Weather forecasting is a classical problem in remote sensing, in which precipitation is difficult to predict accurately because of its complex physical motion. Precipitation significantly impacts human life, work, and the ecological environment. Prec...
In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early...
Due to the limitation of less information in a single image, it is very difficult to generate a high-precision 3D model based on the image. There are some problems in the generation of 3D voxel models, e.g., the information loss at the upper level of...
With the explosive growth in the quantity of electrical equipment in distribution networks, traditional manual inspection struggles to achieve comprehensive coverage due to limited manpower and low efficiency. This has led to frequent equipment failu...
The classification of urban functional zones is crucial for improving land use efficiency and promoting balanced development across urban areas. Existing methods for classifying urban functional zones using mobile signaling data face challenges prima...
Post-traumatic stress disorder (PTSD) is a complex psychological disorder caused by multiple factors, which are not only related to individual psychological states but also closely linked to physiological responses, social environments, and personal...
In recent years, Fully Convolutional Networks (FCN) have led to a great improvement of semantic labeling for various applications including multi-modal remote sensing data. Although different fusion strategies have been reported for multi-modal data,...
As the number of modalities in biomedical data continues to increase, the significance of multi-modal data becomes evident in capturing complex relationships between biological processes, thereby complementing disease classification. However, the cur...
In the field of remote sensing, accurate semantic segmentation is crucial for applications such as environmental monitoring and urban planning. Effective fusion of multi-modal data is a key factor in improving land cover classification accuracy. To a...
With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Conseq...
The Yellow River Delta (YRD) is a vital agricultural and ecologically fragile zone in China. Understanding the spatial pattern and evolutionary characteristics of Rural Settlements Area (RSA) in this region is crucial for both ecological protection a...
The deep integration of machine learning technology with geological prospecting has brought to the forefront a key challenge: how to construct geological-mineralization models by fusing multi-source data, select model features with guidance from meta...
Recommendation systems have lately been popularised globally. However, often they need to be adapted to particular data and the use case. We have developed a machine learning-based recommendation system, which can be easily applied to almost any item...
To address the issue of missing feature information for small objects caused by the sparsity and irregularity of point clouds, as well as the poor detection performance on small objects due to their weak feature representation, this paper proposes a...
Semantic segmentation of remote sensing images is a fundamental task in computer vision, holding substantial relevance in applications such as land cover surveys, environmental protection, and urban building planning. In recent years, multi-modal fus...
Smart city decision-making increasingly relies on heterogeneous urban data sources. Dense traffic sensor streams provide continuous quantitative measurements, while citizen-generated textual reports offer event-driven contextual information. However,...
Land cover classification from very high-resolution (VHR) remote sensing images is a challenging task due to the complexity of geography scenes and the varying shape and size of ground targets. It is difficult to utilize the spectral data directly, o...
The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detecti...
To address the challenges in existing multi-sensor data fusion methods for rail surface defect diagnosis, particularly their limitations in fully exploiting potential synergistic information among multimodal data and effectively bridging the semantic...
Self-supervised learning (SSL) has significantly bridged the gap between supervised and unsupervised learning in computer vision tasks and shown impressive success in the field of remote sensing (RS). However, these methods have primarily focused on...
In oenology, statistical analyses are used for descriptive purposes, mostly with separate sensory and chemistry data sets. Cases that combine them are mostly supervised, usually seeking to optimize discrimination, classification, or prediction power....
The latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions. In order...
The leaf area index (LAI) is an important growth indicator used to assess the health status and growth of citrus trees. Although LAI estimation based on unmanned aerial vehicle (UAV) platforms has been widely used for field crops, mainly focusing on...
Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterog...
In the field of digital cultural heritage (DCH), 2D/3D digitization strategies are becoming more and more complex. The emerging trend of multimodal imaging (i.e., data acquisition campaigns aiming to put in cooperation multi-sensor, multi-scale, mult...
Aiming at the problems of insufficient environmental perception capability of autonomous mobile robots and low multi-modal data fusion efficiency in the complex underground coal mine environment featuring low illumination, high dust, and dynamic obst...
