Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their applications. In this paper, we investigate a challenging yet practical scenario of BDD, Unsupervised Building Damage Detection (U-BDD), where only unlabelled pre- and post-disaster satellite image pairs are provided. As a pilot study, we have first proposed an advanced U-BDD baseline that leverages pre-trained vision-language foundation models to address the U-BDD task. However, the apparent domain gap between satellite and generic images causes low confidence in the foundation models used to identify buildings and their damages. In response, we further present a novel self-supervised framework, U-BDD++, which improves upon the U-BDD baseline by addressing domain-specific issues associated with satellite imagery. Extensive experiments on the widely used building damage assessment benchmark demonstrate the effectiveness of the proposed method for unsupervised building damage detection. The presented annotation-free and foundation model-based paradigm ensures an efficient learning phase. This study opens a new direction for real-world BDD and sets a strong baseline for future research.

Building Change Detection (BCD) is one of the most important parts of remote sensing analysis. However, most of the existing BCD approaches require a large amount of pixel-level annotation, which limits their applicability due to intensive labour costs. To alleviate this issue, we propose a vision-language model-based framework, VLM-BCD, which performs BCD tasks without requiring any labels. Specifically, the proposed framework consists of two stages: 1) Bi-temporal building localisation by leveraging open-vocabulary DETR. 2) Unchanged mask suppressing by the Change Resolver module to detect the building change in bi-temporal satellite images. An application with an interactive dashboard is implemented to maximise the usability of the developed framework.

Recent advancements of sequential deep learning models such as Transformer and BERT have significantly facilitated the sequential recommendation. However, according to our study, the distribution of item embeddings generated by these models tends to degenerate into an anisotropic shape, which may result in high semantic similarities among embeddings. In this paper, both empirical and theoretical investigations of this representation degeneration problem are first provided, based on which a novel recommender model DuoRec is proposed to improve the item embeddings distribution. Specifically, in light of the uniformity property of contrastive learning, a contrastive regularization is designed for DuoRec to reshape the distribution of sequence representations. Given the convention that the recommendation task is performed by measuring the similarity between sequence representations and item embeddings in the same space via dot product, the regularization can be implicitly applied to the item embedding distribution. Existing contrastive learning methods mainly rely on data level augmentation for user-item interaction sequences through item cropping, masking, or reordering and can hardly provide semantically consistent augmentation samples. In DuoRec, a model-level augmentation is proposed based on Dropout to enable better semantic preserving. Furthermore, a novel sampling strategy is developed, where sequences having the same target item are chosen hard positive samples. Extensive experiments conducted on five datasets demonstrate the superior performance of the proposed DuoRec model compared with baseline methods. Visualization results of the learned representations validate that DuoRec can largely alleviate the representation degeneration problem.

With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs.

Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performance. This paper proposes a dual-stream learning framework, dubbed Deep Collaborative Discrete Hashing (DCDH), which constructs a discriminative common discrete space by collaboratively incorporating the shared and individual semantics deduced from visual features and semantic labels. Specifically, the context-aware representations are generated by employing the outer product of visual embeddings and semantic encodings. Moreover, we reconstruct the labels and introduce the focal loss to take advantage of frequent and rare concepts. The common binary code space is built on the joint learning of the visual representations attended by language, the semantic-invariant structure construction and the label distribution correction. Extensive experiments demonstrate the superiority of our method.