摘要
A key component in constructing a broad-scale,gridded population dataset is fine resolution geospatial data accurately depicting the extent of human activity.Analogous datasets are often developed using a wide range of methods and classification techniques,including the use of spatial features,spectral features,or the coupling of both to identify the presence of man-made structures from high-resolution satellite imagery.By using spatial and textural-based descriptors to generate highresolution settlement layers for two dissimilar regions at the peak of seasonal disparity,this study attempts to quantify the influence of seasonality on the accuracy of a supervised,multi-scale,feature extraction framework for automated delineation of human settlement.Results generated by numerous models are evaluated against a reference dataset allowing for assessment of seasonal and feature differences in the context of accuracy.Global or regional mapping of human settlement requires the assemblage of high-resolution satellite images with variegated acquisition characteristics(season,sun elevation,off-nadir,etc.)to produce a cloud-free composite image from which features are extracted.Results of this study suggest an emphasis on imagery criteria,in particular acquisition date,could improve classification accuracy when mapping human settlement at scale.
