Automated cartography using AI
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Welcome to another episode of Practical AI. This is Daniel Witenack. I'm a data scientist and founder at PredictionGuard. And I am really excited today because my life has been filled with large language models for the past months, and I feel inundated with information about those.

But there's so much going on, so many amazing things happening in the AI world outside of the text modality. And today we have with us Gabriel Ortiz, who is principal geospatial information officer at the government of Cantabria in Spain. Welcome, Gabriel. How's it going? Thank you. Thank you. Thanks for having me on and giving me the opportunity to share with you and the audience

what we have been after in the last few years regarding your spatial analysis and particularly artificial intelligence. Yeah, and one of the things that stood out when we started talking, well, first of all, you're a listener of the show, so I love it that you now get to be a guest on the show. That's so wonderful. I'm glad that we have listeners who are doing amazing things as practitioners.

But also, you're in Spain, which is one of my favorite places. I did my collaborators during my grad school days were in San Sebastian, and I spent time there. And I know there's so much innovation happening in that region and in Spain in particular. What's it like to be working in AI in Spain? Well, Spain is, I think, a great country to find professionals in all the branches of engineering and engineering.

there are many things happening in the AI industry. There is a lot of good, you know, environment of startups growing. And I really encourage you to,

engage and contract people from Spain. Yeah, that's so great. And not only is there amazing work going on there, but it's one of the most beautiful places I've been. And even when you logged in, so our listeners can't see it, but I see the beautiful sunshine and trees and town behind you through your window. So I'm a little bit jealous. You mentioned San Sebastian. I am pretty close to San Sebastian.

Santander is a really, really beautiful city. Yeah, yeah. So we mentioned that you work in geospatial. I know, so I've been on the Mapscaping podcast a few times, which has been fun. And I know that that industry is...

Yeah.

You came more from the geospatial side. So could you tell us a little bit about how, as a geospatial practitioner, you first started kind of dipping your toes into deep learning and understanding what it meant for your industry? Sure.

I have been working in the special industry for more than 30 years. I started working for topographic control, bathymetric control of works. Then I moved on to engineering companies designing highways and railroads dealing with environmental data.

Always using GIS, which stands for Geographical Information System, and as many of you know, it's a technology that lets you operate and do analysis over a huge amount of data.

Then I started to work for the government of Cantabria. I am now in the role of principal geospatial officer, as you mentioned. But literally, if you translate directly from Spanish, it would be something like chief of the service of cartography and GIS.

My role here is not only being in charge of the data production, but also in the development of the infrastructure for the analysis of your spatial data within our organization. It means for our staff, but also for our stakeholders outside, which is something very important for us, for the citizenship, for the community and for the companies that are working with your spatial data.

Me and my team, we have something very ingrained in our DNA, which is the public service that we provide using AI and using another set of technologies. And we every day try to do our best to fulfill with that target. Yeah, it's really, really inspiring to hear kind of the motivations behind how you think about doing your work and the people you're serving, which is so great. I

I'm wondering just practically, you mentioned kind of GIS tooling and the processing of data in that space. Of course, deep learning and the

the AI space has its own sort of unique tooling and sometimes weird tooling. So I'm wondering, could you comment in terms of what is it like for a geospatial practitioner to start adopting deep learning techniques and all of that, which I'm assuming have a different set of tools than geospatial people have used in the past. So what is the current state of

the tooling around mixing deep learning with geospatial. Is it difficult? Is it fairly segmented or is it more integrated at this point? - Well, at first sight, it seems daunting and intimidating, but I have to say that it is not so difficult, right? Just to demystify it a little bit, the AI technology. As you mentioned before, I am not a researcher on AI. I am an expert in geospatial industry.

And I will tell you my story, how I began. My first contact, or at least the first time that I paid attention to AI was in 2012 with AlexNet, what happened in the ImageNet challenge. At that point in time, classification of images was great, but it was not very applicable to the spatial industry. It has an application and you can leverage that, but it is not what we do every day.

