Evaluating the Ability to Use Contextual Features Derived from Multi-Scale Satellite Imagery to Map Spatial Patterns of Urban Attributes and Population Distributions

Abstract

With an increasing global population, accurate and timely population counts are essential for urban planning and disaster management. Previous research using contextual features, using mainly very-high-spatial-resolution imagery (<2 m spatial resolution) at subnational to city scales, has found strong correlations with population and poverty. Contextual features can be defined as the statistical quantification of edge patterns, pixel groups, gaps, textures, and the raw spectral signatures calculated over groups of pixels or neighborhoods. While they correlated with population and poverty, which components of the human-modified landscape were captured by the contextual features have not been investigated. Additionally, previous research has focused on more costly, less frequently acquired very-high-spatial-resolution imagery. Therefore, contextual features from both very-high-spatial-resolution imagery and lower-spatial-resolution Sentinel-2 (10 m pixels) imagery in Sri Lanka, Belize, and Accra, Ghana were calculated, and those outputs were correlated with OpenStreetMap building and road metrics. These relationships were compared to determine what components of the human-modified landscape the features capture, and how spatial resolution and location impact the predictive power of these relationships. The results suggest that contextual features can map urban attributes well, with out-of-sample R2 values up to 93%. Moreover, the degradation of spatial resolution did not significantly reduce the results, and for some urban attributes, the results actually improved. Based on these results, the ability of the lower resolution Sentinel-2 data to predict the population density of the smallest census units available was then assessed. The findings indicate that Sentinel-2 contextual features explained up to 84% of the out-of-sample variation for population density.

Overview of the Special Issue on Applications of Remote Sensing Imagery for Urban Areas

“…This Special Issue (SI) aims to invite recent advances in the applications of RS imagery for urban areas, and 17 papers in total were selected and published. Among them, 12 papers emphasize the novel urban application algorithms based on RS imageries, such as urban attribute mapping, building extraction, classification, change detection, and so on [1][2][3][4][5][6][7][8][9][10][11][12], and 5 papers directly employed RS imageries to analyze the environmental variations and urban expansion in typical cities, such as urban heat island, air pollution, lightning, and so on [13][14][15][16][17].…”

“…There are also three other papers that aimed at different demands of urban RS applications [10][11][12]. Chao et al [10] analyzed the ability to utilize contextual features from very-high-spatial-resolution (<2 m) and medium-spatial-resolution (Sentinel-2, 10 m) imageries to model the urban attributes and population density under the human-modified landscape. The results suggest that contextual features can model urban attributes well at very high spatial resolutions, with out-of-sample R 2 values up to 93%.…”

“…In summary, this SI provides an enhanced understanding of applications of RS imagery for urban areas. New methodologies are presented for extracting the building information, modeling urban attributes and population, and detecting the urban flooding using remote sensing imageries [1][2][3][4]10,12]. Novel models for RS classification, target and change detection are also included, which can significantly support further urban applications [5][6][7][8][9].…”