Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.

Extracting representative images of tourist attractions from geotagged photos is beneficial to many fields in tourist management, such as applications in touristic information systems. This task usually begins with clustering to extract tourist attractions from raw coordinates in geotagged photos. However, most existing cluster methods are limited in the accuracy and granularity of the places of interest, as well as in detecting distinct tags, due to its primary consideration of spatial relationships. After clustering, the challenge still exists for the task of extracting representative images within the geotagged base image data, because of the existence of noisy photos occupied by a large area proportion of humans and unrelated objects. In this paper, we propose a framework containing an improved cluster method and multiple neural network models to extract representative images of tourist attractions. We first propose a novel time- and user-constrained density-joinable cluster method (TU-DJ-Cluster), specific to photos with similar geotags to detect place-relevant tags. Then we merge and extend the clusters according to the similarity between pairs of tag embeddings, as trained from Word2Vec. Based on the clustering result, we filter noise images with Multilayer Perceptron and a single-shot multibox detector model, and further select representative images with the deep ranking model. We select Beijing as the study area. The quantitative and qualitative analysis, as well as the questionnaire results obtained from real-life tourists, demonstrate the effectiveness of this framework.

Accurate crop mapping is crucial for ensuring food security. Recently, many studies have developed diverse crop mapping models based on deep learning. However, these models generally rely on a large amount of labeled crop samples to investigate the intricate relationship between the crop types of the samples and the corresponding remote sensing features. Moreover, their efficacy is often compromised when applied to other areas owing to the disparities between source and target data. To address this issue, a new multi-modal deep adaptation crop classification network (MDACCN) was proposed in this study. Specifically, MDACCN synergistically exploits time series optical and SAR images using a middle fusion strategy to achieve good classification capacity. Additionally, local maximum mean discrepancy (LMMD) is embedded into the model to measure and decrease domain discrepancies between source and target domains. As a result, a well-trained model in a source domain can still maintain satisfactory accuracy when applied to a target domain. In the training process, MDACCN incorporates the labeled samples from a source domain and unlabeled samples from a target domain. When it comes to the inference process, only unlabeled samples of the target domain are required. To assess the validity of the proposed model, Arkansas State in the United States was chosen as the source domain, and Heilongjiang Province in China was selected as the target domain. Supervised deep learning and traditional machine learning models were chosen as comparison models. The results indicated that the MDACCN achieved inspiring performance in the target domain, surpassing other models with overall accuracy, Kappa, and a macro-averaged F1 score of 0.878, 0.810, and 0.746, respectively. In addition, the crop-type maps produced by the MDACCN exhibited greater consistency with the reference maps. Moreover, the integration of optical and SAR features exhibited a substantial improvement of the model in the target domain compared with using single-modal features. This study indicated the considerable potential of combining multi-modal remote sensing data and an unsupervised domain adaptive approach to provide reliable crop distribution information in areas where labeled samples are missing.