Classifying ground deformation processes, such as landslides, subsidence, deep-seated gravitational slope deformations (DSGSDs), and mining-induced deformations, is key for large-scale hazard assessment and national land use management. Earth observation provides heterogeneous data over the same geographic region, including Interferometric Synthetic Aperture Radar (InSAR) time series, multispectral imagery, and terrain products. However, effectively integrating such spatiotemporal information from multimodal datasets remains a major challenge. In order to fully utilize the rich information contained in the time series and to exploit the complementary strengths of spatial and temporal data, we propose a dual-branch deep learning approach that integrates InSAR ground deformation time series with geospatial information for classifying slow-moving ground deformation processes. To validate the approach, we construct a ground deformation dataset containing over 26,000 Active Deformation Areas (ADAs), labelled into four deformation types: Landslide, Subsidence, DSGSD, and Mining. Results demonstrate that our model achieves an overall classification accuracy exceeding 90% on both ascending and descending test dataset, though confusion remains between certain classes, such as landslides and DSGSD. Explainable AI (XAI) analysis indicates that spatial and morphological features contribute more significantly to classification performance than temporal deformation patterns, with clearer distinctions for subsidence and mining, but more overlap between landslides and DSGSDs. This work highlights the strength of multi modal data fusion method to classify ground deformation processes, while setting the stage for future research.

Heavy rainfall and earthquake in mountain areas usually trigger numerous landslides. Fast and accurate mapping of landslides is crucial for risk management and emergency rescue. Deep learning-based landslide detection methods can automate identification, but convolutional neural network (CNN) models focus primarily on local features, often missing crucial global context in landslide images. Conversely, Transformer-based models excel at capturing global features but are hindered by high computational complexity. As a result, existing detection models struggle to strike an effective balance between accuracy and efficiency. To address this issue, this article presents a lightweight landslide detection method based on the newly proposed Mamba network. Specifically, a landslide detection model named SegMamba2D with an encoder–decoder structure is proposed. In the encoder, the Mamba network is used to extract multiscale features. A state-space model (SSM) is employed to reduce computational complexity while maintaining accuracy. In the decoder, a multilayer perceptron is used to build a lightweight decoder, ensuring that the model’s overall complexity remains low. The experimental results on both public and new datasets demonstrate that SegMamba2D achieves a superior landslide detection accuracy, with an approximately 2% improvement in$F1$score across various scenarios over conventional models, while significantly reducing computational costs. Additionally, SegMamba2D demonstrates robust generalization performance across diverse research areas. These advancements highlight the model’s potential to enhance accuracy in creating landslide inventories and expedite emergency response times during landslide disasters. The source code is available athttps://github.com/xiaochuan-tang/SegMamba2D

This work focuses on analyzing the efficiency of a purely Rust-written Kyber library for ARM Cortex-M4 microcontrollers. The analysis includes performance comparisons with a C-written Kyber library, as well as the identification and resolution of minor issues within the Rust-written Kyber library. Contributions to the library include proposed solutions for unhandled TRNG exceptions, enhancements in code quality, and improvements in code coverage, all of which have been approved by the library maintainers and released.

In this letter, we propose a Deep Learning (DL) based approach which exploits multispectral Sentinel-2 open-source data and a small-size inventory to map artisanal and small-scale mines (ASM). The study area is in central northern Burkina Faso (Africa) and is characterized by a semi-desert environment that makes mapping challenging. In sub-Saharan Africa, artisanal and small-scale mining represents a source of subsistence for a significant number of individuals. However, because ASM are often illegal and uncontrolled, the materials employed in the excavation process are highly dangerous for the environment as well as for the lives of the people involved in the mining activities. One of the most important aspects regarding ASM is the record of their spatial location which, at the moment, is missing in most of the African regions. Performance evaluation of two state-of-art DL architectures (U-Net, and Attention Deep Supervised Multi-Scale U-Net - ADSMS U-Net) is provided, along with an in-depth analysis of the predictions when dealing with both dry and rainy seasons. The ADSMS U-Net architecture yields generally more accurate predictions than the basic U-Net allowing us to better discriminate ASM in such an environment. The findings show that the proposed approach can detect ASM in semi-desertic areas starting with a few samples at a low cost in terms of both human and financial resources.

In this letter, we use deep learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and synthetic aperture radar (SAR) images (from Sentinel-1). The training, validation, and test zones used to independently evaluate the performance of the CNN on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multipolarization SAR as well as optical data by means of a CNN implemented in TensorFlow that points out the locations where the landslide class is predicted as more likely. As expected, the CNN runs on optical images proved itself excellent for the landslide detection task, achieving an overall accuracy of 98.96%, while CNNs based on the combination of ground range detected (GRD) SAR data reached overall accuracies beyond 95%. Our findings show that the integrated use of SAR data may also allow for rapid detection even during storms and under dense cloud cover and provides comparable accuracy to classical optical change detection in landslide recognition and detection.

In this paper we present a software-based traffic classification engine running on commodity multi-core hardware, able to process in real-time aggregates of up to 14.2 Mpps over a single 10 Gbps interface -- i.e., the maximum possible packet rate over a 10 Gbps Ethernet links given the minimum frame size of 64 Bytes.

Traffic classification on high-speed, multi-Gb/s links has up to now been demonstrated on complex Linux setups using multi-queue Ethernet cards, thread affinity, zero-copy buffers and specialized socket types. Although these approaches do work in principle, the complexity of the involved networking system ends up consuming almost all computational resources to pass packets between kernel and user space, leaving no CPU time to run any actual statistical classification algorithm. In this paper we present a new approach that harnesses a lightweight and inexpensive NetFPGA/1G to group incoming packets in jumboframes so that almost all CPU cycles of a commodity PC running a stock Linux kernel can be dedicated to run a statistical traffic classification algorithm. Experimental results show that our inexpensive setup can execute Support Vector Machine traffic classification in real time to aggregates of up to 7.44M pps. We make MTCLASS' source code available to the community under an open source license.