🌍 AI in Africa - Radiant Earth

This is about the community and the capacity, right? And we are acting as what we call a collaborative agency, providing those resources and supporting the community to be helpful to their end users. For us, the success of us is basically the success of those end users who are building those applications. So if they are more efficient, more productive in deploying solutions into their community, we are successful because we have been able to empower them. That's really how we look at this ecosystem.

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When I think about what AI is, I find the algorithms mathematically fascinating, but.

I find the use of the algorithms.

Far more fascinating because from a technical perspective, we're finding correlations in extremely high dimensional, nonlinear spaces.

It's statistics at scale and in some sense, right, we're finding these correlations between A and B. And those algorithms are really interesting and.

I'm still teaching those now, and they're fun.

But what's more interesting to me is what do those correlations mean for the people?

All right, Me, Myself and AI is a collaboration between MIT Slow Management Review and Boston Consulting Group. It's available wherever you get your podcast. Just search me, Myself and AI. Welcome to Practical AI, a weekly podcast that makes artificial intelligence practical, productive and accessible to everyone. This is where conversations around AI, machine learning and data science happen. Join the community and slack with us around various topics of the show@changelog.com community and follow us on Twitter. We're @practicalai effing.

Well, welcome to another episode of Practical AI. This is Daniel Whitenack. I'm a data scientist with SIL International and I'm joined as always by my co host Chris Benson, who is a tech strategist at Lockheed Martin. How you doing, Chris?

Very well, Daniel. How are you today?

I am doing wonderful. I'm blessed and pretty excited because if you remember, Chris, we had A wonderful conversation not too long ago in a new podcast series that we were launching. This podcast series is sort of a collaboration with the Open for Good alliance, which is a sort of multi stakeholder group that is working to create localized training data which is one of the major obstacles for local AI innovation in Africa and Asia. And, and we're kind of having this podcast series with that Open for Good alliance to spotlight some of the things that are going on with AI in Africa. Last time we talked with Joyce Nabinde from the Macarera Lab and Joyce is back with us today. Welcome, Joyce.

Thank you, Daniel. Nice to be here. I am very excited for another podcast talking about AI in Africa and featuring another organization that's doing a lot of work in making sure that we have AI data. Yeah. Available for male communities in Africa.

I'm so excited that you've joined us again. It was a wonderful conversation before and now we get to welcome you back as sort of a co host with us in this podcast, which is wonderful. So, yeah, if you could maybe introduce our guests and the topic for the episode and then we'll jump into some of the great things that they're doing.

Yeah. Thank you, Daniel. So today we are very excited to host two people from the Radiant Earth Foundation. We have Ahmed Mohammed and Aba Badeh who are going to talk to us about the work that they're doing around AI data collection challenges, especially looking at machine learning for Earth observation. And they're going to give us the perspective that they have, what they do, how they work with the community and especially around the capacity building initiatives that they're involved in. And just to say that I have worked with Ahmed before in the past, so this is, you know, very refreshing to have another conversation with him today on this podcast. So, Hakmed and Abba, you're very welcome. And we are very excited to listen from you and to learn about the work that Radiant is doing in the AI space around the Open for Good initiative. Ahmed, you're very welcome.

Hello everyone and excited to be here. Thank you, Joyce and Daniel and Chris for having us in this episode.

Yeah, we're excited to hear more about what you're doing. Could you give us maybe a little bit of the sort of wider context of why Earth observation, why machine learning as related to the Sustainable Development Goals?

Yeah, so let me start with Sustainable Development Goals, or as we always abbreviated SDGs. These are a set of goals that were set by United nations back in 2015 with a set of targets for 2030. These goals talk about hunger Poverty, access to clean water and sanitation, climate action, life on land, sustainable cities. And basically there are 17 of them that different aspects of a sustainable society. And each country is basically progressing toward those goals depending on where they start with. And they have to regularly monitor and basically report where they are with that target. Where Earth observation comes in is we have this massive number of satellites orbiting the Earth and capturing measurements on a regular basis. These data can be translated into those targets and variables that the countries need to report and also helping those countries to better monitor their progress and see where they put their efforts. Naturally, because you have a lot of data, that's where the AI and ML aspect comes in. Because you're dealing with basically flow of data, a continuous flow of data from these satellites. It is real big data problem. It is not just a toy big data problem because we are dealing with petabytes of data on a daily scale when you think about global scale. And that's where AI can help you derive insights, get those target numbers out and provide support for decision makers to be more effective and affect the society down the road.

