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In the coastal areas of China, the eutrophication of seawater leads to the continuous occurrence of red tide, which has caused great damage to Marine fisheries and aquatic resources. Therefore, the detection and prediction of red tide have important research significance. The rapid development of optical remote sensing technology and deep-learning technology provides technical means for realizing large-scale and high-precision red tide detection. However, the difficulty of the accurate detection of red tide edges with complex boundaries limits the further improvement of red tide detection accuracy. In view of the above problems, this paper takes GOCI data in the East China Sea as an example and proposes an improved U-Net red tide detection method. In the improved U-Net method, NDVI was introduced to enhance the characteristic information of the red tide to improve the separability between the red tide and seawater. At the same time, the ECA channel attention mechanism was introduced to give different weights according to the influence of different bands on red tide detection, and the spectral characteristics of different channels were fully mined to further extract red tide characteristics. A shallow feature extraction module based on Atrous Spatial Pyramid Convolution (ASPC) was designed to improve the U-Net model. The red tide feature information in a multi-scale context was fused under multiple sampling rates to enhance the model's ability to extract features at different scales. The problem of limited accuracy improvement in red tide edge detection with complex boundaries is solved via the fusion of deep and shallow features and multi-scale spatial features. Compared with other methods, the method proposed in this paper achieves better results and can detect red tide edges with complex boundaries, and the accuracy, precision, recall, and F1-score are 95.90%, 97.15%, 91.53%, and 0.94, respectively. In addition, the red tide detection experiments in other regions with relatively concentrated distribution also prove that the method has good applicability. [ABSTRACT FROM AUTHOR]

Skilled seasonal forecasting will effectively reduce the economic losses caused by droughts and floods. Because of the powerful data mining capability of deep learning networks, it is increasingly applied in studies of seasonal rainfall prediction. However, there remain two prominent issues in the modeling process: the lack of enough training samples and the effect of a small number of extreme values on the model optimization. To tackle these deficiencies, we combine strategies such as principal component analysis, reduction of model hidden layers, and early‐stopping with Attention U‐Net to construct a rainfall classification forecasting model. These steps reduced the model outfitting and improved the model generalization. The results show that the prediction accuracy of this network with leads of 1–3 months is obviously better than that of the numerical model. Further analysis also supports that the spatial features of precipitation predicted by the network are very close to the observations. Plain Language Summary: Accurate summer precipitation forecasts can effectively reduce economic losses and casualties caused by heavy precipitation and flooding in China. Nowadays, seasonal forecasting still mainly relies on numerical models based on physical knowledge. However, the predictors of rainfall are so broad and complex that some physical processes are not yet fully understood. As a result, the improvement of the model skills encountered a bottleneck. Recently, deep learning (DL) network can achieve complex function approximations of data by automatically extracting and learning data features. Yet the existing DL seasonal prediction models suffer from over‐fitting and interference from noise and extreme values. Here, we use Attention U‐Net to predict rainfall levels and combine strategies such as principal component analysis, reduced model depth, and early‐stopping. This network not only solves the two drawbacks mentioned above. It also shows the superiority in the prediction results. First, the rainfall level prediction of this network is more accurate than the numerical model at 1–3 months ahead. Second, the network captures the spatial features with excellent performance. The predicted distributions are largely consistent with the true values. Key Points: The Attention U‐Net model outperforms the numerical model in predicting summer precipitation levels with a forecast lead of 1 monthConverting a regression task into a classification task can eliminate the bad effects of noises and extremesThe principal component analysis and the early‐stopping strategy facilitate the model's generalization [ABSTRACT FROM AUTHOR]

Due to the rapid increase in private car ownership in China, most cities face the problem of insufficient parking spaces, leading to frequent occurrences of parking space conflicts. There is a wide variety of parking locks available on the market. However, most of them lack advanced intelligence and cannot cater to the growing diverse needs of people. The present study attempts to devise a smart parking lock to tackle this issue. Specifically, the smart parking lock uses a Raspberry Pi as the core controller, senses the vehicle with an ultrasonic ranging module, and collects the license plate image with a camera. In addition, algorithms for license plate recognition based on traditional image-processing methods typically require a high pixel resolution, but their recognition accuracy is often low. Therefore, we propose a new algorithm called UNET-GWO-SVM to achieve higher accuracy in embedded systems. Moreover, we developed a WeChat mini program to control the smart parking lock. Field tests were conducted on campus to evaluate the performance of the parking locks. The test results show that the corresponding effective unlocking rate is 99.0% when the recognition error is less than two license plate characters. The average time consumption is controlled at about 2 s. It can meet real-time requirements. [ABSTRACT FROM AUTHOR]

