Advanced text generation is paramount for enhancing the naturalness of human-computer interaction and improving emotional expressiveness. Current mainstream methods largely rely on large language models (LLMs) for single-turn generation, often lacking the interactivity and multi-dimensional feedback mechanisms inherent in human writing. This limitation frequently results in generated texts that fall short in terms of depth, fluency, and stylistic sophistication. To address these deficiencies, this paper proposes WRitEer (Writer-Reader iterative tuning with Editor-Driven evolution and refinement), an interactive multi-agent collaborative human-like writing framework. Centered around an LLM, this framework integrates multi-objective optimization with preference fine-tuning techniques. It introduces three synergistic agents: the Reader, responsible for discourse analysis and indicator generation; the Editor, which constructs prompts based on feedback indicators and iteratively refines them through an evolutionary search; and the Writer, which generates text based on these refined prompts and continuously self-optimizes via a DPO mechanism that incorporates preference feedback. Experimental results consistently demonstrate that this ``generate-evaluate-reflect-optimize'' workflow significantly outperforms single LLM models across multiple datasets, yielding advanced rich texts that exhibit superior human-like style, coherence, expressiveness, and controllability.

Fracture detection plays a critical role in medical imaging analysis, traditional fracture diagnosis relies on visual assessment by experienced physicians, however the speed and accuracy of this approach are constrained by the expertise. With the rapid advancements in artificial intelligence, deep learning models based on the YOLO framework have been widely employed for fracture detection, demonstrating significant potential in improving diagnostic efficiency and accuracy. This study proposes an improved YOLO-based model, termed Fracture-YOLO, which integrates novel Critical-Region-Selector Attention (CRSelector) and Scale-Aware (ScA) heads to further enhance detection performance. Specifically, the CRSelector module utilizes global texture information to focus on critical features of fracture regions. Meanwhile, the ScA module dynamically adjusts the weights of features at different scales, enhancing the model’s capacity to identify fracture targets at multiple scales. Experimental results demonstrate that, compared to the baseline model, Fracture-YOLO achieves a significant improvement in detection precision, with mAP50and mAP50−95increasing by 4 and 3, surpassing the baseline model and achieving state-of-the-art (SOTA) performance.

Relic landslide, formed over a long period, possess the potential for reactivation, making them a hazardous geological phenomenon. While reliable relic landslide detection benefits the effective monitoring and prevention of landslide disaster, semantic segmentation using high-resolution remote sensing images for relic landslides faces many challenges, including the object visual blur problem, due to the changes of appearance caused by prolonged natural evolution and human activities, and the small-sized dataset problem, due to difficulty in recognizing and labelling the samples. To address these challenges, a semantic segmentation model, termed mask-recovering and interactivefeature- enhancing (MRIFE), is proposed for more efficient feature extraction and separation. Specifically, to address the visual blur problem, a contrastive learning and mask reconstruction approach is designed under the guidance of remote sensing visual interpretation expert knowledge, which states the height variation at the landslide boundary contributing the most to landslide identification. This approach constructs local patches from the landslide boundary and background to perform supervised contrastive learning and applies mask reconstruction to local patches, guiding the model to focus on the landslide boundary and to extract the most contributive local salient features for reliable recognition. Meanwhile, to address the smallsized dataset problem, a self-distillation learning method is introduced, which uses a momentum encoder to update the teacher network with the average of the student network, suppressing overfitting caused by background interference. By constructing contrastive input pairs, the approach increases the diversity and combinations within the contrastive sample space and improves sample utilization. The proposed MRIFE is evaluated on a real relic landslide dataset, and experimental results show that it greatly improves the performance of relic landslide detection. For the semantic segmentation task, compared to the baseline, the precision increases from 0.4226 to 0.5347, the mean intersection over union (IoU) increases from 0.6405 to 0.6680, the landslide IoU increases from 0.3381 to 0.3934, and the F1-score increases from 0.5054 to 0.5646.

