When the buildings themselves or the environments they are located in change, it poses a cross-domain problem for building extraction. Since the semantics of the same category should be consistent across domains, a memory mechanism can be used to drive models to capture it. A key prerequisite for a satisfactory memory mechanism is that the memory content is highly task-relevant, i.e., the memory mechanism represents domain-invariant semantic features of buildings as much as possible. It is worth noting that images from different domains have diverse styles, which can interfere with the representation of domain-invariant semantic features of buildings. To maximize the effectiveness of memory mechanisms in cross-domain building extraction tasks, it is necessary to reduce the interference of style information. Therefore, we propose a cross-domain extraction method for buildings based on decoupling style-semantic features. It expresses the image style features and building semantic features separately and optimizes the domain-invariant features of buildings by constraining their relationship through an orthogonal loss. Specifically, on the one hand, style features within the same domain as well as semantic features within and across domains are narrowed down through contrastive learning to ensure the consistency of style features extracted within the same domain and the consistency of semantics across domains. On the other hand, the style features and domain-invariant semantic features in the memory mechanism can be stored and reused to fully exploit self-supervised information. Results of the cross-domain experiments show that the proposed method can achieve optimal building extraction.

A high-quality built environment is important for human health and well-being. Assessing the quality of the urban built environment can provide planners and managers with decision-making for urban renewal to improve resident satisfaction. Many studies evaluate the built environment from the perspective of street scenes, but it is difficult for street-view data to cover every area of the built environment and its update frequency is low, which cannot meet the requirement of built-environment assessment under rapid urban development. Earth-observation data have the advantages of wide coverage, high update frequency, and good availability. This paper proposes an intelligent evaluation method for urban built environments based on scene understanding of high-resolution remote-sensing images. It contributes not only the assessment criteria for the built environment in remote-sensing images from the perspective of visual cognition but also an image-caption dataset applicable to urban-built-environment assessment. The results show that the proposed deep-learning-driven method can provide a feasible paradigm for representing high-resolution remote-sensing image scenes and large-scale urban-built-area assessment.

Semantic segmentation of high-resolution remote sensing imagery (HRSI) suffers from the domain shift, resulting in poor performance of the model in another unseen domain. Unsupervised domain adaptive (UDA) semantic segmentation aims to adapt the semantic segmentation model trained on the labeled source domain to an unlabeled target domain. However, the existing UDA semantic segmentation models tend to align pixels or features based on statistical information related to labels in source and target domain data, and make predictions accordingly, which leads to uncertainty and fragility of prediction results. In this paper, we propose a causal prototype-inspired contrast adaptation (CPCA) method to explore the invariant causal mechanisms between different HRSIs domains and their semantic labels. It firstly disentangles causal features and bias features from the source and target domain images through a causal feature disentanglement module. Then, a causal prototypical contrast module is used to learn domain invariant causal features. To further de-correlate causal and bias features, a causal intervention module is introduced to intervene on the bias features to generate counterfactual unbiased samples. By forcing the causal features to meet the principles of separability, invariance and intervention, CPCA can simulate the causal factors of source and target domains, and make decisions on the target domain based on the causal features, which can observe improved generalization ability. Extensive experiments under three cross-domain tasks indicate that CPCA is remarkably superior to the state-of-the-art methods.

Building extraction using deep learning techniques has advantages but relies on a large number of clean labeled samples to train the model. Complex appearance and tilt shots often cause many offsets between building labels and true locations, and these noises have a considerable impact on building extraction. This paper proposes a new knowledge distillation-based building extraction method to reduce the impact of noise on the model and maintain the generalization of the model. The method can maximize the generalizable knowledge of large-scale noisy samples and the accurate supervision of small-scale clean samples. The proposed method comprises two similar teacher and student networks, where the teacher network is trained by large-scale noisy samples and the student network is trained by small-scale clean samples and guided by the knowledge of the teacher network. Experimental results show that the student network can not only alleviate the influence of noise labels but also obtain the capability of building extraction without incorrect labels in the teacher network and improve the performance of building extraction.

China’s urbanization has dramatically accelerated in recent decades. Land for urban build-up has changed not only in large cities but also in small counties. Land cover mapping is one of the fundamental tasks in the field of remote sensing and has received great attention. However, most current mapping requires a significant manual effort for labeling or classification. It is of great practical value to use the existing low-resolution label data for the classification of higher resolution images. In this regard, this work proposes a method based on noise-label learning for fine-grained mapping of urban build-up land in a county in central China. Specifically, this work produces a build-up land map with a resolution of 10 m based on a land cover map with a resolution of 30 m. Experimental results show that the accuracy of the results is improved by 5.5% compared with that of the baseline method. This notion indicates that the time required to produce a fine land cover map can be significantly reduced using existing coarse-grained data.