Xiaoxiang Zhu’s talk

  • Data Science for Global Urban Mapping – 10^16 Bytes from Social Media to EO Satellites

Abstract : Global urbanization is one of the most important megatrends of global change. By 2050, around three quarters of the world’s population will live in cities. The new dimension of ongoing global migration into the cities poses fundamental challenges to our societies across the globe. Despite of increasing efforts, global urban mapping still drags behind the geometric, thematic and temporal resolutions of geo-information needed to address these challenges.

Recently, big Earth observation data amounts in the order of tens of Petabytes (PBs) from complementary data sources have become available. For example, Earth observation (EO) satellites of space agencies reliably provide geodetically accurate large scale geo-information of worldwide cities on a routine basis from space. But the data availability is limited in resolution and viewing geometry. On the other hand, constellations of small and less expensive satellites owned by commercial players, like “Planet”, have been providing images for global coverage on a daily basis since 2017, yet with reduced geometric and radiometric accuracy. As complementary sources of geo-information massive imagery, text messages and GIS data from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality.

To this end, we jointly exploit big data from social media and satellite observations for global urban mapping, and aim at breakthroughs in 3D/4D urban modelling, infrastructure occupancy classification, and very high resolution population density mapping on a global scale for revolutionizing urban geographic research. In this talk, recent achievements will be presented, including the first global map we achieved – the global urban local climate zones classification, and a first impression of the global 3D urban model we are about to generate. Our vision is to enable the generation of the first and unique 3D and 4D global consistent spatial data set on the urban morphology of settlements, which is particularly important for developing countries with their rapidly growing cities and slums. This would be seen as a giant leap for urban geography research as well as for formation of opinions for stakeholders based on resilient data.