Abstract
To the best of our knowledge, the joint use of Wi-Fi login records, OpenStreetMap (OSM), and remote sensing imagery for object-oriented campus land use and land cover (LULC) mapping under multimodal small-data conditions has not been previously reported. This paper proposes the first adaptive prototype last ensemble learning (FAPLEL) framework with a coarse-to-fine architecture for object-oriented campus LULC mapping using multimodal small-data. For land cover (LC) mapping, we propose an adaptive prototype zero-shot learning (APZL) method based on a lightweight foundation model. It offers label-free and training-free inference, adaptive adjustment of the number of subprototypes, and providing knowledge graph-like interpretability. For land use (LU) mapping, we propose a spatiotemporal clustering ensemble learning (SCEL) method. This method requires only a single label per cluster, achieving near-full automation and promising accuracy under small-data conditions. A campus case study shows that APZL achieved an mIoU accuracy of 0.796 for coarse-grained LC mapping. Subsequently, the fine LU mapping process, which uses K-means followed by random forest, yields the highest overall accuracy of 0.813 among various supervised and unsupervised classification methods. These results indicate that the proposed multimodal data-driven FAPLEL paradigm has the potential to be extended to urban LULC mapping.