AI Maps - How is AI being used in Mapping in 2024?

Introduction

Though AI has only truly risen to prominence in the last couple of years, it has already played a crucial role in beginning to revolutionize many industries. Sectors which have remained unchanged for decades are now experiencing upheaval thanks to AI unlocking automations that were previously confined to the realm of science fiction. Meanwhile, new and emerging industries are working to incorporate AI from the ground level.

One industry that straddles both the old and the new is mapping. Maps have been around for thousands of years, but they’re still continuing to evolve at a rapid pace. In recent years, we’ve become accustomed to tools such as Google Maps which can update rapidly and be overlaid with real-time information such as traffic patterns. Digital internet-based maps enable us to quickly scan vast areas of the other side of the globe and zoom into incredible levels of detail, looking at road layouts, topographical information, and more. 

Indoor maps are another relatively recent innovation. While maps of indoor locations have been possible for as long as maps have existed, they’ve lagged behind traditional outdoor maps for a number of reasons; the challenges, cost and time involved in producing accurate indoor maps were often outweighed by how often indoor locations such as retail stores and airports changed layouts, rendering the maps out of date and obsolete. With AI able to better adapt maps without having to start them from scratch again, as well as interact with other systems to anticipate and understand layout changes, these barriers are now being surmounted.

This is just one example of how AI is impacting modern mapping practices. In this post, we’ll explain some of the most prominent uses of AI in mapping at the moment, as well as looking at how things may develop in the future.

But just before we dive in - why not try a real-life demo that illustrates exactly how mapping is being revolutionized by AI? Our revolutionary MapScale® tool is now available with an online demo, enabling you to transform a flat, 2D architectural file into an interactive 3D map. Click the button to try for yourself!

• A quick note - what sort of AI maps are we talking about?

• Google Maps - using AI to deliver a new Immersive View mapping experience

• Helping to prioritize humanitarian efforts

• Helping to solve the riddle of indoor maps

• Disaster relief - predicting wildfires before they happen & mapping historic flood levels

• Ecological maps for protecting the future of the planet

• Improving supply chains and logistics

• Fighting illegal fishing activity in the Earth's oceans

• Charting icebergs

• AI boldly going where no maps have gone before - space

• The future of AI Mapping

A quick note - what sort of AI maps are we talking about?

AI has quickly become associated with a variety of different maps. One is mind mapping, helping people to turn thoughts, ideas and questions into diagrams, flow charts and other visualizations to help them keep organized. Another is data mapping - harnessing the power of AI to interpret vast amounts of data quickly to help organize it (think of a spreadsheet with thousands of products on it, then using AI to categorize each product into a set number of types). 

What we're talking about is classic geospatial maps - literally, the maps you can use either indoors or outdoors to help you navigate from point A to point B.

Without further ado, here's some of the leading AI mapping use cases:

Google Maps - using AI to deliver a new Immersive View mapping experience

Google Maps has been without question one of the greatest achievements in mapping history. In just a few years, Google have delivered maps of incredible quality to users across the world, and then built numerous services upon them, including navigation, user reviews and perhaps most impressively, Street View, which has necessitated a fleet of specialized vehicles driving around the globe repeatedly, capturing photographs as they go to build up a visual, street-level map of a huge number of locations. 

But even through Google Maps' main features were developed before AI truly came to the fore in the past couple of years, Google are managing to find new and exciting ways to use AI in Google Maps to deliver exciting new features for users. One such example is their new Immersive View program, which is designed to take their classic Street View photography project to the next level.

Traditionally, Google Maps has offered both classic satellite imagery (taken from a birds eye view) and images taken from specialized cameras mounted on top of vehicles, which offer what Google calls a Street View of areas. What Immersive View seeks to do is bridge the gap between these two image datasets and provide the best of both. AI stitches aerial satellite photography together with Street View images to enable users to truly immerse themselves in a location, all via the Google Maps interface. Users will be able to swoop inside restaurants, see predictions for traffic and weather visualized, get a more comprehensive idea of the size and scale of buildings, and get a true feel for a location before ever setting foot there.