Previous to that, I have to say that it was in 2010 or 2011, something like that, I knew about the work of NVIDIA with GPUs, the general purpose GPUs. I think Bill Daly talked about this in one of your early episodes.

And that was very interesting for me because in the geospatial industry, we often have a lot of demanding in terms of computing power. When we operate with what we call raster data, which is no more than data organized in a grid.

topologically in a grid, for instance, an image is raster data, but also, for example, a digital terrain model, which is a grid where you store it in every pixel in the center of every cell, the value of the altitude of the terrain over the main sea level, for example.

and you perform calculations over that data model. For instance, getting the watershed or the viewshed of one part of the territory, and that calculation can span for several days or even weeks because in spite that the mathematics underlying running under the hood are not very complex, what happens is that you have so many pixels that it ends up being very demanding.

And what NVIDIA started to do on those days was to be able to parallelize a lot of calculations. And instead of using four computational threads on your GPU or eight computational threads, they were able to spread all the calculations among hundreds or even thousands of computational threads. That caught my eye because it was very important for me. But at that point in time, I thought,

You are going to need, Gabriel, you are going to need a GPU, but not for artificial intelligence. I was not thinking about that, but for calculations of a different nature. Then in 2015, 2016, we witnessed the blossom of a whole new generation of deep

model architectures. Just to mention some of those who had a big impact in computer vision. ResNet in 2015, I think it was presented in 2016, I'm not very sure. Then UNET that has been extensively used. In 2017, the Facebook Artificial Intelligence Research Group presented and proposed MaskR-CNN. You know, it was an evolution of FastR-CNN.

And in 2018, I saw for the first time a demo within the geospatial realm

of our data provider, which is Esri, actually. I think you also have a couple of guys from Esri in a previous episode. And what they were demonstrating is how you can detect swimming pools and oil rigs automatically using a single-shot detector in those days. And that was kind of an aha moment for me because I realized, well, you have to invest your time. This is going to be definitely a game changer.

and you have to start working on this. So that was the moment. And from that point, you know, there are two kinds of persons, right?

I will use a metaphor to explain that. When you see the results of AI, some people think it's magic and everybody likes magicians. Some people end up falling in love with the magician. They are obsessed with the persona and the mystery and the whole shtick. But some other people just want how the trick is done. And I think I belong to the second group. So

It was not only that this looks like magic, the point was how this is done. From that point I started to work, we can delve into this if you want.

But it's not so difficult, as I said before. Yeah, that's so great. Yeah, I think I applaud you for digging in and, you know, not too early where it was only a research topic, but as it started getting into practical applications, you really took that and figured out how to apply it within your context appropriately, which I think is wonderful.

Maybe not everybody takes that approach, so I appreciate that. So with the tooling that you're using, I think maybe this is useful for people that haven't done geospatial as much. So I know there's major tools like ArcGIS and other ones. And then you've got sort of like...

Jupyter notebooks where you train models or GPU services where you can run inference and other things. Have those merged at all? So from within the tooling that you're using as a GIS professional, has some of the deep learning tooling been integrated into those tools? Or is it mostly at this point for you, I'm going to export my data from the geospatial side

and then use a notebook and then import it back in or something like that? Well, that's a smart question. As I said before, our software provider is Esri. We worked with Esri for a number of years, and they are doing an excellent job in integrating many open source frameworks into their platform.

And I think because we try to follow the literature, but we are constantly falling behind. You know, it's extremely difficult every week. Me too. It's impossible. And even, you know, constructing and completing the puzzle and the issue of installing all the frameworks and putting everything into work is

can be very complex. So we have a big advantage working with the Esri technology. They have a team research and development team based in India and I think

These people are doing a great job facilitating the application of that. In some of your previous episodes, you have been talking about UX interfaces for using artificial intelligence, whether or not it makes a difference. And it really makes, because it's a way of democratizing and making accessible the technology. That is one part of the story. I think it has...

facilitated a lot our work because you not only need the frameworks, you need all the platform to move across terabytes of data. Your spatial industry is highly demanding in terms of the data that you have to work. And it's not only the frameworks of open source, it's how you prepare the labeling, how you structure the databases, how there is a lot of more science.

