Coastal regions worldwide are facing increasing threats from climate change-driven sea level rise and more intense storms. Monitoring and managing coastal infrastructure like seawalls is crucial for protecting vulnerable communities. This study presents a framework to accurately and efficiently detect seawalls in low-lying coastal areas using readily available aerial imagery. The proposed approach was evaluated in Hallandale Beach City, Broward County, Florida. Aerial images from the Florida Department of Transportation were processed using object-based image analysis (OBIA) techniques. Two main classification methods were compared: pixel-based and object-based. Within the OBIA framework, three techniques were tested: machine learning (ML) alone, knowledge-based (KB) rules alone, and a combined ML+KB approach. In this session, learn which approaches were most effective for accurately identifying coastal infrastructure like seawalls. Additionally, future work will explore applying this methodology to other coastal regions and infrastructure types to build resilience.