As individuals spend most of their time indoors, determining whether a mobile device is located indoors or outdoors – and identifying the specific building it is in – is essential for enabling building-level location-based services and fine-grained human activity analysis. However, existing indoor positioning techniques often rely on dedicated infrastructure or dense signal fingerprinting, limiting their scalability across diverse urban environments. To address this, we propose a lightweight, data-driven framework for building-level mobile device location recognition that integrates indoor/outdoor (I/O) classification and building matching using limited sensor data. A random forest model is trained on a structured, scene-diverse sample library to classify I/O status based on multi-sensor features. For devices identified as indoors, building identification is performed using a Bayesian inference model that incorporates prior knowledge derived from anonymous crowdsourced data, leveraging spatial heterogeneity in sensor feature distributions across candidate buildings. Experiments conducted in three Chinese cities demonstrated that I/O classification achieved over 90% accuracy, and building matching based on crowdsourced data achieved at least 70% precision using only satellite or Wi-Fi features. Our approach requires no infrastructure deployment or extensive labeled data, offering a scalable and practical solution for building-level location inference across large and heterogeneous regions.