Helping to prioritize humanitarian efforts

It's not just in its consumer-facing projects that Google are leveraging AI. Google's Open Buildings project has been integral to helping all sorts of charities and organizations to better understand the way the world's population is growing, moving, and evolving through analyzing buildings and structures.

This project is now being enhanced through AI models. By examining a set of satellite photographs taken over time, AI is now able to measure the changes in building density over time. AI is also able to combine datasets to better estimate features such as building height.

The upshot of this is we're now better able to understand population trends than ever before. This means that for charities focusing on delivering humanitarian projects, prioritization has never been more data-led; we are now better able to understand the areas with the highest population density that don't, for example, have access to electricity or clean water. This will enable organizations to help the most people possible per project, as well as better anticipate future population movements to ensure their projects are helping people for decades to come. 

Helping to solve the riddle of indoor maps

As we touched upon in our introduction, indoor maps have proved enormously tricky over the years to get right. While they have some advantages over outdoor maps - such as the ability to work certain measurements out using geometry - these tend to be outweighed by the sheer level of detail required to make an indoor map effective.

Imagine a normal office building. On a standard outdoor map, this building would be very easy to account for; simply measuring the exterior walls, as well as the building’s distance from landmarks and roads, would allow outdoor cartography software to incorporate the building quickly. What’s more, the shape and layout of the building’s exterior are unlikely to change significantly - once it’s added onto a map, it may remain for decades without ever needing an alteration.

Compare this relatively simple task to the legion of issues and challenges faced by someone trying to map the inside of the same building. Instead of a simple geometric shape dropped onto a map, an indoor map may have to incorporate everything from seating areas to desks to walls and doors. Many of these features - such as seats, desks, and cubicle arrangements - may move regularly, to the point where an indoor map is quickly out of date. This process will then need to be followed for every single floor of the building.

There’s also the challenge of what details ought to be included, which can be ignored, and how different details populate on different zoom levels of the map. For example, someone with a far zoomed version of the map may be looking for the quickest way to get from one end of the workplace to another, and thus won’t need much in the way of information about rooms and areas along the way. Someone zoomed in to an extremely low level may be looking for features and descriptions of an individual room, such as the facilities within it (like screens or projectors) or how accessible it is. 

The same is true for plenty of other building types. Retailers, for example, are constantly experimenting with changing store layouts, adding pop-up concessions and more. Manually tracking all of these changes and updating the store's map in a timely fashion is a hugely time-consuming task - and that's only for one store.

One Pointr client was, at one stage, having to conduct a cycle through thousands of different locations, manually charting and updating changes to the indoor maps for their retail stores one by one. This process took months and, due to the constantly shifting in-store layouts, meant that maps were quickly out of date, and remained that way until they revolved back to the front of the queue. It was this client who inspired us to create our revolutionary MapScale® tool.

Whereas most indoor maps require laborious manual work, often including on-site visits, MapScale® instead leverages the thousands of maps created over the years here at Pointr as a dataset to draw from. Using this data, it is able to transform static, 2D floorplans into interactive 3D maps, work out via the context of the building type what sort of rooms are present (for example, a room with chairs in a hospital is likely to be a waiting room; a similar room in a workplace is likely a meeting room), and make intelligent decisions about which data from the source file is aught to be retained and which is unlikely to be useful for users.

If all of this sounds impressive, why not take a look for yourself in our interactive demo?

Disaster relief - predicting wildfires before they happen & mapping historic flood levels

Wildfires are becoming a greater and greater problem for much of the world as global temperatures continue to rise, and enterprising scientists at the World Economic Forum and the Turkish Ministry for Agriculture and Forestry are using AI-powered maps to help them predict where ecological disaster could strike next.

Source: World Economic Forum

Using hundreds of different variables and AI trained upon using historical datasets, the FireAid system can help officials predict wildfires before they begin. So far, the system has achieved an 80% accuracy rating in anticipating fires 24 hours before an outbreak, a crucial window of time in which resources can be allocated and firefighters put on alert to help quell a fire before it burns beyond control.

