Where We Live - A Summary of the Achievements and Planned Evolution of the Global Urban Footprint

The TerraSAR-X (TSX) mission provides a distinguished collection of high resolution satellite images that shows great promise for a global monitoring of human settlements. Hence, the German Aerospace Center (DLR) has developed the Urban Footprint Processor (UFP) that represents an operational framework for the mapping of built-up areas based on a mass processing and analysis of TSX imagery. The UFP includes functionalities for data management, feature extraction, unsupervised classification, mosaicking, and post-editing. Based on >180.000 TSX StripMap scenes, the UFP was used in 2016 to derive a global map of human presence on Earth in a so far unique spatial resolution of 12 m per grid cell: the Global Urban Footprint (GUF). This work provides a comprehensive summary of the major achievements related to the Global Urban Footprint initiative, with dedicated sections focusing on aspects such as UFP methodology, basic product characteristics (specification, accuracy, global figures on urbanization derived from GUF), the user community, and the already initiated future roadmap of follow-on activities and products. The active community of >250 institutions already working with the GUF data documents the relevance and suitability of the GUF initiative and the underlying high-resolution SAR imagery with respect to the provision of key information on the human presence on earth and the global human settlements properties and patterns, respectively.

[1]  T. Esch,et al.  Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data , 2009 .

[2]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[3]  Annekatrin Metz,et al.  Dimensioning urbanization – An advanced procedure for characterizing human settlement properties and patterns using spatial network analysis , 2014 .

[4]  Iain Stewart,et al.  Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities , 2015, ISPRS Int. J. Geo Inf..

[5]  Hannes Taubenböck,et al.  TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns , 2012 .

[6]  Hansheng Wang,et al.  Spatiotemporal Characterization of Land Subsidence and Uplift (2009-2010) over Wuhan in Central China Revealed by TerraSAR-X InSAR Analysis , 2016, Remote. Sens..

[7]  Andreas Schenk,et al.  Delineation of Urban Footprints From TerraSAR-X Data by Analyzing Speckle Characteristics and Intensity Information , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Jean-Marie Nicolas,et al.  3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities , 2017, Remote. Sens..

[9]  Martin Boettcher,et al.  Processing Management Tools for Earth Observation Products at DLR-DFD , 2001 .

[10]  Massimiliano Pittore,et al.  Large-area settlement pattern recognition from Landsat-8 data , 2016 .

[11]  Mingsheng Liao,et al.  Health Diagnosis of Major Transportation Infrastructures in Shanghai Metropolis Using High-Resolution Persistent Scatterer Interferometry , 2017, Sensors.

[12]  Achim Roth,et al.  TerraSAR-X: How can high resolution SAR data support the observation of urban areas? , 2005 .

[13]  Alberto Gianoli,et al.  Towards the Development of an Integrated Sustainability and Resilience Benefits Assessment Framework of Urban Green Growth Interventions , 2016 .

[14]  D. Civco,et al.  Mapping urban areas on a global scale: which of the eight maps now available is more accurate? , 2009 .

[15]  Xin Xu,et al.  Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis , 2016, Remote. Sens..

[16]  Andreas Schenk,et al.  Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Xinwu Li,et al.  Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province , 2013, Remote. Sens..

[18]  Paolo Gamba,et al.  A robust approach to global urban area extent extraction using ASAR Wide Swath Mode data , 2012, 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS).

[19]  R. Sibson,et al.  A brief description of natural neighbor interpolation , 1981 .

[20]  L. Dijkstra,et al.  Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping , 2017 .

[21]  Un Desa Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .

[22]  T. Esch,et al.  Breaking new ground in mapping human settlements from space – The Global Urban Footprint , 2017, 1706.04862.

[23]  Paolo Gamba,et al.  Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor , 2015 .

[24]  Ryosuke Shibasaki,et al.  An Automated Method for Global Urban Area Mapping by Integrating ASTER Satellite Images and GIS Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Annekatrin Metz,et al.  EARTH OBSERVATION-SUPPORTED SERVICE PLATFORM FOR THE DEVELOPMENT AND PROVISION OF THEMATIC INFORMATION ON THE BUILT ENVIRONMENT – THE TEP-URBAN PROJECT , 2016 .

[26]  T. Lützkendorf,et al.  Assessing a sustainable urban development: typology of indicators and sources of information. Abstract , 2016 .

[27]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[28]  Chun-Jen Chen,et al.  A linear-time component-labeling algorithm using contour tracing technique , 2004, Comput. Vis. Image Underst..

[29]  Thomas Esch,et al.  Exploiting big earth data from space – first experiences with the timescan processing chain , 2018 .

[30]  Ab Halim Abu Bakar,et al.  A Framework for Assessing the Sustainable Urban Development , 2013 .

[31]  Hannes Taubenböck,et al.  Validation of the DLR Global Urban Footprint in rural areas: A case study for Burkina Faso , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[32]  Olivier Arino,et al.  Global Land Cover Map for 2009 (GlobCover 2009) , 2012 .

[33]  Carsten Brockmann,et al.  Calvalus: Full-mission EO cal/val, processing and exploitation services , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[34]  Thomas Esch,et al.  Monitoring of Urban Environments with TerraSAR-X Data , 2007 .

[35]  Hannes Taubenböck,et al.  How good is the map? A multi-scale cross-comparison framework for global settlement layers: Evidence from Central Europe , 2016 .

[36]  Thomas Esch,et al.  Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission , 2013, IEEE Geoscience and Remote Sensing Letters.

[37]  Martin Habermeyer,et al.  W42 - a scalable spatial database system for holding Digital Elevation Models , 2009, 2009 17th International Conference on Geoinformatics.

[38]  M. Friedl,et al.  Mapping global urban areas using MODIS 500-m data: new methods and datasets based on 'urban ecoregions'. , 2010 .

[39]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[40]  Thomas Esch,et al.  A novel method for building height estmation using TanDEM-X data , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.