Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries. However, traditional sketch-based spatial search methods perform insufficiently due to their inability to find hidden multi-scale map features from mental sketches. In this research, we propose a deep convolutional neural network, namely Deep Spatial Scene Network (DeepSSN), to better assess the spatial scene similarity. In DeepSSN, a triplet loss function is designed as a comprehensive distance metric to support the similarity assessment. A positive and negative example mining strategy using qualitative constraint networks in spatial reasoning is designed to ensure a consistently increasing distinction of triplets during the training process. Moreover, we develop a prototype spatial scene search system using the proposed DeepSSN, in which the users input spatial query via sketch maps and the system can automatically augment the sketch training data. The proposed model is validated using multi-source conflated map data including 131,300 labeled scene samples after data augmentation. The empirical results demonstrate that the DeepSSN outperforms baseline methods including k-nearest-neighbors, multilayer perceptron, AlexNet, DenseNet, and ResNet using mean reciprocal rank and precision metrics. This research advances geographic information retrieval studies by introducing a novel deep learning method tailored to spatial scene queries.

Not yours or your friend's or one you saw in a home makeover show, but one purely from your imagination--perhaps your ideal living room. You should have no trouble doing it: We take this kind of imagination for granted. Rarely do we find ourselves wondering how the mind chooses what objects to put into these novel scenes and which ones to exclude. But it's worth reflecting on, perhaps especially for creative types, because our visual imagination appears to be constrained by regularities in visual memories. Diversifying what you see may mean enriching what you can imagine. In a recent study Irish neuroscientist Eleanor Maguire, of University College London, had people imagine novel scenes and compared this to people imagining single objects against a white background.