Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. However, the limitations of the limited receptive field of CNN and the high-computational complexity of Transformer remain. Recently, the Mamba architecture, based on state-space models, has demonstrated strong global receptive field capabilities and implements linear time complexity in computational processes. While some researchers have incorporated it into CD tasks, most have neglected the effective application of the Mamba selective scanning algorithm for modeling bitemporal image dependencies, resulting in suboptimal feature learning from bitemporal images. In this article, we propose a CD Mamba model (CD-STMamba), which can efficiently encode and decode bitemporal images interactively from multiple dimensions, thus enabling more accurate CD. Specifically, we propose a spatio-temporal interaction module (STIM), which can interact with bitemporal image features in multiple dimensions and fit with the Mamba architecture, allowing it to fully learn the global contextual information of the bitemporal input image. We also introduce a decoding block called the CD block, which can be fully decoded to learn multiple spatio-temporal relationships based on the characteristics of STIM. This block employs multiple change visual state space blocks internally to decode different spatio-temporal interactions and utilizes the change attention module to capture change features comprehensively for more accurate CD. The proposed CD-STMamba achieved state-of-the-art intersection over union (IoU) on three datasets, Wuhan University Building Change Detection Dataset (91.29% ), Sun Yat-Sen University Change Detection (73.45% ), and Change Detection Dataset (95.56% ).

To maintain marine ecosystem health, effective algae monitoring is essential. Traditional threshold-based methods and standard machine learning techniques often fall short in accurately and automatically distinguishing algae types. This study presents Algae-Mamba, an advanced network for algae extraction that builds upon the Visual State Space model (VSS). The Algae-Mamba unified VSS model and the KolmogorovArnold Network (KAN) proposed the Kolmogorov-Arnold Visual State Space (KVSS) model. KVSS block combines VSS for comprehensive global feature extraction with a small-kernel convolution module to capture local spatial and channel-specific information, supporting multi-scale data processing and improving model generalization. The KVSS represents high-dimensional features using orthogonal polynomial combinations through Gram polynomials and leverages an attention mechanism to index interactions between target algae and their features, enabling the model to learn distinct characteristics of sargassum and ulva effectively and enhance extraction precision. To address the common misclassification between sargassum and ulva under limited spectral data, Algae-Mamba incorporates the Normalized Difference Water Index (NDWI) to enhance semantic richness. Further, the model addresses class imbalances by employing a hybrid Cross-Entropy and Lovasz-Softmax loss function, en- ´ suring balanced and robust training. Unlike other methods that depend on extensive spectral information, Algae-Mamba achieves precise differentiation of sargassum and ulva with just 4-band spectral imagery, offering a powerful tool for monitoring marine ecological security. Testing on the GF-1 algae dataset demonstrates that Algae-Mamba surpasses other deep learning approaches in accurately extracting sargassum and ulva.

Observing building changes in remote sensing images plays a crucial role in monitoring urban development and promoting sustainable urbanization. Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. Moreover, most existing methods only detect whether changes have occurred but ignore change types, such as new construction and demolition. To address these issues, we present a building change-type detection network (BCTDNet) based on the Segment Anything Model (SAM) to identify newly constructed and demolished buildings. We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. Furthermore, we develop a change-aware attribute decoder that integrates building semantics into the change detection process via an extraction decoding network. Subsequently, an attribute-aware strategy is adopted to explicitly generate distinct maps for newly constructed and demolished buildings, thereby establishing clear temporal relationships among different change types. To evaluate BCTDNet’s performance, we construct the JINAN-MCD dataset, which covers Jinan’s urban core area over a six-year period, capturing diverse change scenarios. Moreover, we adapt the WHU-CD dataset into WHU-MCD to include multiple types of changing. Experimental results on both datasets demonstrate the superiority of BCTDNet. On JINAN-MCD, BCTDNet achieves improvements of 12.64% in IoU and 11.95% in F1 compared to suboptimal methods. Similarly, on WHU-MCD, it outperforms second-best approaches by 2.71% in IoU and 1.62% in F1. BCTDNet’s effectiveness and robustness in complex urban scenarios highlight its potential for applications in land-use analysis and urban planning.