And also, apart from that, what I did is starting to gain the main concepts related with artificial intelligence from all the great resources that are completely free on the internet. You know, on YouTube you have lessons from MIT, from Stanford, that can introduce you to the simplest concepts such as a perceptron or a backpropagation or a stochastic ready descent.

So I designed it for myself a twofold strategy. First, training to gain experience with getting hands-on on off-the-shelf models, but at the same time, training to also learn about the concepts underlying pinpointing the AI world. I think that's important.

Many people think that artificial intelligence is a black box. It's not a black box. It's mathematics in action. Of course, it's not linear. You cannot fully predict what is going on, but many of the things can be understood. Well, I love your perspective, Gabriel, on how you've developed a mental model of how these technologies work. I think that's an encouragement to others to both...

explore these technologies, but also keep in mind what they are and how they should interact with them as tools. But I'm so fascinated by some of the projects that you've been able to accomplish during your time using this technology. And I want to start diving into those a little bit. One of the ones that you pointed me to that was really fascinating reminded me of standing on the beach and

I know.

Yeah, definitely. I started working with deep learning at the end. I think it was the end of 2019 or something like that. Then came the pandemic.

And after the pandemic, you know, with the release of restrictions, somebody here at Government of Cantabria said, hey, we are a little bit worried about the possibility of having uncontrolled crowds on the beaches, because I have to say that Cantabria is a notable tourist destination. We have more than 100 beaches.

So you can have a big problem in terms of the spreading of the COVID-19. And they were worried. The first thing that they asked to me is how can we get a calculation of how many people we have in every beach and when the tide is up and when the tide is down and things like that.

but it was just a simple calculation in terms of the surface or the area that the beach has. And I said, I think I can go further and I will count the people. And they said, what? Are you crazy? Yeah, I'm not drunk. I think I can do it because I had some experience using single shot detectors and

at that point in time, more models than single shot detectors. And it's what we did. We studied content, the people, because normally we have an archive of aerial surveys conducted always as is normal in clear skies, sunny days when everybody's on the beach in summer. So we had very well the...

behavior of use of every spatial behavior of use of every beach all across Cantabria at different days, different months, different no matter if it is a weekend or for labor day, we had a huge amount of data to analyze and we developed some deep learning models that even if you are changing the input signal, that means changing the

the aerial survey, it works. We could predict the sectors of every beach, not only in terms of absolute figures of population in a beach, but which are the sectors where the people try to concentrate

And after that, we released a small application that you can see in the notes of the podcast when you can see some maps. And just for curious interest, if you want to go to a beach and stay quiet and loosey-goosey, you know, without many disturbances, you can see what places are the most suitable for that use.

So it was a great experience, our first experience releasing something. Yeah, that's so fascinating and it makes so much sense after you say it. I know here I can think of so many more applications for something like this. I know like in the US national parks, you know, thinking about crowding and the impact on the natural environment or other things like that and helping plan out for crowds at certain points of the year. There's

There's so much practical use of this. And this was amazing because, yeah, you took this knowledge that you had been building up and really applied it in the moment during COVID-19 when there was this specific need. But then it sounds like there's

continued usage past that because even if i'm just a consumer like i'm a normal citizen and i want to enjoy the beaches this information is really useful to me i i know myself i probably would go to the quiet places of the beach and yeah and sit and listen to the to the waves so that's um there are much more interesting problems to try to solve than uh

the one that I described now. Later we started to work trying to model certain aspects of how the territory works.

