Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective

ABSTRACT There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modeling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e. presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.

[1]  Stefan Leyk,et al.  Assessing the Accuracy of Multi-Temporal Built-Up Land Layers across Rural-Urban Trajectories in the United States. , 2018, Remote sensing of environment.

[2]  Fabio Del Frate,et al.  Monitoring Urban Land Cover in Rome, Italy, and Its Changes by Single-Polarization Multitemporal SAR Images , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  G. H. Rosenfield,et al.  Applications of statistics to thematic mapping. , 1980 .

[4]  Sérgio Freire,et al.  Combining GHSL and GPW to improve global population mapping , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[5]  Huadong Guo,et al.  A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Jianliang Huang,et al.  Producing more grain with lower environmental costs , 2014, Nature.

[7]  Stefan Leyk,et al.  A framework for radiometric sensitivity evaluation of medium resolution remote sensing time series data to built-up land cover change , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[8]  Barbara P. Buttenfield,et al.  Exploiting temporal information in parcel data to refine small area population estimates , 2016, Comput. Environ. Urban Syst..

[9]  Conferencia de las Naciones Unidas sobre Comercio y Desarrollo.  World Economic Situation and Prospects 2020 , 2020, World Economic Situation and Prospects (WESP).

[10]  Pierre Soille,et al.  A versatile data-intensive computing platform for information retrieval from big geospatial data , 2018, Future Gener. Comput. Syst..

[11]  Steven M. Manson,et al.  Parcel Data for Research and Policy , 2009 .

[12]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[13]  Martino Pesaresi,et al.  Assessing Spatiotemporal Agreement between Multi-Temporal Built-up Land Layers and Integrated Cadastral and Building Data , 2016 .

[14]  Son Lam Phung,et al.  Learning Pattern Classification Tasks with Imbalanced Data Sets , 2009 .

[15]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[16]  J. Wickham,et al.  Accuracy assessment of NLCD 2006 land cover and impervious surface , 2013 .

[17]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[18]  A. Luckman,et al.  Mapping urban change in the UK using satellite radar interferometry , 2003 .

[19]  Martino Pesaresi,et al.  Comparison of built‐up area maps produced within the global human settlement framework , 2018, Trans. GIS.

[20]  Julea Andreea Maria,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 .

[21]  Eric W. Constance,et al.  The 3D Elevation Program initiative: a call for action , 2014 .

[22]  Stefan Leyk,et al.  Enhancing areal interpolation frameworks through dasymetric refinement to create consistent population estimates across censuses , 2018, Int. J. Geogr. Inf. Sci..

[23]  Jiangye Yuan,et al.  Automatic Building Extraction in Aerial Scenes Using Convolutional Networks , 2016, ArXiv.

[24]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[25]  Craig A. Knoblock,et al.  Extracting Human Settlement Footprint from Historical Topographic Map Series Using Context-Based Machine Learning , 2017 .

[26]  R. Pontius,et al.  Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment , 2011 .

[27]  Pierre Soille,et al.  Automated global delineation of human settlements from 40 years of Landsat satellite data archives , 2019, Big Earth Data.

[28]  Deborah Balk,et al.  What is urban? Comparing a satellite view with the Demographic and Health Surveys. , 2013 .

[29]  Pesaresi Martino,et al.  Benchmarking of the Symbolic Machine Learning classifier with state of the art image classification methods - application to remote sensing imagery , 2015 .

[30]  Anna F. Tapp,et al.  Areal Interpolation and Dasymetric Mapping Methods Using Local Ancillary Data Sources , 2010 .

[31]  C. Woodcock,et al.  Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data , 2012 .

[32]  Son V. Nghiem,et al.  Up and out: A multifaceted approach to characterizing urbanization in Greater Saigon, 2000–2009 , 2019, Landscape and Urban Planning.

[33]  J. Wickham,et al.  Impacts of Patch Size and Land-Cover Heterogeneity on Thematic Image Classification Accuracy , 2002 .

[34]  Pesaresi Martino,et al.  Community pre-Release of GHS Data Package (GHS CR2018) in support to the GEO Human Planet Initiative , 2018 .

[35]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[36]  Barbara P. Buttenfield,et al.  Modeling residential developed land in rural areas: A size-restricted approach using parcel data , 2014 .

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

[38]  G. Lemoine,et al.  Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence , 2018 .

[39]  Martino Pesaresi,et al.  A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning , 2016, Remote. Sens..

[40]  Barbara P. Buttenfield,et al.  Establishing relationships between parcel data and land cover for demographic small area estimation , 2013 .