Spatial crowdsourcing (SC) platforms rely on predictive models to allocate tasks to mobile workers. However, real-world dynamics such as traffic, weather, and user behaviour cause frequent data and concept drift, which degrade model performance. Traditional drift detection methods are noise-sensitive, spatially agnostic, and computationally expensive. We present DriftSense, a hybrid incremental learning approach for real-time drift detection and adaptation in SC systems. DriftSense introduces three innovations: (i) spatially localised entropy-based drift detection, (ii) model-aware ADWIN (MA-ADWIN) that incorporates internal signals from Adaptive Hoeffding Trees (AHTs), and (iii) a false-signal filtering mechanism for robust adaptation. Experiments on real-world NYC Taxi and Yelp datasets, with injected abrupt, gradual, and mixed drifts, show that DriftSense achieves up to 25% higher detection accuracy, reduces false alarms by over 8–15% points, and lowers computational overhead by 20–25% compared to baselines. These results demonstrate that DriftSense is both effective and lightweight, making it suitable for deployment in dynamic SC platforms.