How AI Can Help Cities Conserve Resources and Plan for the Future
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The world is changing swiftly, and nowhere are those changes felt more acutely than in our cities. Over half of the world’s population lives in urban areas, a trend only expected to accelerate in the coming decades.
The rapid growth of urban populations is concentrating environmental stress in key regions of the world, increasing demand for resources such as reliable energy and clean water, and pushing urban growth boundaries farther and farther into surrounding forests and farmlands. This tension between our need for urban development and our responsibility to the environment is strained further by factors like global climate change and increasingly severe weather events, which have devastated both our cities and the natural ecosystems that sustain them. Together, these factors present a complex challenge for city managers who need tools that can help them better understand, address and even predict environmental risks to their communities.
Fortunately, there are technology trends emerging today that hold tremendous promise for helping cities address these issues. Advances in artificial intelligence (AI) have the potential to exponentially increase our ability to collect and analyze enormous amounts of data on the state of our planet and empower land use planners with the insights and tools they need to build a more resilient and sustainable future.
One of the many challenges that urban planners face is a lack of current, detailed and easily accessible data about how their municipalities are changing. Land cover maps can help by showing what is where in a particular landscape, and how fast it is changing over time—visualizing everything from natural phenomena (like forests, rivers, lakes, and prairies) to human-made surfaces (such as cities, farms, buildings, roads, and parking lots). The better the picture of our lands, the more informed policymakers and communities will be as they make critical decisions for the future, such as how to grow sustainably, how to manage their natural resources effectively, and where to implement critical environmental protections and interventions like green infrastructure.
In the video below, from a demonstration at this year’s Esri User Conference, I explain how AI and deep learning can be used to develop enhanced mapping software for more sustainable land management.
Unfortunately, many local decision-makers do not have easy access to accurate and timely data. The most accessible land cover map for the United States is currently produced at 30 meter resolution, and is now 5 years out of date. Products like these are useful when asking very broad questions at a national scale, but the lack of spatial and temporal resolution minimizes the utility of these data for city-level planning. Other free, higher resolution imagery, such as the National Agriculture Imagery Program’s (NAIP) 1 meter-resolution data collected annually for the United States government, provides the detail required to spot trends and manage lands with a degree of resolution relevant to urban planning. However, analyzing these increasingly data-dense images to identify every new road, house, and other land use using standard approaches is a resource-intensive process. For example, a land cover map of the entire United States would require training an algorithm to recognize a large array of land uses across a wide variety of geographies and analyzing nearly 10 trillion pixels all in one go, a process requiring access to a level of computing resources and technical expertise that is far beyond what most nonprofits and government agencies can, or should, afford.
AI for Urban Sustainability
Fortunately, land cover mapping is an application perfectly suited for modern AI. Over the past year, Microsoft has partnered with the Chesapeake Conservancy, and the geospatial mapping and technology company Esri, to improve access to high resolution land cover maps across the United States. We started with data created by the Chesapeake Conservancy, who worked with Microsoft and ESRI technologies, along with partners throughout the Chesapeake Bay watershed, to map the roughly 100,000 square miles of land in the region. The new map provides 900 times the amount of information as conventional 30-meter resolution land cover data and enables land managers to see specific details like buildings or trees within larger plots of development or forest land, and pinpoint exactly where the bay’s ecosystems are becoming fragmented or damaged.
Now, we are using those data to train a single, powerful AI algorithm for mapping land cover at 1 meter resolution across the United States, with the ambition to enable on the fly land cover mapping through a simple user interface. Early results are promising, and should we succeed, land use managers at organizations of all sizes will be able to develop accurate, actionable, and sharable insights about what is happening on their lands more quickly than ever before.
In the video below, you can see how the Chesapeake Land Cover Mapping Project is already being used to help those that live and work on the Chesapeake Bay protect and restore their watershed.
Microsoft’s AI for Earth Initiative
This project showed us just how effective AI can be in solving pressing environmental and resource challenges facing cities. Land cover mapping helps understand the physical landscape of the world around us, which is a prerequisite to then make smarter decisions about overall resource management, smart transportation and grid solutions, and a host of other areas critical to building more sustainable cities.
That’s where Microsoft comes in, through existing programs like CityNext, focused on cities, and a newer program focused on AI applications for sustainability called AI for Earth. AI for Earth is built to help address some of the most significant barriers to harnessing AI for sustainability, and provides mechanisms to overcome them. These include access and education, so we have allocated $2 million to grants, trainings and projects to showcase the transformative potential of Microsoft’s AI technologies.
Ultimately, Microsoft’s AI for Earth program exists because of what cities around the world already know: the scale and speed of the changes we see in our physical and natural world require new solutions, and quickly. As Microsoft’s President and Chief Legal Officer Brad Smith wrote at the launch of AI for Earth, “There is no shortage of work to be done to chart a healthier, more sustainable future for our planet. Time is not our friend on this endeavor, and the stakes are high.” By working with organizations of all kinds, we hope to empower communities everywhere with the insights they need to solve today’s complex environmental challenges, and build a more resilient and sustainable future.
To learn more, please visit: https://www.microsoft.com/en-us/aiforearth
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In my business, we’d rather not be right. What gets a climate change expert out of bed in the morning is the desire to provide decision-makers with the best available science, and at the end of the day we go to bed hoping things won’t actually get as bad as our science tells us. That’s true whether you’re a physical or a social scientist.
Well, I’m one of the latter and Meeting of the Minds thought it would be valuable to republish an article I penned in January 2020. In that ancient past, only the most studious of news observers had heard of a virus in Wuhan, China, that was causing a lethal disease. Two months later we were in lockdown, all over the world, and while things have improved a lot in the US since November 2020, in many cities and nations around the world this is not the case. India is living through a COVID nightmare of untold proportions as we speak, and many nations have gone through wave after wave of this pandemic. The end is not in sight. It is not over. Not by a longshot.
And while the pandemic is raging, sea level continues to rise, heatwaves are killing people in one hemisphere or the other, droughts have devastated farmers, floods sent people fleeing to disaster shelters that are not the save havens we once thought them to be, wildfires consumed forests and all too many homes, and emissions dipped temporarily only to shoot up again as we try to go “back to normal.”
So, I’ll say another one of those things I wish I’ll be wrong about, but probably won’t: there is no “back to normal.” Not with climate change in an interdependent world.