Using deep learning in remote sensing interpretation could reduce a lot of human and material costs. Semantic segmentation is the main method for this task. It can automatically outline the objects and it has recently achieved great success in remote sensing images. However, in the appliance of remote sensing interpretation, the accuracy of contour largely determines the evaluation of remote sensing interpretation. Though the current loss functions reflect the segmentation performance, they could not guide the model to optimize itself toward a more precise contour. This paper proposed an exactly defined contour loss (CL) for remote sensing interpretation with Residual Attention U-Net (RA U-Net) as the main framework. The RA U-Net uses the residual attention module as the skip connection layer. It enhances the judgment of U-Net. In CL, image processing methods are used to extract the contours of the foreground. And elements-sum and elements-subtract operations are used to transfer the contour information to a matrix of the same size as label images. Then, these matrices would be the weights for CE. By assigning different weights for different elements in different regions, this function will guide the model to reach a balance between accurate segmentation results and precise contours. The experiment on open datasets shows a good performance. The proposed model was also trained on the Construction Disturbance Dataset collected from Jiang Xi Province, China. The dataset was labeled manually. The evaluation enhanced a lot on the Construction Disturbance Dataset and the IoU on two datasets increased 1 % to 2 % when using CL as the loss function. This paper also compared the proposed method with other state-of-the-art methods and the results showed extensive effectiveness. [ABSTRACT FROM AUTHOR]

Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems. [ABSTRACT FROM AUTHOR]

Green tide beaching events have occurred frequently in the Yellow Sea since 2007, causing a series of ecological and economic problems. Satellite imagery has been widely applied to monitor green tide outbreaks in open water. Traditional satellite sensors, however, are limited by coarse resolution or a low revisit rate, making it difficult to provide timely distribution of information about green tides in the nearshore. In this study, both PlanetScope Super Dove images and unmanned aerial vehicle (UAV) images are used to monitor green tide beaching events on the southern side of Shandong Peninsula, China. A deep learning model (VGGUnet) is used to extract the green tide features and quantify the green tide coverage area or biomass density. Compared with the U-net model, the VGGUnet model has a higher accuracy on the Super Dove and UAV images, with F1-scores of 0.93 and 0.92, respectively. The VGGUnet model is then applied to monitor the distribution of green tide on the beach and in the nearshore water; the results suggest that the VGGUnet model can accurately extract green tide features while discarding other confusing features. By using the Super Dove and UAV images, green tide beaching events can be accurately monitored and are consistent with field investigations. From the perspective of near real-time green tide monitoring, high-resolution imagery combined with deep learning is an effective approach. The findings pave the way for monitoring and tracking green tides in coastal zones, as well as assisting in the prevention and control of green tide disasters. [ABSTRACT FROM AUTHOR]

