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Aerial scene recognition is a fundamental task in remote sensing and hasrecently received increased interest. While the visual information fromoverhead images with powerful models and efficient algorithms yieldsconsiderable performance on scene recognition, it still suffers from thevariation of ground objects, lighting conditions etc. Inspired by themulti-channel perception theory in cognition science, in this paper, forimproving the performance on the aerial scene recognition, we explore a novelaudiovisual aerial scene recognition task using both images and sounds asinput. Based on an observation that some specific sound events are more likelyto be heard at a given geographic location, we propose to exploit the knowledgefrom the sound events to improve the performance on the aerial scenerecognition. For this purpose, we have constructed a new dataset named AuDioVisual Aerial sceNe reCognition datasEt (ADVANCE). With the help of thisdataset, we evaluate three proposed approaches for transferring the sound eventknowledge to the aerial scene recognition task in a multimodal learningframework, and show the benefit of exploiting the audio information for theaerial scene recognition. The source code is publicly available forreproducibility purposes.

Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition