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Visual question answering (VQA) is an important and challenging multimodaltask in computer vision. Recently, a few efforts have been made to bring VQAtask to aerial images, due to its potential real-world applications in disastermonitoring, urban planning, and digital earth product generation. However, notonly the huge variation in the appearance, scale and orientation of theconcepts in aerial images, but also the scarcity of the well-annotated datasetsrestricts the development of VQA in this domain. In this paper, we introduce anew dataset, HRVQA, which provides collected 53512 aerial images of 1024*1024pixels and semi-automatically generated 1070240 QA pairs. To benchmark theunderstanding capability of VQA models for aerial images, we evaluate therelevant methods on HRVQA. Moreover, we propose a novel model, GFTransformer,with gated attention modules and a mutual fusion module. The experiments showthat the proposed dataset is quite challenging, especially the specificattribute related questions. Our method achieves superior performance incomparison to the previous state-of-the-art approaches. The dataset and thesource code will be released at https://hrvqa.nl/.

HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images