Accurately obtaining the multi-year spatial distribution information of crops combined with the corresponding agricultural production data is of great significance to the optimal management of agricultural production in the future. However, there are still some problems, such as low generality of crop type mapping models and susceptibility to cloud pollution in large-area crop mapping. Here, the models were constructed by using multi-phase images at the key periods to improve model generality. Multi-phase images in key periods masked each other to obtain large-area cloud-free images, which were combined with the general models to map large areas. The key periods were determined by calculating the global separation index (GSI) of the main crops (wheat, maize, sunflower, and squash) in different growth stages in the Hetao Irrigation District (HID) in China. The multi-phase images in the key period were used to make the data set and were then combined with a variety of deep learning algorithms (U-Net, U-Net++, Deeplabv3+, and SegFormer) to construct general models. The selection of the key periods, the acquisition of regional cloud-free images, and the construction of the general crop mapping models were all based on 2021 data. Relevant models and methods were respectively applied to crop mapping of the HID from 2017 to 2020 to study the generality of mapping methods. The results show that the images obtained by combining multi-phase images in the key period effectively avoided the influence of clouds and aerosols in large areas. Compared with the other three algorithms, U-Net had better mapping results. The F1-score, mean intersection-over-union, and overall accuracy were 78.13%, 75.39% and 96.28%, respectively. The crop mapping model was applied to images in 2020, and its average overall accuracy was more than 88.28%. When we applied the model to map crops (county food crops, cash crops, and cultivated land area) from 2017 to 2019, the regression analysis between the mapping areas obtained by the model and the ground measurements was made. The R2 was 0.856, and the RMSE was 17,221 ha, which reached the application accuracy, indicating that the mapping method has certain universality for mapping in different years. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the most important food crops in China, and it is of great significance to ensure national food security. The accurate extraction of wheat-growing areas is a prerequisite for growth assessments, stress monitoring, and yield assessments. In this study, GF-6 (8 m resolution) and Sentinel-2 (10 m resolution) remote sensing images were used to create datasets for the accurate extraction of winter-wheat growing areas by improving the U-Net model. First, U-Net was used as the base network to extract features, and then the convolutional block attention module (CBAM) was embedded in the basic convolutional units in the coding and decoding layers of the network to enhance or suppress the features to improve the feature-expression capability of the model, and to finally complete the end-to-end winter-wheat planting-area extraction. SegNet, DeepLabV3+, and U-Net-CBAM were selected as the comparison models, and they were tested using the test set in the Sentinel-2 dataset. The precision of the U-Net-CBAM model trained on the GF-6 dataset was 84.92%, the MIoU was 77.1%, the recall was 88.28%, the overall precision (OA) was 91.64%, and the F1 was 86.45%. For training on Sentinel-2 dataset, those values were: 90.06% for precision, 83.18% for MIoU, 90.78% for recall, 93.93% for OA, and 90.52% for F1, which showed significantly better results than those of the comparison models, indicating that U-Net-CBAM improved the accuracy of winter-wheat area extraction. It also showed that the segmentation performance of the training and test sets from different datasets was much lower than the segmentation performance from the same dataset. [ABSTRACT FROM AUTHOR]

Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due to the high noise of US images, even for experienced sonographers. Especially considering that maternal mortality remains high in many developing countries, a data-driven system with a medical diagnosis can help sonographers and obstetricians make decisions rapidly and improve their work efficiency. In this study, we propose a novel data-driven system based on deep learning for the diagnosis of CSP called CA-Unet, which consists of a channel attention network to segment the CSP and a post-processing module to measure and diagnose the anomalies of CSP. We collected the US data from three hospitals in China from 2012 to 2018 year to validate the effectiveness of our system. Experiments on a fetal US dataset demonstrated that our proposed system is able to help doctors make decisions and has achieved the highest precision of 79.5% and the largest Dice score of 77.5% in the segmentation of CSP. [ABSTRACT FROM AUTHOR]

Coastal salt marsh wetlands not only sequester a large amount of organic carbon, mitigating the effect of climate change, but also nurture rich wetland resources and diverse ecological environments. In this study, habitat pattern and quality of the Jiangsu Yancheng Wetland Rare Birds National Nature Reserve were studied. The evolution of habitat patterns was analyzed using the U-Net model and Sentinel-2 data. The habitat quality was evaluated using the InVEST model, while the future habitat pattern in 2027 under different scenarios were simulated using the PLUS model. Our results showed that, during 2017-2022, the Suaeda salsa habitat showed a net decrease in area of 2077.61 ha, while Spartina alterniflora and Phragmites australis habitats manifested a net increase in different degrees. The overall habitat pattern was characterized by fragmentation decline and regularization enhancement. The habitat quality decreased from 0.75 to 0.72, mainly due to the loss of the S. salsa habitat and the expansion of the P. australis habitat. The simulation results indicated that, the habitat quality is expected to further decline to 0.71 under the natural development scenario, and 390.27 ha of S. salsa habitat will convert to P. australis. While in government control scenario, the habitat quality is expected to improve to 0.78, which was 0.07 higher than that in natural development scenario, and S. salsa habitat can be restored well. This study provides a scientific basis for the protection of suitable habitats for waterfowl and is crucial for the ecological conservation and management planning of nature reserves and coastal salt marsh wetlands., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Over the past decade, deep learning frameworks such as convolutional neural networks (CNNs) have made major research inroads in upstream oil and gas, with applications in seismic processing/imaging, velocity model building, petrophysics, geological seismic interpretation, all the way to development, production and supply chain logistics. CNN fault prediction centers around the idea of image edge detection and, for improved prediction results, three data-driven steps are recommended. First, pre-condition the seismic data to increase signal-to-noise ratio as much as possible: iterative dip-steered median filtering and principal component filtering are adopted to further improve signal-to-noise ratio and sharpness for edge detection. Second, a fault probability volume obtained through deep learning (DL) -based fault detection using a U-Net architecture, taking synthetic seismic models as samples that can improve DL-based fault prediction in lieu of readily available labeled fault sets that may be prohibitively time-consuming to generate. Finally, edge enhancement is performed on the inference results to improve precision and fault continuity. A comparative analysis between related edge enhancement technologies is also presented. Results for three different faulting modes (normal, reverse and strike-slip) over three real seismic field data sets from China demonstrate the robustness of the proposed workflow. [ABSTRACT FROM AUTHOR]

