Marine oil spills can cause severe damage to the marine environment and biological resources. Using satellite remote sensing technology is one of the best ways to monitor the sea surface in near real-time to obtain oil spill information. The existing methods in the literature either use deep convolutional neural networks in synthetic aperture radar (SAR) images to directly identify oil spills or use traditional methods based on artificial features sequentially to distinguish oil spills from sea surface. However, both approaches currently only use image information and ignore some valuable auxiliary information, such as marine weather conditions, distances from oil spill candidates to oil spill sources, etc. In this study, we proposed a novel method to help detect marine oil spills by constructing a multi-source knowledge graph, which was the first one specifically designed for oil spill detection in the remote sensing field. Our method can rationally organize and utilize various oil spill-related information obtained from multiple data sources, such as remote sensing images, vectors, texts, and atmosphere-ocean model data, which can be stored in a graph database for user-friendly query and management. In order to identify oil spills more effectively, we also proposed 13 new dark spot features and then used a feature selection technique to create a feature subset that was favorable to oil spill detection. Furthermore, we proposed a knowledge graph-based oil spill reasoning method that combines rule inference and graph neural network technology, which pre-inferred and eliminated most non-oil spills using statistical rules to alleviate the problem of imbalanced data categories (oil slick and non-oil slick). Entity recognition is ultimately performed on the remaining oil spill candidates using a graph neural network algorithm. To verify the effectiveness of our knowledge graph approach, we collected 35 large SAR images to construct a new dataset, for which the training set contained 110 oil slicks and 66264 non-oil slicks from 18 SAR images, the validation set contained 35 oil slicks and 69005 non-oil slicks from 10 SAR images, and the testing set contained 36 oil slicks and 36281 non-oil slicks from the remaining 7 SAR images. The results showed that some traditional oil spill detection methods and deep learning models failed when the dataset suffered a severe imbalance, while our proposed method identified oil spills with a sensitivity of 0.8428, specificity of 0.9985, and precision of 0.2781 under those same conditions. The knowledge graph method we proposed using multi-source data can not only help solve the problem of information island in oil spill detection, but serve as a guide for construction of remote sensing knowledge graphs in many other applications as well. The dataset gathered has been made freely available online (https://pan.baidu.com/s/1DDaqIljhjSMEUHyaATDIYA?pwd=qmt6).
Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double logistic (DL) and polynomial fitting (Poly) combined with the dynamic threshold method at thresholds of 35% and 50% (DL-G35, DL-G50, Poly-G35, and Poly-G50) and DL combined with the cumulative curvature extreme value method (DL-CUM)—and two data types—the enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF)—to identify the start (SOS), peak (POS), and end (EOS) of the growing season in alpine meadow (ALM), desert steppe (DRS), forest vegetation (FV), meadow grassland (MEG), and typical grassland (TYG) of the MP. The optimal methods for identifying the SOS, POS, and EOS of typical grassland areas were Poly-G50 (NSE = 0.12, Pbias = 0.22%), DL-G35/50 (NSE = −0.01, Pbias = −0.06%), and Poly-G35 (NSE = 0.02, Pbias = 0.08%), respectively, based on SIF data. The best methods for identifying the SOS, POS, and EOS of desert steppe areas were Poly-G35 (NSE = −0.27, Pbias = −1.49%), Poly-G35/50 (NSE = −0.58, Pbias = −1.39%), and Poly-G35 (NSE = 0.29, Pbias = −0.61%), respectively, based on EVI data. The data source explained most of the differences in phenological estimates. The accuracy of polynomial fitting was significantly greater than that of the DL method, while all methods were better at identifying SOS and POS than they were at identifying EOS. Our findings can help to facilitate the establishment of a phenological estimation system suitable for the Mongolian Plateau and improve the observation methods of vegetation phenology.
The paper presents an automatic region detection based method to reconstruct street scenes from driving recorder images. The driving recorder in this paper is a dashboard camera that collects images while the motor vehicle is moving. An enormous number of moving vehicles are included in the collected data because the typical recorders are often mounted in the front of moving vehicles and face the forward direction, which can make matching points on vehicles and guardrails unreliable. Believing that utilizing these image data can reduce street scene reconstruction and updating costs because of their low price, wide use, and extensive shooting coverage, we therefore proposed a new method, which is called the Mask automatic detecting method, to improve the structure results from the motion reconstruction. Note that we define vehicle and guardrail regions as “mask” in this paper since the features on them should be masked out to avoid poor matches. After removing the feature points in our new method, the camera poses and sparse 3D points that are reconstructed with the remaining matches. Our contrast experiments with the typical pipeline of structure from motion (SfM) reconstruction methods, such as Photosynth and VisualSFM, demonstrated that the Mask decreased the root-mean-square error (RMSE) of the pairwise matching results, which led to more accurate recovering results from the camera-relative poses. Removing features from the Mask also increased the accuracy of point clouds by nearly 30%–40% and corrected the problems of the typical methods on repeatedly reconstructing several buildings when there was only one target building.
Abstract Phosphorus anodes are a promising for fast‐charging high‐energy lithium‐ion batteries because of their high specific capacity (2596 mAh g –1 ) and suitable lithiation potential (0.7 V vs Li + /Li). To solve the large volumetric change and inherent poor electrical conductivity, various carbon‐based materials have been studied for loading P. However, the local aggregation of Li ions and electrons in P particles especially in the fast‐charging process induces an uneven lithiation reaction and the great transient stress, leading to poor fast‐charging performance. Herein, bismuth nanoparticles are implanted into a P/graphite (P/C) composite using ball milling. The Bi anode works as a small Li reservoir for trapping Li in the lithiation process and emitting Li in delithiation process prior to P anode, because the Bi anode has a starting lithiation/delithiation potential that is a little bit higher/lower than the P anode. Moreover, the low Li diffusion barrier in Bi and the stable interface between Bi and P enhance the Li reservoir effect of Bi, which promotes fast and uniform lithiation/delithiation reactions and avoids continuous cracking of the Bi‐P/C electrode. Therefore, the Bi‐P/C anode provides a high fast‐charging capacity of 1755.7 mAh g –1 at 7.8 A g –1 (5.2 C) and a high capacity retention of 86.3% after 300 cycles.
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