So maybe Chris knows this because he of his, his work in aerospace and other fields. But if I'm thinking of, okay, I understand the concept that there are many satellites taking pictures of the Earth. Like if I was a data scientist, like I'm not running the satellites, I don't really know, like I know how to access Google Maps or Google Earth or something like that. But like, could you explain a little bit of like, who runs these satellites, where are the images, how are they aggregated and accessed and that sort of thing?

Good question. So there are two big players in this operation side. There are governments that operate many, many large satellites. The US side, we have NASA and the European side, we have the European Space Agency, we have Japanese Space Agency, jaxa, we have Indian Aerospace Agency. Many of national countries have their own space agency that they operate satellites. Historically, US and Europe have been the kind of bigger ones in terms of the government sector. Then there is a growing commercial sector that is operating their own fleet of satellites. And they have been kind of booming the last, I would say 10 to 15 years particularly. And they provide a different suite of satellites and their data sets. Almost everybody is moving to the cloud, let's put it that way, where the data sits. Now commercials are providing the data through the cloud. Governments have their own kind of data stores and kind of portals that you can access the data from. But they are also now working very closely with cloud providers, all of the major ones. And you As a data user can go to those basically cloud buckets, start exploring the Data. There are APIs that you can start query and search for imagery. The government data is usually open in the sense that you don't pay for the imagery to access it. But at the end you should have your own capacity and resources and machine to receive the data. Commercials have paid the data on the kind of regular basis, but they also have, at least I would say major commercials have that. They have a disaster kind of open data program. When there is a problem, kind of immediate situation, they release open data for disaster response. They also have research programs that academics and researchers can get kind of a quota to access some level of free imagery. So yeah, data is now moving to the cloud. That's really the paradigm shift given that.

You have all these different providers with, you know, and that when they put the satellite up, obviously they are, they have their own objectives on what they care about, what you know, what the satellite is designed to collect and that it's not necessarily standardized across all of those different programs. How do you go about thinking about getting a usable data set from your standpoint from all those different sources and knowing where to go and how to put data sets together? It seems like it would be a bit of a logistics challenge.

It is a logistics challenge and actually this is one of the things that Radian started to kind of act in that sector and provide support to the community. So think about you as a user are looking for what we call optical imagery. Optical imagery is like an image that you get with your cell phone, but in this case it's captured by a satellite from like a couple of hundred kilometers into the earth's orbit. Right? This is typically what you see in a Google based map, Apple based map or any other kind of mapping ecosystem that you work with. If you're looking specific image over an area, you will go to an API and query for that image. Right? The problem a couple of years ago was that each provider had their own API definition and how they would record what we call metadata of that image. What is the cloud cover in there? What is the spatial bound? What is the time tag? We as a neutral agency in the community said, hey, this is a problem. Let's get together and come up with a standard way of cataloging our data. And this is what we have now as spatiotemporal asset catalog or stack, which is a standard specification, open source and fully community driven that defines how you're going to build your data catalog and expose it to the end user. So now, everybody who contributed to that, which is around 25 organizations, are adopting that standard because they tested out, they provided feedback, and it's now becoming the universal way of searching geospatial datasets. All the major data providers are now adopting that, and we at RADIAN use it for our own data store.

Yeah, Ahmed, just a quick question. So, for example, if I wanted to have access to the data, and you're saying you came in as a middle player to try and provide the standardization for access of this data, so where is this data stored? How can someone have access to it? Is it that easy that now we don't have to contact all the different satellite providers, but go through Radiant Earth to have access to that kind of data?

Yeah. So on our end, what we provide is specifically AI and ML ready data, not necessarily any satellite imagery. So Radiant basically is working in this sector, but providing those kind of benchmark training data sets that the user needs to build a machine learning model. So we don't necessarily provide the raw satellite imagery for anywhere on the Earth, but we have a data repository with a stack API and a catalog that anybody can access. So it's open access for everyone. Then you can come and search for, oh, I'm looking for a labeled data set of, for example, land cover classes in Kenya. And you can query our database and find the corresponding labels and the source imagery, which is usually from a satellite. Sometimes we also have drone, but majority of them are satellites that then you can use that data set to fit it into a machine learning training pipeline and build a model for, for example, land cover classification. But yeah, we have those type of data sets. Repository is called Radian MLHub and as that name says, it's a hub basically for these kind of resources. So far we have been pretty much focused on training data sets, but we have recently launched our model repository as well, which I can talk later about it.