Non-grain production has emerged as a potential threat to grain production capacity and security in China. Agricultural products with higher economic returns are beginning to replace traditional grain crops, which have relatively low economic returns on a large scale. In this study, we proposed and verified an identification method utilizing an unmanned aerial vehicle and a U-net algorithm to distinguish peach trees in cultivated land; the overall accuracy for verification and prediction were 0.90 and 0.92, respectively. Additionally, a non-grain production index was developed to assess the degree of non-grain production in target plots. The index was 76.90% and 91.38% in the projected plots, representing a high degree of non-grain production. This combination of an identification method and non-grain production index could provide efficient tools for agricultural management to inspect peach trees in cultivated land, thus replacing field measurements to achieve significant labor savings. Furthermore, this method can provide a reference for creating high-standard farmland, sustainable development of cultivated land, and policymaking. [ABSTRACT FROM AUTHOR]

Mapping water bodies with a high accuracy is necessary for water resource assessment, and mapping them rapidly is necessary for flood monitoring. Poyang Lake is the largest freshwater lake in China, and its wetland is one of the most important in the world. Poyang Lake is affected by floods from the Yangtze River basin every year, and the fluctuation of the water area and water level directly or indirectly affects the ecological environment of Poyang Lake. Synthetic Aperture Radar (SAR) is particularly suitable for large-scale water body mapping, as SAR allows data acquisition regardless of illumination and weather conditions. The two-satellite Sentinel-1 constellation, providing C-Band SAR data, passes over the Poyang Lake about five times a month. With its high temporal-spatial resolution, the Sentinel-1 SAR data can be used to accurately monitor the water body. After acquiring all the Sentinel-1 (1A and 1B) SAR data, to ensure the consistency of data processing, we propose the use of a Python and SeNtinel Application Platform (SNAP)-based engine (SARProcMod) to process the data and construct a Poyang Lake Sentinel-1 SAR dataset with a 10 m resolution. To extract water body information from Sentinel-1 SAR data, we propose an automatic classification engine based on a modified U-Net convolutional neural network (WaterUNet), which classifies all data using artificial sample datasets with a high validation accuracy. The results show that the maximum and minimum water areas in our study area were 2714.08 km 2 on 20 July 2020, and 634.44 km 2 on 4 January 2020. Compared to the water level data from the Poyang gauging station, the water area was highly correlated with the water level, with the correlation coefficient being up to 0.92 and the R 2 from quadratic polynomial fitting up to 0.88; thus, the resulting relationship results can be used to estimate the water area or water level of Poyang Lake. According to the results, we can conclude that Sentinel-1 SAR and WaterUNet are very suitable for water body monitoring as well as emergency flood mapping. [ABSTRACT FROM AUTHOR]