Another side effect of climate change is the increased risk of flooding. Floods and high water levels can be inherently hard to predict, with rivers bursting their banks at different points depending on a huge range of factors that are constantly shifting over time. An example can be an area particularly prone to flooding erecting a highly effective floor defense system, which then protects that area, but creates new issues further down the same river for an area that previously hadn’t experienced flooding.

Aecom is attempting to counter this issue of unpredictability and poorly kept historical records of past floods by building a new AI-powered system that is capable of viewing photos of old floods, making an estimate of the water level depicted in the photo, figuring out where the photo was taken, and mapping it. Without AI, conducting this process on a single photo could take a human researcher multiple hours. However, Aecom’s tool is estimated to take just 15 seconds per photo. 

By building a more comprehensive database of historic flooding flashpoints, the hope is that government services will be able to take a more proactive and tactical approach to flood defenses.

Ecological maps for protecting the future of the planet

AI still has a big role to play in maps designed to tackle more long-term ecological problems. 

In order to help protect endangered species, the Spatial Planning for Area Conservation in Response to Climate Change has developed a system which leverages AI to allow conservationists to predict where different animals and fish are migrating. Migration patterns are shifting due to rising temperatures, and scientists are hoping an AI model of how migration patterns continue to develop will enable them to proactively plan conservation efforts in areas before the animals begin migrating through them.

Plant life is also critically important. Microsoft are supporting efforts to effectively monitor and map underwater plant species and organisms to ensure that the ocean ecosystem continues to function as intended. Normally, monitoring underwater plant life requires extensive and expensive manned diving expeditions, where photographs are taken. These photos are then painstakingly classified manually by a team of researchers, a process that can take hours and is open to mistakes.

However, through a combination of a smaller number of samples and high quality satellite photography, Microsoft’s Azure architecture is able to leverage AI to create a comprehensive underwater map by categorizing the sample photos and finding how they correspond to the satellite images. These maps will then help to identify shifts in patterns and pinpoint areas where conservation efforts need to be enhanced to stop destruction of ocean-dwelling organisms before the damage is irreparable. 

Deforestation also poses a major threat to the world's ecosystem, and due to size of many of rainforests and other hotspots for illegal activity, it's notoriously difficult to stop.

The ability of AI to spot minute changes between datasets and identify patterns can, once again, be enormously useful in this context. By comparing and contrasting satellite images taken of the same map area over time, AI can help alert authorities and help them stop illicit activity before it's too late. And it's not just spotting patches of trees or other greenery that have suddenly disappeared - in one example, AI has been able to spot the beginning of activities such as unsanctioned mining which can eventually lead to vast areas of forest being cleared. 

Improving supply chains and logistics

A natural area that AI has quickly impacted is logistics. Whereas before many companies relied upon either basic computer programs or human judgement to make decisions - such as the order in which a series of deliveries should be made - AI is able to take shifting data (such as traffic on a variety of routes) and make sophisticated adjustments based on the latest information. It can even factor in likely amounts of traffic on different routes based on previous days' data and make decisions designed to avoid needing to be in areas when they're at their busiest.

The same is true for supply chains. One of AI's core abilities currently lies in the fact that, via its ability to ingest huge amounts of data, it can offer sophisticated predictions for future trends. In times gone by, supply chain management was an artform that would often take a human years to master in a certain business. With AI able to parse huge amounts of data and look for patterns, it's becoming an invaluable tool for businesses that deal with thousands of deliveries a day and that need to anticipate sudden surges in demand for particular products, lest they run out of stock and miss valuable orders.

Fighting illegal fishing activity in the Earth's oceans

Fishing quotas are an important part of the fight to keep the delicate underwater ecosystems of the Earth's oceans balanced, preventing overfishing and irreparable harm to certain fish species. However, quotas and limits are only effective when they're adhered to, which makes undocumented and unsanctioned fishing activity a major threat to keeping the oceans healthy and productive.