So I'm curious, as a practitioner, maybe we can go to ABBA going from this sort of stack that's very specific and specialized to the sort of Earth observation satellite imagery world and kind of taking that and then mapping it into the formats that, you know, AI and ML people alike. What are some of the challenges with that in going from just sort of that raw satellite imagery down into actual training data sets that can be used with models.

Okay, so some of the challenges which are faced while doing that is so you have vast amount of data. Right. And making it ML ready might be a bit tricky because you need to work with data loaders and you need to find a way which you might have time series data now. Time series Earth observations. Right. And it gets a little bit tricky working with those as well. So on MLHub, we actually have tutorials as to how you could use these ML ready data, and you just pass them onto your model side of things and you train from there.

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So I'm curious, after thinking about this sort of data, what it can mean for training models, I was wondering, maybe Joyce and the others, could you help us connect this specifically to problems that are being solved in Africa and the sort of the types of data sets with this imagery that are relevant to some of those things?

Yeah, Daniel, I think that's a very important question, because if I just look at one typical example, for example, I want to understand a major problem that we have around Africa, which is the problem of deforestation. Right. So you know that forest cover is a very difficult thing. And if you're looking at maybe the authorities in the different countries, they want to understand what are the major drivers of deforestation? How is deforestation occurring in the different countries? I think one of the things that I would refer to is trying to look at this, for example, the Earth observation data, looking at forest cover, maybe. Ahmed, is that a specific use case that you can talk about and how Radiant Earth can be able to provide us with the kind of data set that governments can be able to use to understand what's going on with the forests in the different countries in Africa?

Yeah, that's a very good example, actually, Joy. So, I mean, we Know, in the climate change world, when we talk about mitigation strategies, one of them is being able to reduce the concentration of CO2 that we have emitted to the atmosphere. And that's what forests are about. Right. So forests are kind of absorbing many of those and sequestering all the carbon that we have emitted to the atmosphere. So it's essential for all the governments at the national level and international level to make sure we can monitor forested areas and stop any illegal deforestation. So satellite imagery is providing that regular observation over a region that is forested. And how AI can help with that and how the things that we do can help is, oh, a government wants to build a monitoring system that would basically run an algorithm every time there is a new observation available from the satellite and detect the boundary, the kind of spatial boundary of where are the forested areas and provide an alert or a kind of anomaly detection in the ML world. When you think about it that, hey, there was a change here with respect to the kind of previous observation, this can be at any kind of spatiotemporal scale that you think about because we have observations regularly available and they are available globally. That's the nice thing with the satellite imagery. When you have a satellite orbiting, particularly those that we call in sun synchronous orbit, which are synced with the orbit of the sun, you get regular measurements over a region like every five days, 10am in the morning, you get a regular observation and you get the same thing anywhere on the Earth. So building those models is easier with that kind of constant type of observation. But at the end, the governments can use that and have a kind of a strategy for how they want to basically stop that illegal kind of deforestation or have a monitoring system for prot areas that have a specific boundary. Nothing should happen there. No kind of construction, no built up kind of things happening. They can have a monitoring system to do that. This is a very kind of impactful and I think tangible use case for many people who think about climate change and climate impacts.

Yeah. So I guess then we can segue into the work that you do with the communities. I think that's a very important community problem. Right. That you want to understand what's going on on ground, but you can also use the Earth observation data to be able to understand, for example, forest cover change or even just trying to understand the yield. If you're looking at, for example, farmlands in Uganda or farmlands in Kenya, how does radiant Earth work with the community to be able to solve, especially the SDGs that you listed at the beginning. So how are these all connected around now? Working with the communities, particularly on the community side?