Marine oil spills cause pollution to the environment, where timely information on the oil characteristics (i.e., oil types, concentration, thickness) is essential for spill response and post-spill assessment. Existing remote sensing models (including deep-learning or DL) have been applied to both synthetic aperture radar (SAR) and optical remote sensing images, which are primarily for presence/absence detection. Here, we propose a framework to take advantage of data richness (i.e., multi-band observations under different viewing conditions) of optical sensors to characterize oil spills beyond presence/absence detection. The framework includes a 2-step DL model (F1-score ∼ 0.88) to account for the spectral-spatial-geometrical features of optical images to achieve the presence/absence detection, and a spectral model to characterize oil type and estimate oil quantity. The framework led to a relatively comprehensive "ground truth" dataset (N = 105 oil spill cases with 1616 oil features of each being at least 3 × 3 pixels) including all possible scenarios to account for various oil types and observing conditions, which can be used in the future for model development. Application of the framework to >25,000 HY-1C/D CZI images between December 2018 and April 2023 revealed substantial oil spills in the marginal seas off China, whose spatial distributions and oil characteristics (e.g., normalized concentration, thickness, and volume) were all quantified. The DL model also appears to be applicable to other Sentinel-1 SAR as well as to other optical sensors such as Landsat Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI). The use of multi-sensor data with the proposed approach over an oil spill event in the Gulf of Mexico shows improved temporal coverage as well as better knowledge on where most of the spilled oil can be found. • A framework to use optical remote sensing to characterize oil spills is proposed. • Oil presence can be determined by a DL model and spectral-spatial-viewing variables. • Oil type and quantity are estimated using the NIR and blue bands. • An optical dataset of various types of oil features is developed. • The approach has been used to characterize oil spills in marginal seas off China. [ABSTRACT FROM AUTHOR]

• A method to map Pistia stratiotes L. in dynamic urban water systems over time. • A combination of high-resolution UAV imagery and DL was used to map the IAPs. • The DeepLabv3 + model with ResNet-34 backbone achieved superior classification. • The distribution area of free-floating Pistia stratiotes L. can change rapidly. Despite water ecosystems being capable of sustaining biodiversity and enhancing the overall resilience of the urban environment, they are highly susceptible to biological invasions. Invasive aquatic plants (IAPs) threaten the natural environment by reducing the diversity of native aquatic plants and animal communities. Detecting IAPs and mapping their distribution is crucial for the protection of urban water ecosystems. This study is the first of its kind to use high-resolution unmanned aerial vehicle (UAV) imagery and deep learning approaches to monitor the expansion of Pistia stratiotes (water lettuce). A DJI Matrice 300 RTK was utilized to capture time-series images with a resolution of 0.018 m on the Guanyin Lake, Cangshan District, Fuzhzou, China, during an outbreak and subsequent rapid growth stage of water lettuce. Three deep learning model architectures and three backbones were combined to detect water lettuce. Model performance and the ability to generalize the model were evaluated to determine the optimal model for water lettuce detection from time-series high-resolution UAV imagery. Results show that the DeepLabv3 + model with ResNet-34 achieved superior performance in detecting water lettuce from time-series imagery, yielding an average accuracy of 90.24 % (85.33 %≤F1_score ≤ 96.54 %) for water lettuce detection on five different dates. For the UAV image acquired on September 26th, the U-Net model with ResNet-18 yielded the highest accuracy (F1_score = 92.46 %), but it was not the optimal model for multi-temporal water lettuce detection on subsequent dates. The distribution of water lettuce can have large variations at different times, with an average change rate of 49.50 % every two days and the highest change rate up to 60.33 %. The study demonstrates that the combination of UAV imagery and a deep learning model can achieve excellent accuracy for water lettuce monitoring and provide a method to map IAPs in dynamic urban water systems over time. [ABSTRACT FROM AUTHOR]

