With the advent of the Industry 4.0 era, Federated Learning (FL) provides robust data privacy protection for smart manufacturing and supply chain optimization, while facilitating collaborative intelligent optimization across enterprises and devices. However, the complex and overparameterized deep neural networks used in FL result in significant computational overhead for Industrial Internet of Things (IIoT) devices, leading to low energy efficiency and hindering the practical deployment of FL on IIoT devices. Moreover, the widespread data and device heterogeneity in the IIoT exacerbates the decrease in energy efficiency caused by inconsistent computational efficiency across nodes. This article proposes an energy-efficient dynamic model pruning method for FL, named EDPrune-FL, to address the aforementioned challenges. Compared to existing methods, this approach offers greater flexibility and efficiency by utilizing a dynamic pruning rate allocation mechanism. This mechanism updates the pruning rate for each participating client in every communication round, allowing the pruning upper bound to adapt to the varying importance of different learning stages in FL. EDPrune-FL ensures the global model’s performance while reducing the training energy consumption of clients in heterogeneous environments. To guarantee that dynamic pruning maintains the stability and effectiveness of the model in heterogeneous environments, we also demonstrated the convergence of EDPrune-FL and discussed the relationship between pruning rates and convergence, providing a qualitative analysis. Experimental results demonstrate that our method outperforms the state-of-the-art technique across four real-world datasets. With tests conducted on 100 clients, our approach reduces energy consumption by 10% while maintaining comparable accuracy.

Federated learning (FL) has emerged as a pivotal collaborative machine learning framework, enabling privacy-preserving analytics for smart city applications using distributed data from Internet of Things (IoT) devices. However, the inherent data heterogeneity that arises from diverse geographical and environmental factors poses significant challenges to the effectiveness of FL-based models. To address these challenges, this paper introduces a novel Prototype-Based FL framework for cross-domain object extraction in heterogeneous remote sensing images. The proposed framework employs multiple vectors to represent class prototypes for capturing the intricate intra-class variations and mitigating the adverse effects of non-identically distributed (non-IID) data across clients. Furthermore, we adopt a distance-based classification method to reduce classification errors. Additionally, we propose a Prototype-Anchored Metric Learning approach to minimize intra-class variance and enhance inter-class separability, which can facilitate the alignment of feature representations across heterogeneous datasets. The proposed method improves the coherence and stability of feature spaces in federated settings and enhances the global model’s generalization capabilities for complex urban monitoring tasks. Extensive experiments on three distinct remote sensing datasets(including infrastructure and disaster) demonstrate that the proposed method significantly outperforms state-of-the-art FL-based approaches in urban monitoring accuracy and robustness. The code is available at

Abstract Net primary productivity (NPP) is essential for sustainable resource management and conservation, and it serves as a primary monitoring target in smart forestry systems. The predominant method for NPP inversion involves data collection through terrestrial and satellite sensing systems, followed by parameter estimation using models such as the Carnegie‐Ames‐Stanford Approach (CASA). While this method benefits from low costs and extensive monitoring capabilities, the data derived from multisource sensing systems display varied spatial scale characteristics, and the NPP inversion models cannot detect the impact of data heterogeneity on the outcomes sensitively, reducing the accuracy of fine‐grained NPP inversion. Therefore, this paper proposes a modular system for fine‐grained data processing and NPP inversion. Regarding data processing, a two‐stage spatial‐spectral fusion model based on non‐negative matrix factorization (NMF) is proposed to enhance the spatial resolution of remote sensing data. A spatial interpolation model based on stacking generalization with residual correction is introduced to get raster meteorological data compatible with remote sensing images. Furthermore, we optimize the CASA model with the kernel method to enhance model sensitivity and enrich the spatial details of the inversion results with high resolution. Through validation using real datasets, the proposed fusion and interpolation models have significant advantages over mainstream methods. Furthermore, the correlation coefficient () between the estimated NPP using our improved inversion model and the field‐measured NPP is 0.69, demonstrating the feasibility of this platform in detailed forest NPP monitoring tasks.

