Not one, but two Category 5 hurricanes wreaked destruction on numerous small islands, causing severe damages on islands like Barbuda, Dominica, and Saint Martin. The human cost of these disasters was immense, and the impact of this devastation was felt most strongly by poorer communities in the path of the storms.
It is 7:45 p.m. in Ponto-cho, the historic narrow alley at the core of the Japanese city of Kyoto. Close to the Kaburenjo Theater – where still today Geikos and Maikos (Kyoto Geishas) practice their dances and performances – the traditional adjoining buildings with restaurants and shops are full of guests. Local people, tourists, students… On this Saturday in mid-April, the warm weather brings a lot of people to the streets nearby.
At 7:46 p.m., a M 5.1 earthquake strikes. Seven seconds of swaying. It doesn’t cause major damage, but it is enough to spread panic among a group of tourists. Screams, shoving, confusion… drinks spill, candles fall, people rush.
At 7:49 p.m., the fire starts spreading through the old wooden structures, also threatening the historic theater. Access is difficult due to the narrow streets and panicking crowd.
What happens next?
It could be a fire in the Ponto-cho traditional alley. It could be an earthquake shaking the historic center of Kathmandu (Nepal), the archaeological site of Bagan (Myanmar), or the historic town of Amatrice (Italy). It could be Typhoon Haiyan in the Philippines or Hurricane Irma in the Caribbean, blasting sites with rain, flooding, and gale-force winds.
Cultural heritage assets around the world are at risk. They are often vulnerable due to their age, as well as previous interventions and restorations made without disaster risk or overall site stability in mind. Heritage sites reflect legacies, traditions, and identities. With all this, they carry a large cultural and emotional value of what could be lost – certainly beyond the traditional calculus of economic losses.
In many cases, it is not possible or advisable to conduct reconstruction on cultural heritage sites post-disaster. Therefore, the essence and soul of a cultural heritage site is at risk of being lost forever, making preparedness and preservation even more critical.
The challenge of anticipating and communicating the risk of volcanic eruptions to communities requires complex decision-making. Ecuador’s Cotopaxi Volcano and Indonesia’s Mount Agung are recent examples where the warning signs were present (small earthquakes, increasing gas emissions, and more), yet an eruption came much later than expected. Volcanic eruptions are therefore a double-edged sword that often creates a decision-making dilemma. While signs of volcanic activity can provide adequate time for preparation and evacuation, the very same signs can also create conditions of extreme uncertainty, which can be exacerbated by piecemeal communication around eruption events.
Last week, the world came to attention when the famous Hulene dumpsite in Maputo, Mozambique collapsed under heavy rains, killing at least 16 people.
Buried under piles of waste were homes and people from one of the most impoverished settlements in Mozambique. Many members of this community made a living collecting and selling recyclables from the dumpsite, which had served as the final disposal site for greater Maputo since the 1960s.
The year of 2017 was one of many recent reminders of that “new normal”—from Hurricanes Harvey, Irma, and Maria that pounded coastal United States and the Caribbean to the severe drought that struck Somali, which led to the displacement and even life losses of individuals and families.
Too often, however, that dream risks remaining an urban daydream, due to natural disasters such as hurricanes, earthquakes, and floods, as well as climate change. Those of us working to help these families find a better future must focus more on ways to support efforts to protect their lives – and their livelihoods.
In the 40 years since the launch of Habitat I, governments and municipalities throughout emerging and developing countries have been proving that their cities can be not only inclusive and secure, but also resilient and sustainable. However, unless they increase their speed and scale, they are unlikely to achieve the goals of the “New Urban Agenda” and its Regional Plans, launched at Habitat III in 2016.
From our perspective helping governments in Latin America and the Caribbean, and ahead of the World Urban Forum taking place in Kuala Lumpur, Malaysia in February, let us share three key ingredients necessary to achieve that goal:
Children are often told that home is where to run inside when thunders hit or when the rain comes, and that home is a safe place. However, for billions of people in the world, it is not.
By 2030, it is estimated that 3 billion people will be at risk of losing a loved one or their homes—usually their most important assets—to natural disasters. In fact, the population living on flood plains or cyclone-prone coastlines is growing twice as faster as the population in safe homes in safer areas.
Due to climate change, extreme weather and other natural hazard events hit these populations harder and more often. The 10 natural disasters causing the most property damages and losses in history have occurred since 2005. The damages and losses were highly concentrated in the housing sector. While the poor experience 11% of total of asset losses, they suffer 47% of all the well-being losses. Worse, natural disasters can lead to unnecessary losses of life, with earthquakes alone causing 44,585 deaths on average per year. This is an issue that policymakers and mayors need to address if they don’t want their achievements in poverty reduction to be erased by the next hurricane or earthquake.
In the previous blog, we discussed how remote sensing techniques could be used to map and inform policymaking in secondary cities, with a practical application in 10 Central American cities. In this post, we dive deeper into the caveats and considerations when replicating these data and methods in their cities.
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.
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.