 Source: Global Fishing Watch

The challenge is spotting vessels operating illegally, which is akin to finding a needle in a haystack when considering the vastness of even comparatively small areas of ocean, such as the British Channel and North Sea. This is where AI comes in.

Per Nature, the Global Fishing Watch initiative was able to use various AI models to comb through thousands of terabytes' worth of satellite imagery to identify thousands of images of boats in the world's waterways. Armed with this data, the AI was then able to create dynamic models of the busiest and most frequented routes boats were taking, and cross-reference these routes and areas with the expected volume of ocean traffic and help determine how much illegal activity was taking place. The net result? A finding that around 75% of all fishing vessels are undocumented.

This data can not only provide us with information on where illegal activity has taken place in the past, but can also be enhanced further by helping authorities predict where it's likely to take place in the future, and bolster their presence in these areas.

Charting icebergs

Monitoring the number, depth and expanse of icebergs around the world serves many purposes, from tracking the impact of global warming to alerting ships to dangerous ice in shipping lanes.

However, it's traditionally been a time consuming, difficult, error-prone process. Simply correctly matching two icebergs to one another in two separate photos is a process that can take a human hours, as their shape shifts and they converge and break off with other bergs. AI has proven to not only better humans it many respects when it comes to the accuracy it's able to achieve, but some researchers found that their AI model was able to complete its analysis 10,000 times faster than a human analyst.

AI boldly going where no maps have gone before - space

It’s not just on our own planet where AI is revolutionizing mapping processes. Maps of Earth are still complicated, time-consuming objects to create, having to take into account everything from topography to shifting and changing landmasses to conflicting data across multiple sources to information stored in a range of formats. Take those challenges, and then extrapolate them by attempting to map objects in our solar system, hundreds of thousands of miles away, and you get an idea of the challenge facing scientists attempting to map other planetary and extraplanetary bodies.

Various different companies and foundations are now using AI trained on existing datasets to more accurately identify features such as craters and other landforms on planets and moons that not only inform our understanding of worlds beyond our own, but may someday form the basis of how spacecraft are designed, where they aim to land as we expand our exploratory boundaries to new worlds, and even how agencies such as NASA make decisions on what missions they embark upon.

The future of AI mapping

As you will have seen from the examples above, AI is already having a demonstrable impact upon the way we create and use maps in a huge variety of different contexts, from outdoors to indoors and from underwater to outer space. But as astounding as AI’s growth into an easy-to-use, consumer-friendly tool has been in recent years, there’s still plenty of room for improvement when it comes to AI maps:

Many of the current uses of artificial intelligence boil down to essentially being able to take tasks that humans can do, but that may take hundreds or even thousands of hours, and work through them using datasets or training data. This can not only save huge amounts of time, but also money, and means that tasks that were either acting as huge impediments to accuracy (such as the example above of the company mapping each indoor retail location one by one, resulting in maps that would be months out of date before they were next updated) or were simply too large to tackle at all (such as reading, contextualizing and mapping the images of historic floods) are now possible.

In some respects, it’s the possibility of what AI will layer on top of existing maps that represents the next quantum leap forwards. Let’s use the trusty example of Google Maps once again. Currently, Google calculates traffic volume by the number of devices with the app installed in a particular place, and relies upon user reports to show specific accidents. When traffic is particularly heavy, Google may advise users navigating through the area of alternate routes they can take to avoid traffic and save time. However, this method requires an accumulation of traffic, which takes time, and can therefore mean drivers still get caught up in queues.

With AI, systems such as Google Maps could examine years of traffic data, potentially anticipate when queues and even accidents are most likely to occur, and route drivers around them before they’ve even happened. What’s more, with systems like Google Maps enjoying such popularity, it’s possible that an AI driven model could help to ease overall congestion on the roads by deliberately routing different portions of users to different routes, more effectively balancing the traffic levels and meaning each route has a more even split cars on it.

This is just one example, of which there are almost limitless possibilities.