You have to understand the territory as a whole, as a living entity, where everything is related to everything. So we started to slice every variable and try to address those variables with the help of AI. For example, we have developed some interesting models. We can delve into the architectures you want used today.

later on or whatever you have interested in but some interesting models for detecting and classifying vegetation

also for the evolution of urban growth, also for things like, where, like tracking cars, for example, that is like a kind of proxy of the society, how the society moves. And because everything is on our aerial surveys, you only have to have the skills to bring back that information and convert it in something useful. And we have been, as the years went by, we have been able to produce some projects

more relevant results. I will not talk about deep learning models, but about solutions for tracking the territory. Yeah, and you've mentioned aerial surveys a couple times. It may be useful for those in our audience who don't work in geospatial. They might have in their mind

And things like Google Maps where, oh, I could go and I could look at a satellite image, but it's not current, right? It's maybe one photo that was taken some while back. And you've talked about aerial surveys where you can actually learn, you know, both current information about what's going on in an area, but also historical information.

Could you just help our audience understand, like, as a professional, what sort of data do you have access to and how is that gathered practically and made available to you? Well, I have to say that everything that I have been talking about can be also executed with satellite images, you know. Uh-huh.

There are some differences, but of course you can do it with satellite images. The reason that we work more with aerial surveys is because we are more focused on capturing this kind of images

information rather than working with satellite data. My region, Cantabria, is not very big and we have in Spain a national plan that covers every three years all the country with aerial surveys and also we have a repository of satellite images

So anyway, you can do both of the input signals. The results will differ slightly. But apart from image capture with sensor, no matter if it is airborne or satellite sensor, we also work with a range of technologies. For example, LiDAR data. I know that many of the audience have been working with LiDAR data. LiDAR can be also airborne.

In fact, it was the origin of the technology later using from a plane, you know. And it has been increasingly important in our domain. We also work with, you know, system of records with traditional databases and a number of things. If I had to say something about my job is that

that is extremely interesting because one day we are working with COVID data, for example, another day you are working with energy data, another work with environmental data. The government of Cantabria has powers and duties in many domains. It's kind of one of your states if you, for

forget the difference of area cover only Texas or Florida I think Spain is in the middle between the area of Texas and Florida is something in between but the whole country and my region is quite small but it's a very interesting place to work with because that reason and the data comes from many different technologies and many different databases music

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So, Gabriel, we talked a bit about this kind of first project related to population and crowding on beaches, but you've done so much more. Could you highlight a few of these things in terms of other things you've been able to identify or track with deep learning from these aerial surveys? Yeah. We have an extensive work in the detection of vegetation.

I have to say that we have been also only using supervised learning, that branch of the deep learning, and specifically working with different model architectures such as I mentioned before UNET, you know, MASCAR, CNN, and some others. Now we are testing now some segment anything model, but we haven't done anything with zero-shot learning for production.

What I am going to tell has been achieved using model architectures that have been almost forgotten for the community. You know, everybody is focused on the SOTA architectures and there are so much that can be extracted from the old school of artificial intelligence. Quote unquote, it's not so old, right? Yeah, yeah. And I think actually this is maybe a misconception of

people that occasionally we try to mention this on the show, the majority of applications across enterprise, not just in GIS, but in manufacturing or marketing even, people think of marketing with generative AI, but the majority of applications are still traditional applications

quote, traditional machine learning. You know, there's a lot of scikit-learn models out there still, or there's just supervised learning, you know, models out there. And yeah, it's awesome to see, to highlight that here, because I think it is a misconception. Yeah, because, you know, when a paper appears, normally they do not run out the possibilities of the model.

The professionals who are not very specialist in the AI domain but have a lot of knowledge in a specific domain out of AI, in my case, we can prepare and curate better labels. We can understand the process that we are trying to model.

