High-resolution remote sensing satellites have revolutionized remote sensing research, yet accurately segmenting specific targets from complex satellite imagery remains challenging. While the Segment Anything Model (SAM) has emerged as a promising universal segmentation model, its direct application to remote sensing imagery yields suboptimal results. To address these limitations, we propose RSAM-Seg, a novel deep learning model adapted from SAM specifically designed for remote sensing applications. Our model incorporates two key components: Adapter-Scale and Adapter-Feature modules. The Adapter-Scale modules, integrated within Vision Transformer (ViT) blocks, enhance model adaptability through learnable transformations, while the Adapter-Feature modules, positioned between ViT blocks, generate image-informed prompts by incorporating task-specific information. Extensive experiments across four binary and two multi-class segmentation scenarios demonstrate the superior performance of RSAM-Seg, achieving an $F1$ score of 0.815 in cloud detection, 0.834 in building segmentation, and 0.755 in road extraction, consistently outperforming established architectures like U-Net, DeepLabV3+, and Segformer. Moreover, RSAM-Seg shows significant improvements of up to 56.5% in $F1$ score compared to the original SAM. In addition, RSAM-Seg maintains robust performance in few-shot learning scenarios, achieving an $F1$ score of 0.656 with only 1% of the training data and increasing to 0.815 with full data availability. Furthermore, RSAM-Seg exhibits the capability to detect missing areas within the ground truth of certain datasets, highlighting its capability for completion.

Clouds in remote sensing images inevitably affect information extraction, which hinders the following analysis of satellite images. Hence, cloud detection is a necessary preprocessing procedure. However, most existing methods have numerous calculations and parameters. In this paper, a lightweight CNN-Transformer network, CD-CTFM, is proposed to solve the problem, which is based on encoder-decoder architecture and incorporates the attention mechanism. In the encoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extracting local and global features simultaneously. The backbone of CD-CTFM also incorporates attention gate based on dark channel extraction module. Moreover, a lightweight feature pyramid module is designed to fuse multiscale features with contextual information. In the decoder part, a lightweight channel-spatial attention module is integrated into each skip connection between encoder and decoder to extract low-level features while suppressing irrelevant information without introducing many parameters. Finally, the proposed model is evaluated on two cloud datasets, 38-Cloud and MODIS. The results demonstrate that CD-CTFM achieves comparable accuracy as the state-of-art methods and outperforms in terms of efficiency.

Clouds in satellite images limit the ability of imagery to extract the ground information, which makes it difficult to the following image analysis tasks. Hence, cloud detection is a changeling but fundamental task in the preprocessing of satellite image processing. In this letter, an encoder–decoder neural network architecture, cloud detection with ACON and attention mechanism (CAA)-UNet, is proposed for cloud detection. CAA-UNet is based on U-Net architecture and incorporates the attention mechanism. The asymmetric encoder and decoder blocks are proposed to discover more discriminative features. Then, a modified attention gate is integrated into each skip connection to highlight salient features. These make our model distinguish between the cloud and noncloud more accurately. Furthermore, the recent new and effective activate function, ActivateOrNot (ACON), is introduced into our model, which allows each neuron to adaptively activate or not, and improves the performance remarkably. Finally, the experiment results on two cloud datasets, Landsat-8 and a high-resolution cloud (HRC) cover validation dataset, show that CAA-UNet outperforms the state-of-the-art methods, especially in comprehensive indicators: Jaccard index, $F_{1}$ score, and overall accuracy.

Accurate and stable dynamic target positioning can provide high-precision vehicle motion state information for the autonomous driving decision-making system, which plays a very important role in the development of autonomous driving technology. In order to further improve the accuracy and stability of dynamic target positioning, based on the combination of Global Positioning System (GPS) and Ultra Wide Band (UWB) ranging information, the Kalman filter (KF) algorithm is used to process the measurement data of dynamic target. Since it is difficult to determine the system model noise and measurement noise under dynamic conditions, this paper introduces adaptive factor and dynamic window adjustment based on the covariance matching method, then proposes an adaptive Kalman filter (AKF) combination positioning method integrating UWB and GPS. In this method, the GPS single point positioning based on the sliding window AKF algorithm of covariance matching is used when the target is in the linear motion state. Under the condition of nonlinear motion of the target, the UWB/GPS integrated location is on account of the adaptive unscented Kalman filter (AUKF) algorithm which imports the filter divergence criterion. The simulation results confirm that the combined positioning method can adapt to the dynamic positioning requirements in complex environment and outperforms the traditional KF. The developed filtering algorithm has high adaptability, strong robustness, and can effectively improve the positioning accuracy

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological variables and metallogenic characteristics, which usually requires a large amount of data for training. However, there are some problems in the actual research, such as fewer geological sample data and irregular mining features, which affect the accuracy and reliability of intelligent prospecting prediction. Taking the Pangxidong study area in Guangdong Province as an example, this paper proposes a deep learning framework (SKT) for prospecting target prediction based on selective knowledge transfer and carries out intelligent prospecting target prediction research based on geochemical data in Pangxidong. The irregular features of different scales in the mining area are captured by dilation convolution, and the weight parameters of the source network are selectively transferred to different target networks for training, so as to increase the generalization performance of the model. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved, which can effectively alleviate the problems of a small number of geological samples and irregular features of mining areas in prospecting prediction.