In this work, we propose an approach to aid in mapping small settlements, which are often misclassified by models trained on a large-scale context (global or regional). We leverage pre-trained land cover models and few-shot learning to enhance the detection of these settlements. The backbone models are trained globally, but their application is localized through a spatial sampling strategy to address the challenge of detecting missed or unlabelled settlements. The proposed sampling strategy is based on the distance around a test patch and allows for the sampling of both backgrounds (non-settlements) points and settlements. Following this strategy results in a balanced dataset for model fine-tuning and ensures that the model is well-adapted to the local context. The idea is that nearby settlements share more similar properties, which is leveraged in our approach. We evaluate these transferred models by measuring the number of previously unmapped settlements detected by the fine-tuned classifier. For this, we manually annotated over two thousand buildings across two regions of Tanzania, previously unmapped in the original urban landcover product. Our results indicate the potential of the sampling approach, particularly when combined with a model pretrained with Momentum Contrast (MoCo). However, we also highlight the limitations in terms of spatial resolution of Sentinel-2 data for the detection of small settlements.