Automated building extraction from high-resolution satellite imagery is a challenging research problem, and several issues remain with respect to the variety of variables to be accounted for. In this paper we present an approach for building detection using multiple cues. We use the shadow, shape, and color features of buildings to propose our approach, known as Building Detection with Shadow Verification (BDSV). BDSV has three main pillars, which are: (1) tile building detection (TBD) to detect roof tile buildings; (2) flat building detection (FBD) to detect non-tile flat buildings according to shape features; and (3) results fusion used to fuse and aggregate results from previous blocks. Analyses performed over different study areas reveal high quality percentage and precision metrics, exceeding 95%. Performance analysis over the SztaKi-Inria and Istanbul datasets shows that BDSV outperforms benchmark algorithms.

Natural disaster and wars wreak havoc not only on individuals and critical infrastructure, but also leave behind ruined residential buildings and housings. The size, type and location of damaged houses are essential data sources for the post-disaster reconstruction process. Building damage detection due to war activities has not been thoroughly discussed in the literature. In this work, an automated building damage detection technique that relies on both pre-and post-war aerial images is proposed. Building damage estimation is done using shadow information and Gray Level Co-occurrence Matrix features. Accuracy assessment applied over a Syrian war-affected zone near Damascus, reveals the excellent performance of the proposed technique.

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