Impact craters, as key geomorphic features on Mars, provide important insights into surface processes and geological evolution. However, automatic classification of crater morphologies remains challenging due to substantial variations in size, degrad...
Cyberbullying involves the use of social media platforms to harm or humiliate people online. Victims may resort to self-harm due to the abuse they experience on these platforms, where users can remain anonymous and spread malicious content. This high...
The complementary nature of different modalities and multiple bands used in remote sensing data is helpful for tasks such as change detection and the prediction of agricultural variables. Nonetheless, correctly processing a multi-modal dataset is not...
This study addresses the shortcomings of traditional exercise volume assessment methods in dynamic modeling and individual adaptation by proposing a multi-modal data fusion framework based on a spatio-temporal attention-enhanced LSTM neural network f...
Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Report...
The popularity of information technology has given rise to a growing interest in smart education and has provided the possibility of combining online and offline education. Knowledge graphs, an effective technology for knowledge representation and ma...
The rapidly evolving field of Virtual Reality (VR)-based Human–Computer Interaction (HCI) presents a significant demand for robust and accurate hand tracking solutions. Current technologies, predominantly based on single-sensing modalities, fal...
Due to the low cost and easy deployment, self-supervised depth completion has been widely studied in recent years. In this work, a self-supervised depth completion method is designed based on multi-modal spatio-temporal consistency (MSC). The self-su...
Data processing in robotics is currently challenged by the effective building of multimodal and common representations. Tremendous volumes of raw data are available and their smart management is the core concept of multimodal learning in a new paradi...
Land use and land cover (LULC) mapping is a powerful tool for monitoring large areas. For the Amazon rainforest, automated mapping is of critical importance, as land cover is changing rapidly due to forest degradation and deforestation. Several resea...
With the rapid advancement of remote sensing technologies, the joint classification of hyperspectral image (HSI) and LiDAR data has become a key research focus in the field. To address the impact of inherent uncertainties in hyperspectral images on c...
Inter-state cyberattacks are increasingly becoming a major hidden threat to national security and global order. However, current prediction models are often constrained by single-source data due to insufficient consideration of complex influencing fa...
The convergence of biometric and environmental sensing represents a transformative advancement in wearable technology, moving beyond single-parameter tracking towards a holistic, context-aware paradigm for health monitoring. This review comprehensive...
With the advent of big data science, the field of geoscience has undergone a paradigm shift toward data-driven scientific discovery. However, the abundance of geoscience data distributed across multiple sources poses significant challenges to researc...
With the development of network technology, the number of gambling websites has grown dramatically, causing a threat to social stability. There are many machine learning-based methods are proposed to identify gambling websites by analyzing the URL, t...
The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expre...
Fruit quality is an important aspect in determining the consumer preference in the supply chain. Thermal imaging was used to determine different pineapple varieties according to the physicochemical changes of the fruit by means of the deep learning m...
Identifying sedimentary facies represents a fundamental aspect of oil and gas exploration. In recent years, geologists have employed deep learning methods to develop comprehensive predictions of sedimentary facies. However, their methods are often co...
To accurately characterize the complex coupling and nonlinear interactions between meteorological and oceanic variables in offshore wind power scenarios, this study proposes a novel forecasting model based on a “multi-scale fusion-decomposition...
A novel eggplant disease detection method based on multimodal data fusion and attention mechanisms is proposed in this study, aimed at improving both the accuracy and robustness of disease detection. The method integrates image and sensor data, optim...
The effective association of multimodal data is the basis of massive multi-source heterogeneous data sharing in the era of big data. How to realize data autonomous association between massive multimodal databases and the automatic intelligent screeni...
Monocrystalline silicon is an important raw material in the semiconductor and photovoltaic industries. In the Czochralski (CZ) method of growing monocrystalline silicon, various factors may cause node loss and lead to the failure of crystal growth. C...
In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, mul...
This study conducted field experiments in 2024 in Meidaizhao Town, Tumed Right Banner, Baotou City, Inner Mongolia Autonomous Region, adopting a plant-level sampling design with 10 maize plots selected as sampling areas (20 plants per plot). At four...
of 26