And we have so much to give and to propose to the community. And that's one of the reasons that some people have said that your models are quite good. How have you done it? It's...

brand new architecture is something that you have created on your own and always say, no, it is not. It's just using in a smart way model architectures proposed back in 2015, 2016, but with a lot of data, very well created. I also have to say that the computer power that we have at our disposal is quite modest. We don't have

From that point, something big or very extensive. And the key is how you create the data. Yeah. And one of the things that you had mentioned prior to recording was this idea of automated cartography as kind of an integration of a bunch of these different models that you've been working on. I'm wondering if you could...

kind of first describe what do you mean by automated cartography and maybe even for people that aren't familiar, what is cartography? And, you know, I'm assuming modern cartography isn't like

you know, Magellan getting on his paper and drawing, you know, maps on parchment paper or something. But what does cartography look like these days? And then what do you mean by sort of automated cartography with these sorts of models? Well, cartography is the art and the science of, you know, trying to model the reality and abstract the reality and plot it in a flat surface.

is a science that has been developing for a number of centuries, from many centuries. Up until now it was highly dependent on the human ability to trace and to draw everything on the surface of the Earth.

As the technology has been developed from the 90s, we started to move very rapidly into digital technologies. And the automation of the cartography has taken place not only with the advent of AI, but several decades before. However, this is a revolution because we have never been able to produce such a degree of quality using so few people working.

There are some similar technologies like remote sensing, which is the part of the technology in charge of analyzing from imagery of satellites and producing cartography also. It recalls many things of the artificial intelligence, but it can match the results in many other fields.

So the revolution continues. It started, as I said before, in the 80s, 90s, but now it's a complete revolution. And I think that

For the first time, we are able, we have an example that you can check it out in the description of the podcast, but where we have been able to produce a map with basic coverage where you have trees, where you have shrub, where you have no vegetation, where you have buildings or roads or railroads completely generated by AI. Of course, it has some mistakes, but we left

on purpose, those mistakes on purpose, because we wanted that the rest of the community could evaluate the capacity and the ability of the models to work alone. This is a question that just popped into my mind as you were talking about these models, what's possible. And, you know, it's not perfect, right? No AI system is perfect. So there's going to be mistakes.

I'm wondering, as someone who's been in GIS and been a practitioner for, I think you said, 30 years now, I also imagine that human-based processes are error-prone, or at least they're slow, right? So by the time a human maybe processes a certain map or something, things have been updated, right? And it's maybe not current anymore. What do you think about the... What are the implications...

for maybe cartography or GIS as we move to the future where AI systems maybe can do things more up-to-date, but with some mistakes, but they're up-to-date and can really maybe highlight certain areas that are incomplete or something, combined with human efforts to correct those mistakes and keep the... What do you see as this balance between

trying to be automated with AI-based techniques

and the role that human cartographers or GIS professionals play as these systems expand to more and more places? - Yes, it's a very interesting question because one of the big problems that we have is to maintain up to date every single database that we release into the market or for our stakeholders. That's a very big problem because it's always difficult

and one of the main advantages of artificial intelligence is that you can have a model and you know. It will not probably work perfectly with the next area survey because it will have some differences in terms of colors or shadows or whatever, but you can fine-tune, you can, or maybe you can train the model from scratch again

start from scratch with the training and you can update something in a reasonable time frame. So that is one of the things that I'm most attracted by, the capacity of updating things.

And it's a game changer, as I said before. No other artificial intelligence offers things that other technologies really don't. Yeah, and of course there's limitations. You know, AI is never... The expectation should never be that it solves all of our issues, but it also should be that it's going to solve some of our issues or solve some of our problems, but not all of them. From your perspective...

How do you think about the current limitations of AI within GIS and cartography? What are some of the things on your mind with respect to that? Yeah, I think, of course, you have to bear in mind that we have limitations. What happens to me also happens in teams in India or in the US that I am always seeing what they are doing. I would like to point out two limitations. One is the computing power and technology.

Another thing is the limitations of CNS, which is the technology that we are using right now, convolutional neural networks. We can talk a little bit about model architectures and things. In terms of computing power, I think it's worth delving into the role of GPUs because in the geospatial realm, it's not well understood why do we need a GPU.

