Towards large-area morphologic characterization of urban environments using the TanDEM-X mission and Sentinel-2

Analysis of urbanization processes and assessments of natural hazard risks, among others, require detailed information of the physical properties of urban environments. In this paper, we present a multistep procedure for morphologic characterization of urban environments by estimating average built-up height and share of built-up area for spatial processing units. Thereby, we rely on data from the TanDEM-X mission and Sentinel-2. These earth observation systems feature a notable tradeoff between a fairly high spatial resolution and large-area coverage and, thus, allow for spatially continuous and consistent analysis over urban environments. Experimental results were obtained for the city of Munich (Germany). Estimated average built-up height features a mean absolute error (MAE) of 2.7 m (i.e., less than one floor) and share of built-up area could be estimated with a MAE of 13.3 %, when compared to a reference data set.

[1]  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.

[2]  Gerhard Krieger,et al.  TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Benjamin Bechtel,et al.  Classification of Local Climate Zones Based on Multiple Earth Observation Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Tobia Lakes,et al.  TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from Berlin, Germany , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Hannes Taubenböck,et al.  Building Types’ Classification Using Shape-Based Features and Linear Discriminant Functions , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Hannes Taubenböck,et al.  Estimation of Seismic Vulnerability Levels of Urban Structures With Multisensor Remote Sensing , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[8]  Thomas Blaschke,et al.  Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data , 2014, Remote. Sens..

[9]  Hannes Taubenböck,et al.  Normalization of TanDEM-X DSM Data in Urban Environments With Morphological Filters , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[11]  T. Esch,et al.  Delineation of Central Business Districts in mega city regions using remotely sensed data , 2013 .

[12]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[13]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[14]  Hannes Taubenböck,et al.  Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques , 2015 .

[15]  Hannes Taubenböck,et al.  Investigating the Applicability of Cartosat-1 DEMs and Topographic Maps to Localize Large-Area Urban Mass Concentrations , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Hannes Taubenböck,et al.  Assessment of Seismic Building Vulnerability from Space , 2014 .

[17]  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.