The accession numbers for sequencing data are SRP111763 and SRP111797.

Columbia(a) Harbor (b) Forest (c) Residence (d) Beach (e) Parking Lot Collected from Satellite Collected from UAV Collected from Land Ground View Aerial View Satellite View Figure 1: Examples of scenes from top-down views. From top to down are scenes from the satellite view, the aerial view, and the ground view. Scenes from the satellite view are with much lower resolution and clarity compared with the aerial view. Scenes from the ground view and the aerial view are with huge domain gap even with the consistent semantic labels.

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Most publicly available datasets for image classification are with single labels, while images are inherently multilabeled in our daily life. Such an annotation gap makes many pre-trained single-label classification models fail in practical scenarios. This annotation issue is more concerned for aerial images: Aerial data collected from sensors naturally cover a relatively large land area with multiple labels, while annotated aerial datasets, which are publicly available (e.g., UCM, AID), are single-labeled. As manually annotating multi-label aerial images would be time/labor-consuming, we propose a novel selfcorrection integrated domain adaptation (SCIDA) method for automatic multi-label learning. SCIDA is weakly supervised, i.e., automatically learning the multi-label image classification model from using massive, publicly available single-label images. To achieve this goal, we propose a novel Label-Wise self-Correction (LWC) module to better explore underlying label correlations. This module also makes the unsupervised domain adaptation (UDA) from single-to multi-label data possible. For model training, the proposed model only uses single-label information yet requires no prior knowledge of multi-labeled data; and it predicts labels for multi-label aerial images. In our experiments, trained with single-labeled MAI-AID-s and MAI-UCM-s datasets, the proposed model is tested directly on our collected Multi-scene Aerial Image (MAI) dataset. The code and data are available on GitHub(https://github.com/Ryan315/Single2multi-DA).