Rapid and accurate detection of landslides after extreme climate events, such as heavy rainfalls or hurricanes, is essential for hazard response and mitigation. Traditional mapping methods rely on manual interpretation or labelled datasets, limiting scalability. This paper presents an integrated workflow combining unsupervised autoencoder-based + KMeans change detection and deep learning semantic segmentation to improve landslide identification in Western North Carolina following Hurricane Helene (September 2024). The approach leverages Planetscope RGB-NIR imagery at 3 m spatial resolution and North Carolina Department of Environmental Quality post-event landslide inventory points. The unsupervised autoencoder extracts latent features and highlights change zones, while segmentation models such as UNet learn spatial–contextual patterns from semi-automated labels. Results demonstrate high detection accuracy with segmentation models achieving strong overlap with ground-truth inventories and minimal false positives with an F1-score of 92%. This hybrid pipeline bridges rapid unsupervised detection and precise pixel-level segmentation, enabling scalable, near-real-time landslide mapping.