I think the role that Radiant plays is we don't want to be the problem solver because we are just one organization and problems are so diverse on the ground. But we want to be, as we say in our mission statement, we want to empower those organizations and individuals in their local communities to be able to use these resources, particularly the benchmark data sets and guidelines and tutorials that we put out to solve those problems. So we work on use cases, we work hand in hand with some stakeholders and governments on the ground. But at the end of the day, the goal is really empowering them to be able to do that themselves. That's how we kind of model our partnership and our collaboration with the local agencies. But the crop example that you mentioned is another impactful one. About two years ago, when we had the locust swarm hitting the East Africa region, after all the kind of cyclones and the wet season that there was there, there were a couple of governments in the East Africa region that were looking, okay, what are we growing in terms of crops this season and where are they? Because they need to have an immediate response in terms of what will be the impact of those swarms on the food security, how much they need to import, what is the impact on farmers, should they provide any subsidy there? And governments, some of them didn't have any basically updated map of cropland areas in their region because that is a very intensive process. If you want to go on the ground and do census every year and provide basically a baseline map of that, but satellite imagery can do that for you. If you have good reference data on the ground, not necessarily a full census, you can build a machine learning model that look at the time series of Sentinel 2 and then gives you basically a crop type of a region. And then you can have a map at the national scale that the government can use for decision making and basically having a better insight into what are farmers growing in this region. And this is a growing field in the AI and Remote Sensing Board. And I want to pass it to Abba because he's working on a problem around crop type classification and how we deal with actually regions that we don't have good reference data. But if it'd be good if you can talk about the synthetic data problem.

Because we have limited data in a lot of regions, so limited level data. So what we were able to work on is using GANS Generative Adversarial Networks to generate an image for each of the bands of the satellite images. So assuming we have just 2,000 labeled images of different regions, we can now generate much more than 2,000 based on the data which we have. And that has proved to improve our classifiers. Crop type classifier which we built. And it's still an ongoing work, but yes, it's. It's provided good results for now.

Yeah. So if I'm understanding right, Ava, that's you're sort of using the GANS for data augmentation in the case of data scarcity. So do you take that sort of the actual observed imagery and use that sort of with your discriminator in the. In this framework to create these augmented images? And could you describe a little bit was that kind of the initial solution that made sense to you, or is that something you kind of stumbled on later? Could you describe maybe a little bit more of that process and how you came to that solution?

Okay.

It was a research which came off an existing paper which an MSG gun was used and we decided to make a few modifications on that to be able to take in all the possible bands which we have and generate them. The initial paper just had generating images without the labels. Right. So we were able to modify it and also generate including with the labels as well. So that's basically the setup which we used.

I'm curious, as you're talking about GANs and the use of that and trying to augment data and stuff, have you kind of as a little bit of a random question thrown in, any thoughts on the use of simulation? We're seeing a lot of simulation starting to be used with that going forward in the space. And have you all gotten to that area or have you put any thought into what you might do in terms of data augmentation with simulation and GANs and such, or not something.

And again, just to say that we have a website, if you're interested to see some of those synthetic imagery, you can go to is this placereal.com we have a game there. You can see how good you can detect real from synthetic satellite imagery or fake imagery. We got kind of interested in this, similar to the website that there's in the GAN word for this persondoesonexist.com but anyway, back to your question about simulation. So that is true. That is a growing kind of application in the generally Earth science geospatial sector. We haven't had radiant touched on that space yet. We are not doing any simulation ourselves. But particularly for those who are working to kind of embed AI ML modeling into the general Climate modeling in terms of projecting what will with the warming world within 10 years, 20 years, 100 year windows. They are basically relying on many of the simulations to fit it into the machine learning model part and train those models. That is a very growing field. There's a lot of research, a lot of kind of interdisciplinary, actually departments being established at universities to just work on that type of problem, how we can learn from the physical and the simulation world, to teach them machine learning models to simulate DAs and be more scalable and hopefully more interpretable in the world. So it's a growing field, but we at RADIAN haven't done anything in there yet.

I'm interested. As I was sort of exploring the crop spotting or classification use case that you were highlighting, I noticed that there's a leaderboard on the Zendi site related to a spot the crop challenge. Could you talk a little bit about that and how you've decided to kind of utilize some of this competition leaderboard type of approach to look at some of these problems?

