Growing urban human settlements is one of the most significant global trends with wide reaching impact on freshwater resources. By 2050 all regions are expected to be predominantly urban and this unprecedented urban expansion poses an array of critical, water-related challenges, from access to access to clean water and sanitation to environmental and human security. Earth observation holds the potential for monitoring long-term urbanization processes and supporting a wide range of urban planning applications related to infrastructure development, soil protection, waste management, water supply systems, and disaster mitigation (e.g. flood vulnerabiliy).
EO has routinely demonstrated its usefulness in analyzing changes in settlement characterization. A wide variety of satellite sensors can be used for the urban characterization studies and provide urban base maps and land use change maps for a range of urban planning applications related to infrastructure development, soil protection, waste management, water supply and sanitation systems, and flood risk control etc. These sensors include optical sensors (e.g. Spot, IKONOS, QuickBird, WorldView) and radar sensors (e.g. TerraSAR-X). HR and VHR are available at 0.5 – 10 meter pixel resolution.
Extracting and managing urban datasets at different scales by using EO data is one of the main challenges of remote sensing data interpretation. The complexity of designing data analysis procedures for multiple sensors at multiple spatial and spectral resolutions added another limitation for promising techniques of extracting urban datasets for characterization. In addition, texture analysis allows a precise estimation of land cover, but remain a proxy to land use characterization. Only partial identification by automatic techniques is allowed and require additional fine-tuning manually to obtain a complete and precise settlement characterization.
Regular improvements of computation capacities and its combination with potential new high-resolution digital surface models will significantly improve settlement characterization. Moreover, its combination and comparison with additional detailed source of geo-data, including Open Data and Crowdsourcing data would provide better understanding of settlement characterization for multiple decision makers.