Satellite video multi-label scene classification predicts semantic labels of multiple ground contents to describe a given satellite observation video, which plays an important role in applications like ocean observation, smart cities, et al. However, the lack of a high-quality and large-scale dataset prevents further improvement of the task. And existing methods on general videos have the difficulty to represent the local details of ground contents when directly applied to the satellite videos. In this paper, our contributions include (1) we develop the first publicly available and large-scale satellite video multi-label scene classification dataset. It consists of 18 classes of static and dynamic ground contents, 3549 videos, and 141960 frames. (2) we propose a baseline method with the novel Spatial and Temporal Feature Cooperative Encoding (STFCE). It exploits the relations between local spatial and temporal features, and models long-term motion information hidden in inter-frame variations. In this way, it can enhance features of local details and obtain the powerful video-scene-level feature representation, which raises the classification performance effectively. Experimental results show that our proposed STFCE outperforms 13 state-of-the-art methods with a global average precision (GAP) of 0.8106 and the careful fusion and joint learning of the spatial, temporal, and motion features are beneficial to achieve a more robust and accurate model. Moreover, benchmarking results show that the proposed dataset is very challenging and we hope it could promote further development of the satellite video multi-label scene classification task.

Siamese-based trackers usually convert the object tracking task into a similarity matching problem between the target template and the search region. Since fixed or manually updated templates are not robust when tracking moving objects with dramatically changing appearance, this paper proposes an improved siamese graph attention network with adaptive template update called SiamGT. By establishing spatiotemporal and context dependencies between historical images and search regions, a frame selection mechanism is added to improve the richness of information. In addition, a graph attention network with residual connections is used in the template update mechanism which enables the propagation and aggregation of information to generate robust templates. Extensive experimental results on challenging benchmarks such as UAV123, OTB100, and VOT2019 demonstrate that the proposed SiamGT has achieved state-of-the-art performance in visual object tracking.

Video satellites can continuously image large areas and provide dynamic, real-time monitoring of hotspots and objects. The intelligent processing and analysis of satellite video have become a research hotspot in the field of remote sensing. However, the lack of high-quality satellite video datasets limits the development of relevant object detection, object tracking, and object segmentation. In this paper, we build the largest scale satellite video dataset with the most task types supported and object categories, named Satellite Video Multi-Mission Benchmark (SAT-MTB). First, multi-task annotation of aircraft, ships, cars, trains, and their corresponding 14 categories of fine-grained objects in 249 satellite videos is performed based on horizontal bounding boxes (HBB), oriented bounding boxes (OBB), masks, which cover more than 50,000 frames and 1,033,511 annotated object instances. Then, we review the tasks of object detection, object tracking, and object segmentation based on satellite videos, providing a comprehensive overview of progress in related datasets and algorithm research. Finally, we establish the first public benchmark of multi-task algorithms for satellite video object detection, object tracking, and object segmentation, evaluating and analyzing the performance of a total of 47 representative algorithms under different tasks on the constructed dataset. The proposed SAT-MTB will significantly advance research in intelligent processing and analysis of satellite video and related applications.

In this paper, we briefly summarize the first challenge on moving object detection and tracking in satellite videos (SatVideoDT). This challenge has three tracks related to satellite video analysis, including moving object detection (Track 1), single object tracking (Track 2), and multiple-object tracking (Track 3). 123, 89, and 70 participants successfully registered, while 37, 42, and 29 teams submitted their final results on the test datasets for Tracks 1-3, respectively. The top-performing methods and their results in each track are described with details. This challenge establishes a new benchmark for satellite video analysis.

We propose CFS, a distributed file system for large scale container platforms. CFS supports both sequential and random file accesses with optimized storage for both large files and small files, and adopts different replication protocols for different write scenarios to improve the replication performance. It employs a metadata subsystem to store and distribute the file metadata across different storage nodes based on the memory usage. This metadata placement strategy avoids the need of data rebalancing during capacity expansion. CFS also provides POSIX-compliant APIs with relaxed semantics and metadata atomicity to improve the system performance. We performed a comprehensive comparison with Ceph, a widely-used distributed file system on container platforms. Our experimental results show that, in testing 7 commonly used metadata operations, CFS gives around 3 times performance boost on average. In addition, CFS exhibits better random-read/write performance in highly concurrent environments with multiple clients and processes.

MOTIVATION: Single-cell bisulfite sequencing (BS-seq) techniques have been developed for DNA methylation heterogeneity detection and studies with limited materials. However, the data deficiency such as low read mapping ratio is still a critical issue. RESULTS: We comprehensively characterize single-cell BS-seq data and reveal chimerical molecules to be the major source of alignment failures. These chimerical molecules are produced by recombination of genomic proximal sequences with microhomology regions (MR) after bisulfite conversion. In addition, we find DNA methylation within MR is highly variable, suggesting the necessity of removing these regions to accurately estimate DNA methylation levels. We further develop scBS-map to perform quality control and local alignment of bisulfite sequencing data, chimerical molecule determination and MR removal. Using scBS-map, we show remarkable increases in uniquely mapped reads, genomic coverage and number of CpG sites, and recover more functional elements with precise DNA methylation estimation. AVAILABILITY AND IMPLEMENTATION: The scBS-map software is freely available at https://github.com/wupengomics/scBS-map. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: DNA methylation is important for gene silencing and imprinting in both plants and animals. Recent advances in bisulfite sequencing allow detection of single nucleotide variations (SNVs) achieving high sensitivity, but accurately identifying heterozygous SNVs from partially C-to-T converted sequences remains challenging. Results: We designed two methods, BayesWC and BinomWC, that substantially improved the precision of heterozygous SNV calls from ∼80% to 99% while retaining comparable recalls. With these SNV calls, we provided functions for allele-specific DNA methylation (ASM) analysis and visualizing the methylation status on reads. Applying ASM analysis to a previous dataset, we found that an average of 1.5% of investigated regions showed allelic methylation, which were significantly enriched in transposon elements and likely to be shared by the same cell-type. A dynamic fragment strategy was utilized for DMR analysis in low-coverage data and was able to find differentially methylated regions (DMRs) related to key genes involved in tumorigenesis using a public cancer dataset. Finally, we integrated 40 applications into the software package CGmapTools to analyze DNA methylomes. This package uses CGmap as the format interface, and designs binary formats to reduce the file size and support fast data retrieval, and can be applied for context-wise, gene-wise, bin-wise, region-wise and sample-wise analyses and visualizations. Availability and implementation: The CGmapTools software is freely available at https://cgmaptools.github.io/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.