Yeah, so this is actually one of those use cases that we work with a local partner. So in this case it was the Western Cape Department of Agriculture in South Africa. So they do this agricultural census every decade or so because as I mentioned, it is very extensive and expensive process. And they had done the recent one in 2017, 2018. So they had a high quality map of their state in terms of what crops are grown, specific field boundaries of each, basically farmland, the crop type and other kind of metadata like irrigation type and so on. And they were interested to see can this process be automated or at least semi automized using satellite images so they can get an updated map every year. And what is the art of possible with that? So what we work with them was receiving that data as a partner and then curating a high quality and diverse training data out of that. So we match those kind of labels that they have collected underground. Those are practically what we call labels. You're underground, you collect a crop type and because we match this with satellite imagery, that becomes the label and then we basically match with corresponding time series. As ABBA mentioned, in this type of problem, we use usually time series of imagery. It's not just one image and you classify a label for it. Because crops have a seasonality, they have a phenology and you want to find that signature and then the model will be better able to decide if this is a wheat or a maize or a sorghum or so on. So we basically curated that data set and then to kind of Crowdsource models. We run a competition. So getting some support from the Giz Fare Forward program, we ran this competition on Zindi platform and exposed the problem to a pool of talents that Xindi has to see who can build the best model for this crop type classification problem. And similar to any other competition, we had a training set and a test set. And the test set basically predictions were hidden. We used that for scoring and kind of defining that leaderboard that you saw. And people basically build their models. The incentive for them is getting exposed to a new problem. It's also a capacity development effort for us because many of the people in the AI community across Africa are eager for new problems. And I think geospatial and satellite imagery is one of those domains. So it's also a capacity development effort for us. While it is a real world problem solving, it is a problem that a government agency is interested in and there is a good potential for it. So the winners basically are those three. And the leaderboard that you see, they have built the best models in terms of accuracy score of detecting crop types in Western Cape, South Africa. And the models are all open source. We haven't put it on GitHub yet, but soon they will be. Yeah. So that is the scope of that competition.

I'm wondering maybe Joyce, as a member of the sort of wider research community in Africa, if you could give your perspective on how a research group like yours might think about using some of these tools that Radiant Earth is creating and what it might enable for you. And then maybe then if you have follow up questions for, for Radiant Earth in those regards, feel free to take us wherever.

Yeah, so I think this is interesting. I think the question, the example that I gave are about trying to understand deforestation is one of the practical problems that we are working on in the lab. And it was interesting to hear Hapmed's thoughts on that. But also what is important is from the lab, we've been doing a lot of work around collecting data around about the crops, some sort of crop mapping. And that's always the problem, trying to understand the ground truth and collect as much ground truth data as possible. Because if you want to use satellite imagery data, then the ground truth can act as a reference. And so I feel like the problem also that we can solve is now that we have the ground truth, can we be able to map that to the satellite imagery data and be able to build models that can easily be used for crop type mapping around different farms in the country? So there are several potential areas that really, we can benefit from what Radiant Earth is providing and the kind of data that they're providing. But most especially since the Makere AI lab is located within a university, it really provides an opportunity for capacity building. Right. Because that's how the lab grows. We have a lot of students who come in, do internship and then get introduced to several concepts in machine learning and AI and ML for Earth Observation is one of those concepts that we are starting to work on. And I know that earlier this year we had the ML Bootcamp that we organized together with Radiant US and FairForward as well. And so I think that's what I want to hear from Ahmed. I just saw recently that this had even moved into, you know, Atsinji. So I think it's important for us to know if I were a student out there and I want to learn more. I understand there's Radiant Earth, yes, there are tutorials, but I want to get a whole idea of, you know, what's Earth observation, How do I get the data, but not just the data, how do I actually start to build my own model, you know, for example, for a prediction of deforestation, how do I get started? And I know that Radiant Earth has been able to provide that opportunity. So, Ahmed, if you could speak more about this capacity development, that would be really great.

