ASAI: A general and training-free artificial surfaces anomaly index using post-disaster single-temporal and high-resolution imagery

• Accurate mapping artificial surface areas without prior knowledge. • An efficient index method for extracting anomaly artificial surfaces using texture features. • The ASAI method shows superior performance without necessitating training samples. • The ASAI method can be applied in various disaster scenarios. Efficient detection of anomalous artificial surfaces is vital after disasters to support rapid emergency response. This study proposes a novel, training-free texture index method to accurately identify damaged artificial surfaces using post-disaster imagery. The method was tested across diverse disaster sites, including earthquake-affected areas in Turkey (Sites A–C), and tsunami- and tornado-damaged regions in Palu and Joplin (Sites D–I), covering a range of surface types and building structures. Sites A–C were chosen to develop and assess the efficacy of the proposed texture index method. Primarily, the three-dimensional texture features (3DTF) comprised of Contrast, Gabor wavelets, and secondary texture extraction (Con_Gabor), were amalgamated with a K-means classifier to delineate post-disaster artificial surface areas without prior knowledge. Given the discernible texture discrepancies between normal and anomalous artificial surfaces post-disaster, Homogeneity and Entropy texture features derived from Worldview-3 images at each site were leveraged to construct the artificial surface anomaly index (ASAI) for automatically extracting anomalous artificial surfaces. The findings demonstrated high overall accuracies of detecting anomaly artificial surfaces, ranging from 90.07 % to 91.76 % in Sites A–C. Notably, the ASAI outperformed Artificial Neural Network (ANN), Random Forest (RF), and the U-Net model. The overall accuracy (OA) of the ASAI method is 10 %–20 % higher than that of the U-Net model, showing superior automatic performance without necessitating training samples. Furthermore, the effectiveness of ASAI was validated in the Palu and Joplin sites, affirming its utility across diverse disaster scenarios. The damages of steel-tiled houses, middle-level houses and roads were effectively identified as anomaly artificial surfaces. The overall accuracies of the ASAI for identifying anomaly artificial surfaces were 89.55 %–92.5 %. These findings indicate the identification anomaly artificial surface using the ASAI was robust in different types of anomaly artificial surface caused by different disaster. The method of using ASAI to automatically identify anomaly artificial surfaces in post-disaster and single-temporal images has the potential for wide applicability.