Building Attributes Recognition with Noisy and Incomplete Labels

Recognizing building attributes from remote sensing images is crucial for various applications. Recent developments in deep learning have demonstrated promising results in identifying these attributes. Nonetheless, a major challenge is the requirement for extensive and accurate building attribute data. Two primary data sources are commonly considered: Open-StreetMap (OSM), which offers global building information but often lacks completeness and correctness, and cadastral data, known for its high quality but typically restricted to certain areas. These two sources enable comparison between deep learning models trained on noisy and incomplete OSM data and those trained on accurate and complete cadastral data. In this work, comprehensive experiments on buildings in Bavaria, Germany, are conducted, covering diverse attributes such as footprints, use, and height. A large building dataset with corresponding building attribute labels from OSM and cadastral data is created, with OSM data featuring varying levels of incompleteness and noise for different attributes and cadastral data serving as ground truth. Moreover, we evaluate the effectiveness of several prevailing methods designed to handle noisy and incomplete labels, assessing their applicability to real-world scenarios with incomplete and noisy OSM labels.