Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies’ mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and À Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

Remote sensing has more advantages than the traditional methods of land surface water (LSW) mapping because it is a low-cost, reliable information source that is capable of making high-frequency and repeatable observations. The normalized difference water indexes (NDWIs), calculated from various band combinations (green, near-infrared (NIR), or shortwave-infrared (SWIR)), have been successfully applied to LSW mapping. In fact, new NDWIs will become available when Advanced Land Imager (ALI) data are used as the ALI sensor provides one green band (Band 4), two NIR bands (Bands 6 and 7), and three SWIR bands (Bands 8, 9, and 10). Thus, selecting the optimal band or combination of bands is critical when ALI data are employed to map LSW using NDWI. The purpose of this paper is to find the best performing NDWI model of the ALI data in LSW map. In this study, eleven NDWI models based on ALI, Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data were compared to assess the performance of ALI data in LSW mapping, at three different study sites in the Yangtze River Basin, China. The contrast method, Otsu method, and confusion matrix were calculated to evaluate the accuracies of the LSW maps. The accuracies of LSW maps derived from eleven NDWI models showed that five NDWI models of the ALI sensor have more than an overall accuracy of 91% with a Kappa coefficient of 0.78 of LSW maps at three test sites. In addition, the NDWI model, calculated from the green (Band 4: 0.525–0.605 μm) and SWIR (Band 9: 1.550–1.750 μm) bands of the ALI sensor, namely NDWI_A4,9, was shown to have the highest LSW mapping accuracy, more than the other NDWI models. Therefore, the NDWI_A4,9 is the best indicator for LSW mapping of the ALI sensor. It can be used for mapping LSW with high accuracy.

Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms based on deep learning face the problem of catastrophic forgetting, that is, they cannot maintain the performance of the old batch data. Therefore, it has become more and more important to ensure that the scene classification model has the ability of continual learning, that is, to learn new batch data without forgetting the performance of the old batch data. However, the existing remote sensing image scene classification datasets all use static benchmarks and lack the standard to divide the datasets into a number of sequential learning training batches, which largely limits the development of continual learning in remote sensing image scene classification. First, this study gives the criteria for training batches that have been partitioned into three continual learning scenarios, and proposes a large-scale remote sensing image scene classification database called the Continual Learning Benchmark for Remote Sensing (CLRS). The goal of CLRS is to help develop state-of-the-art continual learning algorithms in the field of remote sensing image scene classification. In addition, in this paper, a new method of constructing a large-scale remote sensing image classification database based on the target detection pretrained model is proposed, which can effectively reduce manual annotations. Finally, several mainstream continual learning methods are tested and analyzed under three continual learning scenarios, and the results can be used as a baseline for future work.

Indexes are vital to enhance the lookup on single or multiple columns, and building proper indexes can significantly improve the database performance. Existing works focus on adding new indexes that can benefit the read queries, but they have several limitations. First, real-world workloads may have numerous queries and it is tricky to analyze their index requirements and find the most beneficial indexes within resource limit. Second, they fail to consider the update of existing indexes, which may be redundant or even have negative effects to current workload. Third, they cannot estimate the index maintenance costs, which are affected by multiple index utilization factors and can significantly affect the index benefits, especially for high-write-ratio workloads. To address those challenges, we propose an incremental index management system Autoindex for dynamic workloads. First, to support incremental index management, we map the incoming queries into query templates and efficiently generate promising candidate indexes from matched templates. And then we propose to utilize Monte Carlo Tree Search to incrementally add indexes from the candidate indexes or remove indexes from existing indexes, so as to ensure high workload performance. Besides, we propose a deep index estimation model, which integrates the practical experience to extract critical cost features and applies deep regression to estimate index benefits from historical index management data. We have implemented the modules like candidate index generation and index estimator in an open-sourced database system openGauss. Experimental re-sults showed that our method outperformed existing approaches on both testing and real-world workloads.

The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model’s backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network’s ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.