Evaluation Of Segment Anything Model (SAM) For Automated Labelling in Machine Learning Classification of UAV Geospatial Data

Abstract With the present trend toward digitization in many areas of urban planning and development, accurate object classification is becoming increasingly vital. To develop machine learning models that can effectively classify the broader region, it is crucial to have accurately labeled datasets for object extraction. However, the process of generating sufficient labeled data for machine learning models remains challenging. A recently developed AI-assisted segmentation approach called Segment Anything Model (SAM) offers a solution to enhance the semi-automated labeling of complex and intricate image structures. By utilizing SAM, the accuracy and consistency of annotation results can be improved, while also significantly reducing the time required for annotation. This paper aims to assess the efficiency of SAM annotated labels for training machine learning models using high-resolution remote sensing data captured by UAVs (Unmanned Aerial Vehicles) in the peri-urban region of Anad, Kerala, India. A comparative analysis was conducted to evaluate the performance of training datasets generated using SAM and semi-automated labeling with existing tools. Multiple machine learning models, including Random Forest, Support Vector Machine, and XGBoost, were employed for this analysis. The results, as indicated by the classification accuracy of the machine learning models, demonstrate that the SAM-based segmentation approach is more efficient in generating semantic labels for accurately training the models.