For example, a Cairo-based startup called “Swvl” is disrupting commuting in the In the Middle East and North Africa region by mapping out commuters’ travel directions and enabling app-based, affordable bus rides that can compete with on-demand ride-hailing.
Machine learning algorithms are excellent at answering “yes” or “no” questions. For example, they can scan huge datasets and correctly tell us: Does this credit card transaction look fraudulent? Is there a cat in this photo?
But it’s not only the simple questions – they can also tackle nuanced and complex questions.
Today, machine learning algorithms can detect over 100 types of cancerous tumors more reliably than a trained human eye. Given this impressive accuracy, we started to wonder: what could machine learning tell us about where people live? In cities that are expanding at breathtaking rates and are at risk from natural disasters, could it warn us that a family’s wall might collapse during an earthquake or rooftop blow away during a hurricane?
Droughts, floods, hurricanes, and other disasters displaced over 24 million people in 2016. This is crucial, as land and homes are usually the main assets that people have.
Land and geospatial information tells the what, who, where, how much, and other key attributes of a property. Without this information, it is almost impossible for cities and communities to develop proper disaster response or preparedness plans.
– by providing accessible and instant data on disaster impact, the value of losses, the beneficiaries, as well as the levels of appropriate compensation and required investment to restore activities.
Global positioning systems (GPS), real time traffic maps, accurate weather forecasts, Uber, self-driving cars…
“Geospatial,” or location-based data has existed for hundreds of years – for example, in street and topographical maps. What’s different is how quickly new information is being gathered and the more sophisticated analytics that is being applied to it, thanks to technological advances.
What was once information only found in the domain of government, military, and select private sector, even up to the 1980s and 90s, has come into broad use over the last 20 years.
This summer, some tens of millions of people in the U.S. traveled to see the total solar eclipse, including a co-author of this blog. Not only was the eclipse amazing – but the drive back from Tennessee to Washington, D.C. showed the integration and impact of geospatial information in our daily lives.
But, what happens when the palm tree is cut or when the street vendor changes the location?
The absence of street names poses not only challenges for orientation, but also for property tax collection, postal services, emergency services, and the private sector. Especially, new economy companies, such as Amazon or Uber, depend on street addressing systems and are eager to cater to market demands of a growing middle class.
To address these challenges, the Accra Metropolitan Assembly (AMA), financed by the World Bank’s second Land Administration Project , is implementing a street addressing and property numbering system in Accra. Other Metropolitan areas received funding from other World Bank-funded projects for similar purposes.
Can we rely only on satellite? How accurate are these results?
It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.
So, it’s important to learn how well the resulting maps reflect the reality on the ground.
Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.
Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.
|Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).|
The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.
But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.
The question remains:
In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.
A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.
Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.
What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.
Want to explore our maps? Download our data here. Click here for an interactive land cover map of La Ceiba.
Many urban residents these days will find it hard to imagine a life without mobile apps that help us locate a restaurant, hail a cab, or find a subway station—usually in a matter of seconds. for example, geospatial data on land-use change and built-up land expansion can provide for more responsive urban planning, while information on traffic conditions, road networks, and solid waste sites can help optimize management and enhance the quality of urban living.
The “urban geo-data gap”
However, information and data that provide the latest big picture on urban land and services often fail to keep up with rapid population growth and land expansion. This is especially the case for cities in developing countries—home to the fastest growing urban and vulnerable populations.