Sure thing. When you think about the whole ecosystem of Sustainable Development goals, all the data being available, and as I mentioned, working with local partners, one of the missing pieces that we found is we need to train more individuals to be able to solve these problems because it is a new field, it is a growing field, it is an impactful field, but we need people to get trained how to use the data and generally starting with what is satellite imagery, as Joyce mentioned. So for that reason, one of the pillars of our work is really training and capacity development. So earlier this year, back in May of 2021, actually, so we joined efforts with Joyce's team at Make Career University and got support from the Giz Fare Forward program to run a training bootcamp focused on machine learning for Earth observation. And the target for us was there's a growing community of AI practitioners across Africa. I mean, we at RADIAN have worked with Data Science Africa, with INDABA teams, with Indaba X teams across different countries. And they are all AI people, but they are not exposed to the remote sensing world. So the training was kind of outlined in a sense that let's start exposing them to what is satellite imagery, how you deal with these type of data, how do you access them? I mentioned APIs and the repositories. But practically, how do you write a Python code to get that type of imagery? And then how do you curate a training data set when you have a reference data on the ground? So the lectures were kind of designed in that sense and we had around 40 participants in that course. And then in the second week of that we were working on practical use cases. How do you build a crop type classification model? How do you build a model, for example, wind estimation from a cyclone, using again, satellite imagery that we get regularly. And particularly we were asking participants to also work on some of those exercises between lectures. So it was hands on and practical. It wasn't just lectures. And I think it was a very successful training based on the feedback and the kind of survey we collected from the participants afterwards. And then we packaged that training program into an online course which is now available on the AT&G platform, which is established by Giz. So anybody can go through that course now on their own pace and basically start learning about Earth observations. The topics that I mentioned out to you deal with machine learning in that sector and then building your own model. If you finish the course on a tinge, you can also get a certificate. And there's also a user community on our end. So we have an open Slack workspace, the link is in that course and it's basically Radian ML Hub Slack workspace that anybody can join, ask questions, connect with other peers, share their experience or problem, or look for collaborators and others. So those who participate in that course can also connect to others and basically get feedback from others.

So as y' all have been talking about this for the last few minutes, it's a cool process and I find my mind kind of wandering back to earlier in the conversation and you've hit a couple of different use cases, but I'm starting to wonder how this could be applied to so many areas. Now that I understand how you're approaching and the tools that you're developing. And so if you go back to kind of that notion of sustainable development goals, the SDGs and those kind of big problem areas that you're addressing, some of those, I'm rather curious now, now that I understand which of those types of things you're hitting or planning to hit in the future and which might be aspirational. So even if it's something that you have an interest in, your brain has been chugging on it for a while and you know that might not be something that you're going to be able to address right away. I'd Love to understand kind of what you're thinking about, what's possible here, what types of things, what types of problems are achievable, maybe in the short, medium and long term. Long term, maybe just being aspirational.

Yeah, I'll share my feedback. I will feel free to jump in afterwards. So think of geospatial as a horizontal sector. So geospatial can feed into many vertical domains from agriculture, food security, land cover, surface water monitoring, drought monitoring, deforestation, ocean monitoring and sea life. There's many aspects that geospatial as a horizontal sector can feed into. That's how we have established MLHub. So it is agnostic to the application, but as a radiant team, we have limited capacity. So we work ourselves on specific problems which are particularly in the agriculture and food security sector. That has been really the prime kind of application area for us because of its impact across the development sector. It employs a significant portion of the labor across these developing regions and it's a significant portion of the gdp. And then. And food security and human life is definitely another angle of that. So for those reasons we have been kind of doubling down our efforts into how we can better solve stakeholders on the ground to solve the problems related to that. The ambition and the end goal. If I want to think, okay, what is the ideal world, the utopia that we are thinking about is the kind of stakeholders, particularly in this case governments and to some extent commercials or providing services, be able to say, oh, I'm in this region, I want to know what the farmers are doing, I want to provide the right recommendation to them in terms of fertilizer application, the best crop type to grow, how to basically plant that, how much water you need, and maximize their production toward the end of the season. That addresses their economic kind of well being, that addresses their food security and the human well being because they have more nutrition and food to feed the society. And then we are utilizing our natural resources the best way. Because that's another angle of this thing. If we are kind of over planting in regions that are not suitable for that specific crop, we are killing the nutrition of the soil. And then down the road we are not going to have a sustainable agriculture. Really the end goal is supporting the governments and stakeholders to be able to do that. It needs a holistic approach. It is not just us doing that, but what we are trying to do is showing the art of possible providing those benchmarks and access points and providing the know how and the skill sets to as many people as we can train and then asking them to Train others, so be sustainable in the training ecosystem to be able to solve those type of problems.

