Multimodal information fusion for urban scene understanding

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A survey of data fusion in smart city applications (2019, background): “In addition, a large variety of data sources can be used for urban planning such as physical sensors [160], photography [76], [161], or hybrid data sources [85].”
SMSnet: Semantic motion segmentation using deep convolutional neural networks (2017, unknown): “As described in Section IV, the KITTI benchmark does not provide any official ground truth for semantic segmentation, therefore to train the semantic stream of our network, we combine the Cityscapes dataset with the KITTI semantic ground truth from [6] and [24] to obtain the most generalized trainin”