Rainfall is the primary landslide triggering factor in China, and the spatial-temporal hazard prediction of rainfall-induced landslides is of great practical significance. Currently, most countries and regions establish landslide hazard prediction systems based on rainfall data only, resulting in low spatial precision of hazard prediction results and a high false alarm rate. This paper proposes a hazard prediction model that considers landslide triggering factors, landslide predisposing environment, and the spatial regularity of historical landslides based on multi-modal earth observation data. The proposed model has significantly improved the spatial-temporal hazard prediction performance of rainfall-induced natural terrain landslides in Hong Kong.

Ice roads serve as vital land transportation during the Arctic winter season. In the context of polar increased warming, there are great uncertainties for human activities on ice roads. In this paper, we integrate remote sensing techniques to quantify the potential ice road evolution in the Pan-Arctic and its impact on land accessibility from 1979 to 2017. We show that the potential ice roads have significantly decreased, with the fastest decrease in March to 2.34×104km2yr−1. Furthermore, the contribution of potential ice roads to port accessibility is most severely reduced in the Canadian Arctic, reaching 0.93 h yr−1. The results demonstrate that warmer winters are imposing severe stress on Arctic land access.

In the identification of geological hazards in a wide area, the rapid extraction of deformation areas in the InSAR phase becomes an important part of whether the hazards can be quickly and accurately identified. In practical applications, there is a significant difference in the size of deformation regions over a wide area. Therefore, this article uses the Bilateral Segmentation Network (Bisenet), which calculates in parallel through two branches. We first utilize a small step spatial path to preserve spatial information and generate high-resolution features. Meanwhile, a context path with a fast downsampling strategy is employed to obtain sufficient receptive fields. On top of these two paths, we utilize a new feature fusion module to effectively combine features. This greatly improves the extraction speed while preserving multilayer feature fusion and preserving information extraction results at different scales. A suitable balance has been achieved between speed and segmentation performance, which well meets the current work of identifying geological hazards in wide areas.

Slow-moving landslide detection is of vital importance in preventing and mitigating geohazards. Extracting abstract features from remote sensing images is crucial for achieving high-precision detection of slow-moving landslides. This study utilizes both activity features and terrain structure features for geohazard detection. We propose a pixel-level and a feature-level image fusion network, and investigate the multi-level fusion cooperative mechanism. We evaluate the performance of the two-level fusion and single-modal data base on the test data. The experimental results demonstrate that fusion of the activity characteristics and topographic characteristics can enhance the accuracy of identifying slow-moving landslides. Feature-level fusion outperforms pixel-level fusion for slow-moving landslides identification.

Recent advancements in Vison Foundation Models (VFMs) like the Segment Anything Model (SAM) have exhibited remarkable progress in natural image segmentation. However, its performance on remote sensing images is limited, especially in some application scenarios that require strong expert knowledge involvement, such as landslide detection. In this study, we proposed an effective segmentation model, namely LandslideNet, which is realized by embedding a tuning layer in a pre-trained encoder and adapting the SAM to the landslide detection scene for the first time. The proposed method is compared with traditional convolutional neural networks (CNN) on two well-known landslide datasets. The results indicate that the proposed model with fewer training parameters has better performance in detecting small-scale targets and delineating landslide boundaries, with an improvement of 6-7 percentage points in accuracy (F1 and mIoU) compared to mainstream CNN-based methods.

Landslide is one of the most dangerous and frequently occurred natural disasters. The semantic segmentation technique is efficient for wide area landslide identification from high-resolution remote sensing images (HRSIs). However, considerable challenges exist because the effects of sediments, vegetation, and human activities over long periods of time make visually blurred old landslides very challenging to detect based upon HRSIs. Moreover, for terrain features like slopes, aspect and altitude variations cannot be sufficiently extracted from 2-D HRSIs but can be from digital elevation model (DEM) data. Then, a feature-fusion based semantic segmentation network (FFS-Net) is proposed, which can extract texture and shape features from 2-D HRSIs and terrain features from DEM data before fusing these two distinct types of features in a higher feature layer. To segment landslides from background, a multiscale channel attention module is purposely designed to balance the low-level fine information and high-level semantic features. In the decoder, transposed convolution layer replaces original mathematical bilinear interpolation to better restore image resolution via learnable convolutional kernels, and both dropout and batch normalization (BN) are introduced to prevent over-fitting and accelerate the network convergence. Experimental results are presented to validate that the proposed FFS-Net can greatly improve the segmentation accuracy of visually blurred old landslides. Compared to U-Net and DeepLabV3+, FFS-Net can improve the mean intersection over union (mIoU) metric from 0.508 and 0.624 to 0.67, the F1 metric from 0.254 and 0.516 to 0.596, and the pixel accuracy (PA) metric from 0.874 and 0.906 to 0.92, respectively. For the detection of visually distinct landslides, FFS-NET also offers comparable detection performance, and the segmentation is improved for visually distinct landslides with similar color and texture to surroundings.