Remote-sensing (RS) images with high spatial and temporal resolutions play a significant role in monitoring periodic landscape changes for earth observation science. To enrich RS images, spatiotemporal fusion (STF) is considered a promising approach. The key challenge in the current STF-based methods is the requirement for large-scale data. In this work, we propose a deep-learning-based method called spatiotemporal fusion multilayer perceptron (StfMLP) to tackle this challenge. First, our method focuses on the given data in the manner of transductive learning. Second, we propose a designed multilayer perceptron (MLP) model to capture the time dependency and consistency among the input images. Consequently, StfMLP is capable of simultaneously achieving more accurate fusion and requiring a small-scale of data. We conduct extensive experiments on two widely adopted public datasets, namely Coleambally irrigation area (CIA) and the lower Gwydir catchment (LGC). The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods effectively. Code, trained model, and cropped images are available online ( https://github.com/luhailaing-max/StfMLP-master ).

The smart city system, which is a type of enterprise management system (EMS), automatically manages cities and schedules resources efficiently based on spatial data generated by devices, such as the Internet of Things and mobile. However, with the increasing deployment of technologies, including sensor and location-based services, their ever-growing spatial data are no longer managed efficiently by traditional EMS. To overcome this issue, we present SeFrame, which is aspatiallyenabledframework for improving the efficiency of smart city EMS based on a distributed architecture. The framework supports a set of spatial queries, including: The range query, k-nearest neighbors query, and spatial join query. It benefits greatly from using the buffer-enabled partition method to eliminate duplicate results. In each partition, the local index based on combination of the quad-tree and grid index (CQG) significantly improves the spatial query efficiency in memory. CQG manages complex spatial objects, including a point, polygon, and polyline. By taking full advantage of the local index, SeFrame accesses skewed spatial data in constant time. In experiments, we demonstrated that the proposed method delivered superior performance in terms of scalability and query efficiency, in most cases.

Abstract High resolution remote sensing image cloud removal can bring a lot of convenience for human activities. However, the existing cloud removal algorithms have a variety of disadvantages. First of all, they have the disadvantages of long computing time and large consumption of computing resources. Secondly, the effect of recovery needs to be improved. In order to improve the above two points, a near real‐time effective algorithm is proposed, namely MSAR‐Defognet (multiple scale attention residual network using for cloud remove), which consumes less computing power and space and has superior cloud removal effect. On the one hand, several different large‐scale filters are chosen to extract the weak information effectively, while can save the computing power and shorten the image processing time. On the other hand, the fine‐grained convolution residual block with channel attention mechanism is used to enhance the network's ability to extract cloud features. In addition, a data set which is closer to the real cloud shape and has higher richness to train the cloud removal network, so that the parameters obtained by training have stronger robustness and can adaptively remove clouds with different thickness. Experiments show that, compared with other advanced network models, the network not only has the advantage of fast processing speed, but also has better image restoration effect in high‐resolution remote sensing image restoration. It can meet the requirements of many hard real‐time tasks, so that remote sensing images can play a greater value for human activities.

Deep unsupervised hashing methods are gaining attention in the field of remote sensing (RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most previous unsupervised hashing research used only natural image-based pre-trained models to generate label matrices; however, this method cannot capture the semantic information of RS images well and limits the accuracy of retrieval. To solve this problem, the authors propose a deep unsupervised weighted hashing (DUWH) model that uses a similarity matrix updating strategy based on a weighted similarity structure to achieve the mutual optimization of the similarity matrix and hash network. The authors devise a novel combinatorial loss function to improve the hash performance that can be used to obtain higher quality hash codes by assigning different weights to the sample pairs with different difficulties. Experiments were conducted on two RS datasets to verify the excellent performance of the proposed method.

Compared with traditional images, multispectral images (MSIs) contain more spectral bands and higher data dimensions. The existing MSI classification model has high computational complexity and consumes a lot of computing resources. In this letter, we propose a lightweight multispectral classification method named CABNN based on binary neural networks (BNNs) to effectively have a trade-off between model performance and computational cost. First, we modify and binarize the MobileNetV1 network and add almost computation-free shortcuts to enhance the expressive capability. Secondly, since the BNN is sensitive to the distribution of activation functions, we introduce RPReLU with learnable coefficients to automatically adjust activation distribution at almost no extra cost. Lastly, considering that MSIs have multiple channels, we utilize an efficient channel attention (ECA) module to assign different weights to each channel to concentrate on crucial features and suppress insignificant features. We conduct experiments on four public MSI datasets, including NaSC-TG2, EuroSAT, GID Fine land-cover classification, and UC Merced Land Use. Extensive experiments demonstrate that the proposed CABNN has higher efficiency and better comprehensive performance than the state-of-the-art methods across the board.