Monitoring the wind-induced lateral displacement (WLD) of the bridge deck is crucial for structural health monitoring (SHM) of suspension bridges. An accurate WLD prediction model can aid the bridge SHM systems in abnormal data detection and reconstruction, structural response estimation under specific wind events, and structural condition assessment. However, WLD prediction faces challenges due to stochastic wind action and complex aerodynamic effects acting on the bridge deck. To address this, a deep learning-based framework was proposed for predicting the WLD response of the suspension bridge deck. This framework decomposed the WLD response into two components, namely the quasi-static and the dynamic one. Two separate deep-learning tasks were employed to predict these components using the lateral wind speed as input. In Task 1, a recurrent neural network (RNN) based on the gated recurrent unit (GRU) was built, whereas a fully convolutional neural network (CNN) based on U-Net was built in Task 2. Novel loss functions tailored to each task were established to facilitate accurate predictions. Measured data from the SHM system of the Jiangyin Yangtze River Bridge, China, was used as a case study to verify the proposed predictive framework's feasibility and high accuracy. The extreme value-weighted loss function in Task 1 enhanced the prediction accuracy for the extreme quasi-static WLD, while the time-frequency cross-domain loss functions in Task 2 effectively integrated the prediction accuracies in both time and frequency domains for the dynamic component of WLD. However, trade-offs were identified between the prediction errors of extreme and non-extreme values, as well as between the time- and frequency-domain prediction accuracies. • A deep learning framework is proposed for predicting wind-induced lateral displacement (WLD) responses. • Extreme value-weighted loss functions improve the prediction accuracy for the extreme quasi-static WLD. • Cross-domain loss functions ensure both the time- and frequency-domain accuracy for the dynamic WLD. • Trade-offs exist between prediction errors of extreme and non-extreme values, as well as between time- and frequency-domain prediction accuracies. [ABSTRACT FROM AUTHOR]

We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net's up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net's up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors.

The expansion of power development industry is facing enormous pressure to reduce carbon emissions in the context of global decarbonization. Using solar energy instead of traditional fossil energy to adjust energy structure is one of the important means for reducing carbon emissions. Existing research focuses on the evaluation of the generation potential of centralized or distributed photovoltaic power plants, rather than the comprehensive evaluation of multi-type power plants. Based on multi-source remote sensing data for information extraction and suitability evaluation, this paper develops a method to comprehensively evaluate the construction potential of multi-type photovoltaic power stations and determine the potential of photovoltaic power generation and carbon emission reduction on the Qinghai-Tibet Plateau (QTP). The results showed that estimating the power generation potential of only single-type photovoltaic power stations cannot accurately reflect the photovoltaic power generation potential of QTP. It is also demonstrated that the emission reduction effect of the photovoltaic power generation in all prefecture-level cities of QTP can meet national emission reduction targets, showing high annual power generation potential, of which 86.59% is concentrated in Qinghai province's Guoluo, Yushu, and Haixi. An accurate estimation of the photovoltaic power generation potential in QTP can provide a useful theoretical basis for developing carbon-saving and emission reduction strategies for clean energy in China., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

• A defencing algorithm based on deep learning was proposed. • The algorithm based on U-Net and Pix2pixHD. • Chicken contours occlude by the wire mesh can be recovered. • The accuracy of caged chicken detection is improved after defencing. Cage farming is the mainstream farming mode in China. Accurate individual identification and behavioral detection of caged chickens can provide managers with a better understanding of chicken status. However, for image detection of caged chickens, the cage may affect the accuracy of the detection algorithm. For this reason, CCD (caged chicken defencing), a defencing algorithm based on U-Net and pix2pixHD, was proposed to improve caged chickens' detection accuracy. The proposed defencing algorithm can accurately identify the cage wire mesh and recover the chicken contours completely. In the test set, the detection accuracy of the cage wire mesh was 94.71%, while a structural similarity (SSIM) of 90.04% and a peak signal-to-noise ratio (PSNR) of 25.24 dB were obtained in the image recovery. To verify the practicality of the method proposed in this paper, we analyzed the performance of the object detection algorithm before and after defencing from the perspective of the most basic individual detection in the caged chicken detection task. We validated the defencing algorithm with different YOLOv5 detection algorithms, including YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The experimental results showed that the defencing algorithm improved the detection precision of caged chickens by 16.1%, 12.1%, 7.3%, and 5.4%, respectively, compared with before defencing. The recall improvement was 29.1%, 16.4%, 8.5%, and 6.8%. To our knowledge, this is the first time that a deep learning-based defencing algorithm has been applied to caged chickens, and the detection accuracy can be significantly improved. The method proposed in this paper can remove cage wire mesh greatly and provide a technical reference for subsequent poultry researchers. [ABSTRACT FROM AUTHOR]

