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This study uses high-resolution (HR) satellite imagery to quantify the stock of buildings, referred herein as building stock. The risk assessment requires information on the natural hazards and on the element at risk, that is the building stock in this article. This study combines (1) texture-based image processing to map built-up areas, (2) statistical sampling that allows locating the building samples and (3) photo-interpretation to encoding building footprints. Statistical inference is then used to quantify the building stock per class of building size. Legaspi in the Philippines is used as a case study. The results show that texture-based computer algorithms provide accurate area estimations of the built-up, that the detail of HR imagery allows the mapping of single buildings using photo-interpretation, and that a systematic sampling approach that uses building encoding and built-up maps can be used to quantify the building stock.

A procedure for the calculation of a "built-up presence index" is presented. The index is based on fuzzy rulebased composition of anisotropic textural measures derived from the satellite data by the gray-level co-occurrence matrix (GLCM). The case study includes multi-temporal analysis, wide area coverage (65x135 kilometers) with multiple datasets, scattered settlements, and presence of arid and semi-arid areas together with complex agricultural landscape in the background. The concept is validated comparing the output of the procedure with and exhaustive reference dataset covering 218.23 square kilometers.