I would be curious just to sort of follow up on that. That was a very interesting kind of take on where Geospatial sits sort of in the stack horizontally and what it can impact. I was wondering just for our listeners out there, who with it being practical AI are very or many of them are hopefully thinking about practical things, I was wondering if you could describe a little bit in more detail. If people are excited by this podcast and they want to dig a little bit into the Radiant ML hub, could you just describe a little bit like what the API looks like for Radiant, the Radiant ML hub, and the sorts of functions and things that you can do with the hub. And I see some sort of model extensions and that sort of thing.

The Radiant ML hub, just like Hamid mentioned. So now there's a model registry on it and it has. It contains just brief tutorials on how to use the models. And also it has the data sets.

Right.

So what you could browse for different applications. So crops, wildfire, land cover, just different applications which you might want to use. Then you get to see all the data sets. Now the API calls made, it's a bit direct as well, because it was made to be simple in Python. You just from Gradient MLhub imports data set and then you specify the data set which you want for each of the data sets is a bit direct as well. How to do that? You just proceed from there to build whatever model you want to build or analyze data. So it's a bit straightforward. It was made to be very simple for anyone to use. Everyone can view that as well on MLHub Earth. So you could register and download whatever data sets there and work with it as well.

I'm wondering, as we sort of come to near to a close here, we've heard some, of course, very exciting things about Radiant Earth and the ML Hub. I wonder, Joyce, if you had a final closing question for the team as you're thinking about Radiant Earth, how it's impacting AI in Africa and going into this new year of work, do you want to maybe close us out with that?

So I think also looking back at the training that we had, I think that's a very important thing for people to reflect about, that the resources are available. And also note that it's only for people in academia, but also people in industry, in policy. I remember during that training we had people who had private startups that were looking around Earth observation, who also benefited from the training that's available. So when I think about where AI is going in Africa, the things that we think about, one is that there's capacity and especially in a growing field like ML, for Earth observation. So how do we train people? How do we build capacity? But then another thing that I also think about is the data. And through the radiant ML hub, the data, because Hamid has explained that they provide ML or AI ready data sets that people can be able to use use for various solutions of various problems that they have to encounter using Earth observation data. So I feel like that helps to close this gap that we've been struggling with. Of, okay, there's no capacity or I'm not knowledgeable in that area. Okay, if I get that knowledge, then how do I gain access to the data? So the data is also provided and there are tutorials and it's a bit easy for people to follow through and practically build their email models. So I think I kind of feel like as a wrap up, this is something that's important for us to remember, but maybe helpmed, there's something that I missed out that you think could be important for us, for the AI community in Africa as we move into the New Year. It would be exciting to hear your thoughts on this. Yeah, thanks.

Yeah, thank you, Joyce. I mean, you hit it pretty well. This is about the community and the capacity, right? And we are acting as what we call a collaborative agency, providing those resources and supporting the community to be helpful to their end users, which are decision makers, farmers on the ground, some people who are working in a sustainable kind of urban development. For us, the success of us is basically the success of those end users who are building those applications. So if they're more efficient, more productive in deploying solutions into their community, we are successful because we have been able to empower them. That's really how we look at this ecosystem. And we look forward to engaging with more partners, with more users. As ABBA mentioned, everything on RN is open access. So feel free to start using the data if you have data you want to contribute. Because MLHUB is an open repository, so you can access the data openly, but you can also contribute data. It is not just us publishing data in there. Many of the data sets are contributed by other providers and users. So if you have a benchmark data, you want to expose it to a broader community, please get in touch. You can publish your data on MLHub and we definitely are interested to expand the coverage of the data in terms of both geospatial coverage as well as the kind of application areas and we will have more training and capacity development in 2022 and years beyond. If you have specific needs you want to kind of get support from us, get in touch. The Slack workspace that I mentioned is definitely a good way to communicate with us. We also have support channel on our website you can see and we look forward to engaging more of those users. Yeah.

Well thank you all so much. Thank you Joyce for joining us again and for the Radiant Earth Team for taking time out of their amazing work to have this conversation. It really is wonderful and we'll include links in our show notes for those that want to jump off to the ML Hub to jump off to the open data sets and the competitions and the course. So please take a look start to get involved in the new year. Thank you to you all. Have a wonderful rest of your day.

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Sam.