Abstract
Urbanization is the second largest mega-trend right after climate change. Accurate measurements of urban morphological and demographic figures are at the core of many international endeavors to address issues of urbanization, such as the United Nations’ call for “Sustainable Cities and Communities”. In many countries – particularly developing countries –, however, this database does not yet exist. Here, we demonstrate a novel deep learning and big data analytics approach to fuse freely available global radar and multi-spectral satellite data, acquired by the Sentinel-1 and Sentinel-2 satellites. Via this approach, we created the first-ever global and quality controlled urban local climate zones classification covering all cities across the globe with a population greater than 300,000 and made it available to the community (https://doi.org/10.14459/2021mp1633461). Statistical analysis of the data quantifies a global inequality problem: approximately 40% of the area defined as compact or light/large low-rise accommodates about 60% of the total population, whereas approximately 30% of the area defined as sparsely built accommodates only about 10% of the total population. Beyond, patterns of urban morphology were discovered from the global classification map, confirming a morphologic relationship to the geographical region and related cultural heritage. We expect the open access of our dataset to encourage research on the global change process of urbanization, as a multidisciplinary crowd of researchers will use this baseline for spatial perspective in their work. In addition, it can serve as a unique dataset for stakeholders such as the United Nations to improve their spatial assessments of urbanization.
Highlights
By combining the urban morpho logical map with a global population map, we discovered that the 30% sparsely built area contains less than 10% of the total population of the 1692 cities, whereas the 25% low-rise area contains 26% of the total population
General assessment We evaluated the performance of our deep learning model based on the three data splits mentioned in the previous section
We see that the three different evaluation strategies illustrate that our Local climate zones (LCZs) maps have an average accuracy between about 50% for cities that show characteristics disjunct from the training set, and more than 80% for cities that are fairly similar to the training distribution
Summary
Urbanization is one of the most important trends in global change. a near-perfect correlation is verified between urbanization and economic prosperity of societies (Glaeser et al, 2013), this correlation does not automatically lead to a golden future: instead, the unprece dented dynamics and dimensions of natural growth of and migration into cities pose fundamental challenges to our human societies across the globe. Many international endeavors addressing issues of urbani zation, such as the United Nations’ call for “Sustainable Cities and Communities,” are based on accurate measurements of urban morphological and demographic figures Such measurements provide key scientific foundations for the allocation of valuable resources for a wide range of stakeholders and form the basis of global efforts to un derstand and track progress in improving human livelihoods. The World Urban Database provides intra-urban local climate zone classification maps (Stewart et al, 2014; Stewart and Oke, 2012) – only for about 100 metropolises and to a best resolution of a few hundred meters Despite their significant contribution to large scale urban mapping, these approaches have not addressed the variations of intra-urban morphology. Its huge po tential in global urban mapping using EO data is ripe for discovery
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