Very-high-resolution (VHR) land cover and land use (LCLU) is an essential baseline data for understanding fine-scale interactions between humans and the heterogeneous landscapes of urban environments. In this study, we developed a Fine-resolution, Large-area Urban Thematic information Extraction (FLUTE) framework to address multiple challenges facing large-area, high-resolution urban mapping, including the view angle effect, high intraclass and low interclass variation, and multiscale land cover types. FLUTE builds upon a teacher-student deep learning architecture, and includes two new feature extraction modules – Scale-aware Parsing Module (SPM) and View-aware Embedding Module (VEM). Our model was trained with a new benchmark database containing 52.43 million labeled pixels (from 2014 to 2017 NAIP airborne Imagery) to capture diverse LCLU types and spatial patterns. We assessed the credibility of FLUTE by producing a 1-meter resolution database named UrbanWatch for 22 major cities across the conterminous United States. UrbanWatch contains nine LCLU classes – building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. We have further made UrbanWatch freely accessible to support urban-related research, urban planning and management, and community outreach efforts: https://urbanwatch.charlotte.edu . • We propose a model FLUTE for very-high-resolution land cover/use mapping. • FLUTE builds upon a teacher-student deep learning architecture. • We construct a new benchmark database containing 52.43 million labeled pixels. • We develop a 1-m resolution UrbanWatch land cover/use product for 22 U.S. cities. • UrbanWatch has 9 classes with an overall accuracy of 91.52%.