The geological characteristics of old landslides can provide crucial information for the task of landslide protection. However, detecting old landslides from high-resolution remote sensing images (HRSIs) is of great challenges due to their partially or strongly transformed morphology over a long time and thus the limited difference with their surroundings. Additionally, small-sized datasets can restrict in-depth learning. To address these challenges, this paper proposes a new iterative classification and semantic segmentation network (ICSSN), which can significantly improve both object-level and pixel-level classification performance by iteratively upgrading the feature extraction module shared by the object classification and semantic segmentation networks. To improve the detection performance on small-sized datasets, object-level contrastive learning is employed in the object classification network featuring a siamese network to realize global features extraction, and a sub-object-level contrastive learning method is designed in the semantic segmentation network to efficiently extract salient features from boundaries of landslides. An iterative training strategy is also proposed to fuse features in the semantic space, further improving both the object-level and pixel-level classification performances. The proposed ICSSN is evaluated on a real-world landslide dataset, and experimental results show that it greatly improves both the classification and segmentation accuracy of old landslides. For the semantic segmentation task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448, the mIoU improves from 0.6405 to 0.6610, the landslide IoU grows from 0.3381 to 0.3743, the PA is improved from 0.945 to 0.949, and the object-level detection accuracy of old landslides surges from 0.55 to 0.90. For the object classification task, the F1 score increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to 0.8875.

Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation. Due to the long and thin shape as well as the shades induced by vegetation and buildings, small-sized roads are more difficult to discern. In order to improve the reliability and accuracy of small-sized road extraction when roads of multiple sizes coexist in an HRSI, an enhanced deep neural network model termed Dual-Decoder-U-Net (DDU-Net) is proposed in this paper. Motivated by the U-Net model, a small decoder is added to form a dual-decoder structure for more detailed features. In addition, we introduce the dilated convolution attention module (DCAM) between the encoder and decoders to increase the receptive field as well as to distill multi-scale features through cascading dilated convolution and global average pooling. The convolutional block attention module (CBAM) is also embedded in the parallel dilated convolution and pooling branches to capture more attention-aware features. Extensive experiments are conducted on the Massachusetts Roads dataset with experimental results showing that the proposed model outperforms the state-of-the-art DenseUNet, DeepLabv3+ and D-LinkNet by 6.5%, 3.3%, and 2.1% in the mean Intersection over Union (mIoU), and by 4%, 4.8%, and 3.1% in the F1 score, respectively. Both ablation and heatmap analysis are presented to validate the effectiveness of the proposed model. Moreover, the designed small decoder and introduced DCAM can be used as a portable module to be embedded in other U-Net-like models with encoder-decoder structure to enhance the road detection performance, especially for small-sized roads. The high portability of the designed module is validated by embedding in the LinkNet, which greatly improves the road segmentation performance.

In this study, we used HyMap-C airborne hyperspectral imagery and ground samples collected synchronously to explore the estimation of soil heavy metal concentration. Preprocessing methods such as first-order derivative were used to enhance the weak spectral information related heavy metals. The multivariate stepwise regression method was used to select the spectral characteristics and establish the inversion model. The samples were divided into 3 parts, model set, validation set and test set. For the arsenic (As) the errors of the samples sets were 0.55, 0.75, 0.44, and the root-mean-square error were 51.20, 30.12, 32.78 mg/kg respectively. The results show that this method can predict the heavy metals arsenic in the study area.