Automatic extraction of hand-drawn annotations from participatory sketch maps is essential for digitising community-generated spatial information but remains challenging due to heterogeneous drawing styles, scanning artefacts, and complex basemap content. Existing approaches typically treat markup extraction as pixel-level segmentation or simple image differencing, which struggles under real-world variability. To address this, we formulate annotation extraction as an object-level task using a YOLO-based detector applied to RGB images of annotated maps. In addition, change detection is performed using paired RGB images of annotated and clean maps to isolate user-drawn content from the underlying basemap. Experiments on ∼2,300 real sketch maps and ∼18,000 synthetic samples show strong performance across diverse conditions. Object detection on annotated maps alone achieves mAP@50 of 91.5% on satellite imagery and 97.3% on OSM basemaps, while incorporating paired clean maps for change detection improves performance to 97.4% and 98.1%, respectively. Synthetic pretraining further enhances results on real hand-drawn data, indicating that simulated annotations effectively supplement limited labelled samples.