Hyperspectral image (HSI) registers hundreds of spectral bands, whose intraclass variability and interclass similarity are resourceful information to be mined. Intraclass variability reflects the nonuniform and redundancy of the spatial and semantic features extracted from HSI. Interclass similarity represents the inherent relationship between adjacent features and snapshots. Existing models extract the superficial correlation representation for HSI to tackle the classification task but fail to embed the interclass and intraclass correlations due to these models’ intrinsic bottlenecks. Confronting the challenges of capturing interrelation for complex data in practice, we propose a Context-Aware Attentional Graph U-Net (CAGU) to improve these two modes of representation, which is more flexible in feature enhancement. In this method, attentional Graph U-Net is capable of extracting the intraclass embeddings within a non-Euclidean space by combining similar distributing feature vertices. The gated recurrent unit (GRU) is another critical component of our model to capture the context-aware dynamic interclass embeddings. Extensive experiments demonstrate that our model can efficiently outperform state-of-the-art methods across-the-board on five wide-adopted public data sets, namely, Pavia University, Indian Pines, Salinas Scene-show, Houston 2013, and Houston 2018, on par with the same scale of model parameters.

Hyperspectral images (HSIs) have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of the HSI are, respectively, representing the correlated information among the local and global, as well as the abundant parameters of the model. To tackle this challenge, we propose a multi-scale U-shape multi-layer perceptron (MUMLP) a model consisting of the designed multi-scale channel (MSC) block and the U-shape multi-layer perceptron (UMLP) structure. MSC transforms the channel dimension and mixes spectral band feature to embed the deep-level representation adequately. UMLP is designed by the encoder–decoder structure with multi-layer perceptron layers, which is capable of compressing the large-scale parameters. Extensive experiments are conducted to demonstrate that our model can outperform state-of-the-art methods across the board on three wide-adopted public datasets, namely Pavia University (PaviaU), Houston 2013, and Houston 2018.

Semantic segmentation is a fundamental task extensively used in the analysis of high-resolution aerial images. Due to the objects with various scales widely appearing in high-resolution aerial images, the fixed square receptive field in most existing DCNNs cannot work well and usually leads to unexpected predictions. To alleviate this problem, we propose a light-weight scale-aware module (SWD), which is end-to-end differentiable and can be embedded in most existing networks. In our module, we employ the re-sampling scheme to make each element of convolutional patches adjust its position, which explicitly adjusts the receptive field size to fit different scales of target objects. Further, considering re-sampled feature maps as weighted maps implicitly introduces spatial attention mechanism. As a result, with only 0. 2M additional parameters, the network embedded with our module can automatically adjust its receptive field to better recognize objects in various scales. In the experiment, we evaluate our method on the ISPRS Vaihingen Dataset, especially the analysis of buildings category, which usually in different scales. We further compare it to the spatial attention mechanism and mainstream networks that utilize multi-scale information. The experimental results and comprehensive analysis demonstrate the effectiveness and efficiency of our proposed method.

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability.

With the development of network communication, a 1000‐fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory‐based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two‐level index based on the Alluxio key‐value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory‐based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.

As a form of social media, Twitter records real life events in our cities as they happen. Huge numbers of tweets under the heading of transportation or metro are published every day. This paper presents an application for Traffic Events Detection and Summary (TEDS) based on mining representative terms from the tweets posted when anomalies occur. The proposed ensemble application contains an efficient TEDS search engine with multiple indexing, ranking, and scoring schemes. Spatio-temporal analysis and a novel wavelet analysis model are applied for traffic event detection. This application could benefit both drivers and transportation authorities. Users can search transportation status and analyze traffic events in specific locations of interest. Utilizing the proposed signal processing technology, we demonstrate the system's effectiveness by examining traffic and metro travel in the Washington D.C. area. As the collaboration between a citizen's life and social media becomes ever greater, this could have a significant impact on the prediction of traffic flow, travel selection, and other city computing functions.