As one of the important timber species in China, Cunninghamia lanceolata is widely distributed in southern China. The information of tree individuals and crown plays an important role in accurately monitoring forest resources. Therefore, it is particularly significant to accurately grasp such information of individual C. lanceolata tree. For high-canopy closed forest stands, the key to correctly extract such information is whether the crowns of mutual occlusion and adhesion can be accurately segmented. Taking the Fujian Jiangle State-owned Forest Farm as the research area and using the UAV image as the data source, we developed a method to extract crown information of individual tree based on deep learning method and watershed algorithm. Firstly, the deep learning neural network model U-Net was used to segment the coverage area of the canopy of C. lanceolata , and then the traditional image segmentation algorithm was used to segment the individual tree to obtain the number and crown information of individual tree. Under the condition of maintaining the same training set, validation set and test set, the extraction results of the canopy coverage area by the U-Net model and traditional machine learning methods [random forest (RF) and support vector machine (SVM)] were compared. Then, two individual tree segmentation results were compared, one using the marker-controlled watershed algorithm, and the other using the combination of the U-Net model and marker-controlled watershed algorithm. The results showed that the segmentation accuracy (SA), precision, IoU (intersection over union) and F1-score (harmonic mean of precision and recall) of the U-Net model were higher than those of RF and SVM. Compared with RF, the value of those four indicators increased by 4.6%, 14.9%, 7.6% and 0.05, respectively. Compared with SVM, the four indicators increased by 3.3%, 8.5%, 8.1% and 0.05, respectively. In terms of extracting the number of trees, the overall accuracy (OA) of the U-Net model combined with the marker-controlled watershed algorithm was 3.7% higher than that of the marker-controlled watershed algorithm, with the mean absolute error (MAE) being decreased by 3.1%. In terms of extracting crown area and crown width of individual tree, R 2 increased by 0.11 and 0.09, mean squared error decreased by 8.49 m 2 and 1.72 m, respectively. The combination of deep learning U-Net model and watershed algorithm could overcome the challenges in accurately extracting the number of trees and the crown information of individual tree of high-density pure 2 and 1.72 m, respectively. The combination of deep learning U-Net model and watershed algorithm could overcome the challenges in accurately extracting the number of trees and the crown information of individual tree of high-density pure C. lanceolata plantations. It was an efficient and low-cost method of extracting tree crown parameters, which could provide a basis for developing intelligent forest resource monitoring.

• The U-Net model to process GF-2 data showed great habitat extraction performance. • Suaeda salsa habitat decreased greatly after the Spartina alterniflora invasion. • GF-2 data were employed to analyze the habitat pattern. • New insights into the mechanisms of habitat competition in coastal wetlands. Spartina alterniflora (S. alterniflora) was introduced into China in the 1980s to control coastal erosion. However, it has significant negative impacts on coastal wetlands by encroaching on the habitat of native plant communities, which has seriously threatened coastal wetland function and biodiversity maintenance. The core area of the Migratory Bird Sanctuaries along the Coast of Yellow Sea-Bohai Gulf of China was taken as the study area and GF-2 remote sensing images from 2017, 2019 and 2020 were selected in this study. By using U-Net, this study extracted habitat information, identified the variation in habitat pattern, investigated interspecific competition and the characteristics of habitat type shifts. The results indicated that the U-Net model showed excellent classification performance, with the highest F1-score and MIoU. The habitat of S. alterniflora expanded and increased from 3,920 to 4,350 hm2 between 2017 and 2020. The habitat of Suaeda salsa (S. salsa) continuously fragmented and had been reduced to 1,414 hm2 by 2020. The degree of habitat fragmentation in the core area was strengthened and heterogeneity was enhanced. Moreover, while the competition between S. alterniflora and Phragmites australis (P. australis) was intensifying, both encroached on the habitat of S. salsa , which aggravated the reduction and fragmentation of the habitat of S. salsa, causing the loss of food and habitat of red-crowned cranes and further threatening biodiversity maintenance and ecosystem stability. These results can offer new insights on the habitat competitive mechanism among native plant communities and alien invasive species in the coastal wetland under global warming and anthropogenic influences. [ABSTRACT FROM AUTHOR]