I don't know if it happens in other markets, but in our industry, when you talk to somebody about GPU, normally my fellows and mates, I don't know, they try to say, it's something related with the IT department. I don't want to be in charge of that, but it's not at all. You have to be aware of what technologies do you have for calculation. The hardware is so important.

And you have to speak the same language as a data scientist that the rest of the community speaks. And that is very, very important to understand that it's not the same GPU in your laptop that DGX talking about or HC100 if we are talking about NVIDIA hardware. It's not the same.

It's everything related with the amount of data that you want to put into the train. You know, the quality of your training, the level of the convergence that you are going to get if you are going to stay in a local minimum in the convergence or you are going to reach and assess the possibility of the label that you are ingesting into the model. Everything is related with the hardware. I think that Bill Daly and Anima and Kumar in many of their talks

I always talk about the trinity of AI. One of them is the data. Another is the software, the algorithms that many of them have been with us for a while. Propagation, many of those algorithms have been from the 80s, if not before. But the hardware is the third part.

Bill Daly always says that it's the spike that starts this engine of creativity of AI. And I think it's true. You have to pay a lot of attention to computer power. And there is another limitation that is ingrained in the DNA of the CNNs. As far as I know from my experience,

You cannot expect to perform exactly as a human being and sometimes, in spite that you create very well your labels and your tips and your data, the model do not learn as good as you expect. But somewhere in between, you can have a reasonable amount of success in that.

What we do to overcome is combining different model architectures. It's something very useful and widespread in the geospatial industry. For instance, we combine models at two different levels. From the architectural levels, it's quite common to see the

the combination of ResNet with, for example, UNET. In ResNet, you remove the last part, the fully connected layers, and connect the remaining part with UNET. So you are using ResNet as a feature restriction, for feature restriction, and then the rest of the decoder happens during the rest of the UNET architecture.

It also happens with the mask R-CNN. We use constantly ResNet as a backbone.

but then the rest of the model goes. And there is a second point, which is combining the results or the inference. When you have inference from two different model architectures, for example, talking about vegetation, imagine that you have one model that detects very well the big areas of vegetation, but fails in the small spots. And you have another model that

works very well for a small spot but fails detecting the big areas because the big areas creates artificial holes and mistakes. You can combine the results of the outcomes of those model architectures with traditional GIS techniques to mesh all the results together and obtain

a bigger, best quality of the layer that you want to infer. That has worked for me and it's one of the ways that we are trying to overcome the

the limitations of artificial intelligence. That's great. Super practical. And I know that's what a lot of our listeners want to hear is some of the practical ways they can explore these technologies. Well, Gabriel, it's been an amazing pleasure to talk to you. As we close out here, there's a million things we could talk about. I know some we didn't get to and we'll link in the show notes. But as you look to the future, could you just briefly in the last...

minute or so, just briefly share with us what's exciting for you as a GIS professional looking to the future that either you want to dig into next or what are you encouraged by or optimistic about as you look to the future of your own work and how AI influences that? Well, I

- Well, I have to say that in my 30 years plus of working in the US spatial industry, these two last years, two or so, have been the most exciting part of my career because it's so creative. We are just scratching the surface of AI

Great things are coming. I think that with the advent of Zero Shot, we have been watching from the first week of April what can be done with the SAM segment anything model. And I'm sure that new versions will come of future versions of SAM.

when we combine that with LLMs, with large language models, and we can interact with the voice and say, hey, draw me all the trees in the image, or it will be much easier to use these set of technologies. Anyway, just to finish, I would like to send a message to the audience for those who are not artificial intelligence researchers like me, that

It's possible to apply this set of technologies even though you are not a specialist on that specific domain. It's also to get hands-on on one of the, take one of the self models and start playing around with them. And I know the future will be absolutely focused on artificial intelligence. There will be a different environment

geography in the next few decades. Awesome. Yeah, this is so inspiring. Thank you for your work, Gabriel. And it was awesome to have you on the show. Thank you so much. Thank you.

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