The extraction of buildings in aerial remote sensing applications is an important and challenging task. Most existing methods extract buildings based on local area attention, ignoring the loss of accuracy due to the global structure of the building. However, global structural features of buildings with strong coupling relationships in complex scenes are difficult to extract, such as the edges and bodies of buildings, leading to discontinuous results. Therefore, multiscale decoupled body and edge supervision network (MDBES-Net), which can consider both edge optimization and inner consistency, is proposed to solve these problems. MDBES-net consists of the body-mask-edge consistency constraint base network (BMECC), decoupling the body and edge aware module (DBEA), and the channel decoupled attention module (CDA). First, body-mask-edge consistency constraint supervision is established by body and edge labels to jointly improve the segmentation effect in the BMECC base network. Second, in the mutiscale DBEA module, building features are warped by a learnable flow field to make body parts more consistent and edges more detailed. Finally, the CDA module performs adaptive calibration of the recoupled feature map channel response to minimize external background noise interference. Experiments on the open Massachusetts building dataset, WHU Building Dataset show that the proposed MDBES-Net can accurately extract buildings in complex scenarios, enabling complete building segmentation with refined boundaries and improved internal consistency.

Recent advances in mamba-based architectures have demonstrated promising potential for hyperspectral image classification (HSIC), offering linear-complexity long-range dependency modeling. However, two critical challenges persist in adapting this paradigm to HSI analysis: the substantial requirement for annotated training samples, and insufficient capacity to interpret the intricate spatial-spectral features inherent in HSI data, particularly under few-shot learning scenarios. To address these limitations, we present SSupMamba, a novel self-supervised mamba framework tailored for HSIC. First, we propose a composite scanning mamba block (CSMB) that enables comprehensive global feature extraction through multi-directional selective scanning of HSI data cubes. Second, we develop a spatial-spectral masked mamba (SAEM) framework that employs randomized masking and reconstruction tasks to enhance local representation learning. Third, we establish a unified self-supervised architecture incorporating contrastive learning to maximize mutual information between multi-views while preserving intrinsic spatial-spectral characteristics. Experimental results on four public datasets demonstrate that the proposed method exhibits excellent feature extraction capabilities under few-shot conditions and outperforms several state-of-the-art HSIC methods. The code is available at: https://github.com/Winkness/SSupMamba.

Thermal discomfort body language has been shown to be a psychological representation of personnel’s particular thermal comfort. Individual thermal comfort differences are ignored in public building settings with random personnel flow. To solve this issue, we suggested a Bayesian group thermal dissatisfaction rate prediction model based on thermal discomfort body language expression and subsequently implemented intelligent indoor temperature and humidity control. The PMV-PPD model was utilized to represent the group’s overall thermal comfort and to create a prior distribution of thermal dissatisfaction rate. To acquire the dynamic distribution of temperature discomfort body language, data on thermal discomfort body language expression were collected in a real-world office setting experiment. Based on Bayesian theory, we used personalized thermal discomfort body language expressions to modify the group’s universal thermal comfort and realized the assessment of the thermal dissatisfaction rate by combining commonality and personalization. Finally, a deep reinforcement learning system was employed to achieve intelligent indoor temperature and humidity control. The results show that when commonality and personalized thermal comfort differences are combined, real-time prediction of thermal dissatisfaction rate has high prediction accuracy and good model performance, and the prediction model provides a reference basis for reasonable indoor temperature and humidity settings.

For fine-grained recognition, capturing distinguishable features and effectively utilizing local information play a key role, since the objects of recognition exhibit subtle differences in different subcategories. Finding subtle differences between subclasses is not straightforward. To address this problem, we propose a weakly supervised fine-grained classification network model with Local Diversity Guidance (LDGNet). We designed a Multi-Attention Semantic Fusion Module (MASF) to build multi-layer attention maps and channel–spatial interaction, which can effectively enhance the semantic representation of the attention maps. We also introduce a random selection strategy (RSS) that forces the network to learn more comprehensive and detailed information and more local features from the attention map by designing three feature extraction operations. Finally, both the attention map obtained by RSS and the feature map are employed for prediction through a fully connected layer. At the same time, a dataset of ancient towers is established, and our method is applied to ancient building recognition for practical applications of fine-grained image classification tasks in natural scenes. Extensive experiments conducted on four fine-grained datasets and explainable visualization demonstrate that the LDGNet can effectively enhance discriminative region localization and detailed feature acquisition for fine-grained objects, achieving competitive performance over other state-of-the-art algorithms.