Evaluating the Relationship Between Contextual Features Derived from Very High Spatial Resolution Imagery and Urban Attributes: A Case Study in Sri Lanka

Extracting information about variations within urban areas using satellite imagery has generally focused on mapping individual buildings or slum versus non-slum areas. While these data are useful, they can run into issues in very dense urban areas, additionally slums have a subjective definition. In previous research we have found that contextual features are related to population, census variables, poverty, and other values, but have not explored which urban attributes (i.e., buildings and roads) these features represent. In this study we seek to determine the correlation between contextual features calculated on Very High Spatial Resolution (VHSR) satellite data and urban attributes derived from Open Street Map (OSM) for portions of multiple cities in Sri Lanka. Results indicate that individual contextual features are highly correlated with building area, building density, road area, road density, total built up areas and other features. Moreover, when multiple contextual features are combined within a model, they can explain from 70 to 92 percent of the variance of these urban features within the study area. This indicates that contextual features are very strong indicators of urban variability and can be used to map differences within the urban setting. This may allow us to forgo having to map each building and road individually for mapping urban areas in future projects.

[1]  Sang Michael Xie,et al.  Combining satellite imagery and machine learning to predict poverty , 2016, Science.

[2]  Warren C. Jochem,et al.  Spatially disaggregated population estimates in the absence of national population and housing census data , 2018, Proceedings of the National Academy of Sciences.

[3]  Kasey Jones,et al.  Toward Model-Generated Household Listing in Low- and Middle-Income Countries Using Deep Learning , 2018, ISPRS Int. J. Geo Inf..

[4]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[5]  Myung Jin Chung,et al.  A robust line extraction method by unsupervised line clustering , 1999, Pattern Recognit..

[6]  N. Lam,et al.  Urban Textural Analysis from Remote Sensor Data: Lacunarity Measurements Based on the Differential Box Counting Method , 2006 .

[7]  Li WangDong-Chen He,et al.  Texture classification using texture spectrum , 1990, Pattern Recognit..

[8]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[11]  Jonathan Hersh,et al.  Evaluating the relationship between spatial and spectral features derived from high spatial resolution satellite data and urban poverty in Colombo, Sri Lanka , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[12]  Anil M. Cheriyadat,et al.  Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Ryan Engstrom,et al.  Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being , 2017, The World Bank Economic Review.

[14]  D. Gabor,et al.  Theory of communication. Part 1: The analysis of information , 1946 .

[15]  Martino Pesaresi,et al.  A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Catherine Linard,et al.  Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data , 2015, PloS one.