand and not enough. You know, we were a tiny organization trying to scale to meet that demand. I feel like those tech advances are going to make it easier to do that, so we're not going to be, you know, always behind on the problem.

Speaker 2:

We're going to be able to actually hopefully meet and manage those changes in maybe not real time, but near real time, and I think the final thing that makes me optimistic is, I think, this kind of growing recognition of not just that we want the community to have a role, but like solid, quality examples we can point to where that's happening, to use to kind of explain and scale and set a higher standard for kind of community agency and community involvement moving forward for us and for other people, because I think that's something that has been talked about for a long time but people have lacked often robust solutions for so I hope that's something that we can keep improving at ourselves. We definitely don't do it perfectly all the time.

Speaker 1:

It's the ethos and the philosophy and the vision, but there are, for sure, spaces in which we come lacking there and my goal is that we can learn and improve and um hopefully uh publish and share and think in public in about those ways in which we're learning, improving, so that we can uh benefit from what others are learning and share um our experiences and that is the whole purpose, honestly, of this podcast is to share examples where people have found ways to connect AI and people and community and nature because nature is a huge part of the solution as well so that when, hopefully, somebody can listen to this podcast and think of an idea or maybe you know, want to get involved somehow of how they could do this with their community. And that's what we're really trying to do is elevate those stories and the voices of people that are already doing the great work that you guys are doing.

Speaker 2:

So if somebody wanted to get involved with your work or learn more about what you're doing, how could they do that? Well, we have masses of information on our website, hotosmorg so it's hotosmorg as well as kind of pathways to get involved, join the community, learn to map, et cetera. So really encourage people to take a dive around there and get in touch, because this is a community of people, so there's people that are here to kind of welcome you in, help you get involved and understand what it is that you want to contribute. So, yeah, do hit us up there.

Speaker 1:

Awesome, and I'll be sure to put the website in the show notes so that people can access it easily there as well, because we know easy access in the show notes, so that people can access it easily there as well, because you know easy access, low barriers, so that we can make things smoother for everyone.

Speaker 1:

Well, with that, I want to say thank you so much for coming on. I don't know if there are any last words that you want to share with our listeners about your work or thoughts for the future, but I really appreciate you taking the time and I learned a lot.

Speaker 2:

Thank you so much, ellen.

Speaker 2:

I just want to thank you for making this space.

Speaker 2:

I think so much of the public discussion about AI is negative. It's about, you know, replacing jobs and you know it's going to take over and unethical decisions and things, and I think you know, yes, those are necessary challenges, but there is also so much that AI is solving and there are so many positive examples around the world of how this is really making a difference in communities, and so, yeah, I'm really grateful to you, to you and Francesca, for taking the space to highlight those and and provide some kind of much needed optimism and encouragement, um, that there's there's a lot we can solve, uh, with the help of AI, and there's a lot we can solve with the help of technology. We just need to do it right and make sure we're doing it in a problem first, not a tech first way. So, um, yeah, grateful to you for the space and thank you so much for having us on. Thank you so much for listening. Don't forget to subscribe to the Optimist Circuit on Spotify or Apple Podcasts so you never miss an episode.

Speaker 1:

And let's keep the conversation going. Follow us on LinkedIn, youtube and Instagram at the Optimist Circuit for more insights and inspiration.

Speaker 2:

Until next time stay optimistic, stay curious and stay inspired.