A review of regional science applications of satellite remote sensing in urban settings
By Jorge E. Patiño, J. Duque. Computers, Environment and Urban Systems, 2013
333 citations recorded by the source corpus.
This paper reviews the potential applications of satellite remote sensing to regional science research in urban settings. Regional science is the study of social problems that have a spatial dimension. The availability of satellite remote sensing data has increased significantly in the last two decades, and these data constitute a useful data source for mapping the composition of urban settings and analyzing changes over time. The increasing spatial resolution of commercial satellite imagery has influenced the emergence of new research and applications of regional science in urban settlements because it is now possible to identify individual objects of the urban fabric. The most common applications found in the literature are the detection of urban deprivation hot spots, quality of life index assessment, urban growth analysis, house value estimation, urban population estimation and urban social vulnerability assessment. The satellite remote sensing imagery used in these applications has medium, high or very high spatial resolution, such as images from Landsat MSS, Landsat TM and ETM+, SPOT, ASTER, IRS, Ikonos and QuickBird. Consistent relationships between socio-economic variables derived from censuses and field surveys and proxy variables of vegetation coverage measured from satellite remote sensing data have been found in several cities in the US. Different approaches and techniques have been applied successfully around the world, but local research is always needed to account for the unique elements of each place. Spectral mixture analysis, object-oriented classifications and image texture measures are some of the techniques of image processing that have been implemented with good results. Many regional scientists remain skeptical that satellite remote sensing will produce useful information for their work. More local research is needed to demonstrate the real potential and utility of satellite remote sensing for regional science in urban environments.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/3cff275c13b11d71d78e00ab47044baf235d6525
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In Atmospheric Science
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In Global and Planetary Change
Citations in the corpus
- An object-based convolutional neural network (OCNN) for urban land use classification (2018, background): “It also provides a key input to urban and transportation 40 models, and is essential to understanding the complex interactions between human activities 41 and environmental change (Patino and Duque, 2013).”
- Joint Deep Learning for land cover and land use classification (2019, background): “It also serves as the basis for understanding the constant changes on the surface of the Earth and associated socio-ecological interactions (Cassidy et al., 2010; Patino and Duque, 2013).”
- Slums from Space - 15 Years of Slum Mapping Using Remote Sensing (2016, background): “Remote sensing can play a key role in analyzing “space ́time dynamics” [30], such as monitoring densification and expansion processes or assisting in the implementation of slum improvement policies.”
- HeatPrompt: Zero-Shot Vision-Language Modeling of Urban Heat Demand from Satellite Images (2026, unknown): “Remote sensing has been used for urban classification, land cover analysis, and socio-economic inference [5], [12].”