Journal
XXIV ISPRS CONGRESS IMAGING TODAY, FORESEEING TOMORROW, COMMISSION IV
Volume 5-4, Issue -, Pages 293-300Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/isprs-annals-V-4-2022-293-2022
Keywords
data integration; data quality dimensions; SDG 11; Earth observation; building footprints; urban planning; informal settlements; humanitarian applications
Funding
- Austrian Federal Ministry for Digital and Economic Affairs
- Christian Doppler Research Association
- Medicins Sans Frontieres (MSF, Arzte ohne Grenzen) Austria
- Federal Government of the Province of Salzburg (WISS 2025 initiative)
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More than half of the world's population lives in urban areas, with over 1 billion people lacking basic services and infrastructure. Spatially targeted, data-driven policies are crucial for sustainable urban planning to improve these situations and increase resilience. Earth observation can support the achievement of the Sustainable Development Goals, particularly SDG 11. The integration of heterogeneous datasets for humanitarian response requires adequate data assimilation strategies and a good understanding of data quality.
More than half of the world-population lives in urban areas, with more than 1 billion people lacking basic services and infrastructure. Spatially targeted, data-driven policies are crucial for sustainable urban planning to improve these situations and increase the resilience. Earth observation (EO) can support the process of achieving the SDGs, in particular SDG 11. Aiming at such high-level targets requires a multi-source data environment, defining and extracting suitable EO-based indicators and linking them with socio-economic or environmental data. When embedded in the context of humanitarian response, where physical access to regions is often limited while at the same time, insights on several scales of intervention are key to rapid decisions, the integration of (potentially) heterogeneous datasets requires adequate data assimilation strategies and a good understanding of data quality. This paper investigates the usability of datasets regarding technical and organisational aspects from an application-driven point of view. We suggest a protocol considering various quality dimensions to evaluate via scoring the fitness of multi-source geospatial datasets to integration. The aim is to provide a general orientation towards data assimilability in the context of deriving higher-level indicators, while specific constraints and the need to relativize may occur for concrete use case.
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