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Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.

Aggressive emission control measures were taken by the Chinese government after the promulgation of the 'Air Pollution Prevention and Control Action Plan' in 2013. Here we evaluated the air quality and health benefits associated with this stringent policy during 2013-2015 by using surface PM2.5 concentrations estimated from a three-stage data fusion model and cause-specific integrated exposure-response functions. The population-weighted annual mean PM2.5 concentrations decreased by 21.5% over China during 2013-2015, reducing from 60.5 in 2013 to 47.5 μg m−3 in 2015. Subsequently, the national PM2.5-attributable mortality decreased from 1.22 million (95% CI: 1.05, 1.37) in 2013 to 1.10 million (95% CI: 0.95, 1.25) in 2015, which is a 9.1% reduction. The limited health benefits compared to air quality improvements are mainly due to the supralinear responses of mortality to PM2.5 over the high concentration end of the concentration-response functions. Our study affirms the effectiveness of China's recent air quality policy; however, due to the nonlinear responses of mortality to PM2.5 variations, current policies should remain in place and more stringent measures should be implemented to protect public health.

Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.

Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.

Global warming is driving a significant increase in the frequency and intensity of extreme weather events, posing serious challenges to regional ecological security and sustainable agricultural development. Northeast China, situated at the climatic transition between temperate monsoon and cold temperate zones, is particularly sensitive to extreme heatwaves, heavy rainfall, and prolonged drought. This study aims to develop a spatially explicit extreme climate event forecasting system for Northeast China for the period 2000–2024 by integrating multi-source remote sensing data (NDVI, LST, albedo), MODIS surface products, topographic parameters, and ground-based meteorological observations from the China Meteorological Administration (CMA). Three ETCCDI indices—annual maximum daily temperature (TXx), maximum 1-day precipitation (RX1day), and consecutive dry days (CDD)—are used as prediction targets. A gradient-boosting machine learning model (XGBoost) was trained on an 80/20 stratified split of station-grid matched samples and validated using a leave-one-city-out cross-validation strategy. SHAP analysis was applied to quantify variable contributions. The model achieves AUC values of 0.91 (TXx at 37 °C threshold), 0.89 (RX1day), and 0.84 (CDD), with Probability of Detection (POD) of 76.3%, 72.1%, and 69.4%, respectively. For continuous prediction, the root mean square error (RMSE) is 1.3 °C for TXx and 6.8 mm for RX1day. The prediction error rate in the black soil belt is 10.2%, demonstrating the effectiveness of multi-source data integration for high-precision extreme weather forecasting. These results provide technical support for climate risk assessment and agricultural disaster warning in Northeast China.

Tourist behavior recognition and prediction are core technologies for smart tourism systems, with significant importance for optimizing resource allocation in scenic areas and enhancing the tourist experience. However, existing methods exhibit clear shortcomings in handling multi-source heterogeneous data fusion and in modeling long-term sequential behavior. To this end, this article proposes a tourist behavior recognition and prediction model based on a dual-stage attention fusion network. This model incorporates three key contributions: an adaptive hierarchical attention-based fusion mechanism for multi-source heterogeneous data; a dual-stage attention network architecture that transitions from coarse-grained recognition to fine-grained prediction; and a behavior prediction framework. Specifically, the dual-stage architecture, through a cascaded design of spatio-temporally separated attention and semantically enhanced attention combined with a learnable gating mechanism, enables adaptive feature transfer between the two stages, capturing multi-scale spatio-temporal features of behavior sequences. Results show that our method improves accuracy by an average of 10.3% and reduces average displacement error by 27.6%, validating its effectiveness.

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements.

With the continuous expansion of the expressway network and the rapid growth of traffic demand, traditional single-source traffic detection data is limited in spatial–temporal coverage and accuracy, which can hardly support the refined operation and management of intelligent expressways. Existing data preprocessing methods often fail to fully capture global spatiotemporal features, and traditional PSO-BP neural networks are prone to local optima. To address these issues, this study investigates multi-source traffic data fusion using ETC-DSRC and RTMS microwave data from the Jiangsu section of the G50 Shanghai-Chongqing Expressway. The HaLRTC tensor completion algorithm is adopted to repair missing and abnormal data, fully mining the spatial–temporal correlation characteristics of traffic flow. The beetle antennae search (BAS) mechanism is introduced into the particle swarm optimization (PSO) process to improve particle search behavior and population diversity. On this basis, a BAS-optimized PSO-BP neural network, referred to as BSO-BP in this study, is constructed for multi-source traffic data fusion. In this model, the improved PSO algorithm is used to optimize the initial weights and thresholds of the backpropagation (BP) neural network, thereby improving the global search capability and convergence stability of the fusion model. Taking the average road speed as the fusion target, MAE, RMSE and MAPE are used for accuracy verification. The results show that the proposed model has significantly higher accuracy than single-source data methods and BP, PSO-BP, and GA-PSO-BP models, and can reflect the real traffic state of road sections more accurately.

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.