GIScience 2016 Short Paper Proceedings Assessing Spatiotemporal Agreement between Multi- Temporal Built-up Land Layers and Integrated Cadastral and Building Data Johannes H. Uhl 1 , Stefan Leyk 1 , Aneta J. Florczyk 2 , Martino Pesaresi 2 , Deborah Balk 3 University of Colorado Boulder, Department of Geography, Boulder, CO 80309, U.S.A. Email: {johannes.uhl; stefan.leyk}@colorado.edu European Commission – Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Global Security and Crisis Management Unit, 21027 Ispra, Italy Email: {martino.pesaresi; aneta.florczyk}@jrc.ec.europa.eu City University of New York, Institute for Demographic Research and Baruch College, New York, NY 10010, U.S.A. Email: deborah.balk@baruch.cuny.edu Abstract There is an increasing availability of multi-temporal land use and built-up land datasets. However, little research has been done regarding the spatiotemporal uncertainty of these data products. In this work we present an approach that has the potential to be applicable for spatiotemporal evaluation of the novel Global Human Settlement Layer (GHSL) created by automatic classification of global collections of Landsat data recorded in the epochs 1975, 1990, 2000, and 2014. The proposed approach produces the reference data by integrating publicly available parcel and building data and derives agreement statistics with the GHSL. 1. Introduction The Global Human Settlement Layer (GHSL) project aims to assess the human presence in the planet by estimating the amount of built-up area based on remote sensing data and census data (Pesaresi et al. 2015). In Pesaresi et al. (2013) the GHSL information production workflow was tested for sensors ranging between 0.5 and 10m spatial resolution, which usually perform very well in detection of built-up areas but are typically constrained regarding data access, redistribution rights, and are typically not available consistently for long periods of time. For these reasons, these data are difficult to use for spatiotemporally uniform and systematic extraction of built-up areas on global, national or even regional level. Therefore, the GHSL workflow was tested with global collections of publicly available Landsat imagery collected in the past 40 years (Pesaresi et al. 2016). The Landsat GHSL dataset is available as seamless global mosaic at high spatial resolution (approx. 38m) and for various points in time (1975, 1990, 2000, 2014, see Figure 1a). GHSL data offers promising opportunities for population projections, disaster management and risk assessment (Freire et al. 2015; Freire et al. 2016), as well as for analysing and modelling urban dynamics and land use change. Before such novel data products can be made available to the research community, an extensive quality assessment is required. However, such assessments are difficult due to the lack of reliable reference data particularly for earlier time periods and in less developed regions. In this paper we present and discuss a novel approach to evaluate multi-temporal spatial data on built-up land such as GHSL or developed land cover classes using publicly available parcel (cadastral) data integrated with building footprints in the U.S. The aim of this study is to establish a protocol for future testing the accuracy of fine-scale multi-temporal products derived from automatic classification of satellite data featuring the presence of built-up areas. The selection of the test areas discussed here are driven and constrained by the availability of the reference data and consequently the results derived are