• Redundant information of the imagery was removed by the feature selection method. • U-Net algorithm is more suitable for crop mapping in the irrigation district. • The key early window period of major crop mapping was determined. • The crop mapping models with strong versatility in the early period were obtained. The information on spatial distribution pattern and area of different crops is particularly useful for monitoring and managing the sustainability of agricultural resources. However, there are still challenges in timely mapping of crop types and planting areas to support production during the present growing season. Here, we proposed to use time-series Sentinel-2 imagery and a deep learning method for major crop mapping (wheat, maize, sunflower, and squash) in the agricultural production areas in the Hetao Irrigation District (HID), Inner Mongolia Autonomous Region, China. A feature selection method that combines the global separability index and feature recursive elimination was proposed to optimize the band features of Sentinel-2 imagery. The Random Forest, Extreme Gradient Boosting, U-Net and deeplabv3+ algorithms were then used for image classification with all spectral bands and the selected band features, respectively. The selected bands and best method were used to determine the key time window for the early identification of four major crops. Integrate all image datasets in the early window period, and build mapping models suitable for images at different times in the window period by combining the selected bands and best method. The results showed that the proposed feature selection method effectively removed redundant features and improved the classification efficiency. The U-Net algorithm was more suitable for the classification of major crops in HID with a higher accuracy whether using all band features or the selected features. The key periods for early recognition of wheat, maize, sunflower, and squash in HID were mid-May, late June, early July, and late June, respectively. The constructed models for crop type mapping in the early period were tested in 2020 and 2021 in the HID, and the F1-score, the mean intersection-over-union and overall accuracy were 86.71 %, 84.66 %, and 98.89 %, respectively. The proposed feature selection method, U-Net classification algorithm, and screening of the early mapping window period in this study provide an efficient, accurate and timely crop mapping tool in HID. Future research can further verify the applicability of the early identification time window proposed in this study to support other agricultural management activities in the areas. [ABSTRACT FROM AUTHOR]

Objective: This work conducts a comparative study on the effect of neural networks of different architectures on the detection of paroxysmal atrial fibrillation (PAF) events from dynamic electrocardiography (ECG) recordings, a problem raised in the 4th China Physiological Signal Challenge 2021 (CPSC2021)., Approach: We proposed 3 neural network models and an auxiliary one for QRS detection to tackle the problem. A convolutional recurrent neural network (CRNN) model and a U-Net model that accepts ECG waveform input make sample-wise predictions. This regards the PAF events detection as a segmentation task. A stacked bidirectional long short-term memory (LSTM) model takes the sequence of RR intervals, which is derived from the output of the QRS detection model and makes beat-wise predictions. The QRS detection model also has a CRNN architecture, which is slightly different from the model for the AF segmentation task. Final predictions are merged by outputs from models making sample-wise predictions and making beat-wise predictions. Finally, the locations of QRS complexes are used to filter out segments (both normal and AF) shorter than 5 beats. In order to make the neural network models more sensitive to the critical sample points (onsets and offsets) of the AF events, we proposed a novel masked binary cross-entropy (MaskedBCE) loss function for training the models. This loss function is the conventional BCE loss multiplied by a mask, whose values in a neighbourhood of critical sample points are significantly larger than elsewhere., Main Results: Our method received a score of 1.9972 on the first part of the hidden test set of CPSC2021 and a score of 3.0907 on the second part. The average score was 2.5440, ranked 5th out of 17 teams with successful official entries., Significance: This work proposed an effective solution to the problem of the detection of PAF events from dynamic ECGs and validated the efficacy of several neural network architectures on this problem., (Copyright © 2022 Elsevier Inc. All rights reserved.)

• Segmentation algorithms based on deep neural network achieve good results in medical images. • U-net used in medical imaging segmentation but the accuracy rate needs to be improved. • Propose Attention Enhanced U-net based on U-net to highlight salient features. • Apply three groups of HDC with expansion rates of [1,2,5] in AE U-net to reduce spatial information loss. Breast cancer poses a great threat on women health due to its high malignant rate. In China, ultrasound screening is the commonly-used method for breast cancer diagnosis, and the localization and segmentation of the lesions in ultrasound images are helpful for breast cancer detection. In this paper, an Attention Enhanced U-net with hybrid dilated convolution (AE U-net with HDC) model was proposed and employed to segment the breast tumors in ultrasound images. First, based on Attention U-net, we added a new loss function to update the weight matrix in the AGs module, in order to enhance the weight of the lesion area. Combined with fine-tuning training method, the precision of breast ultrasound image lesion region segmentation was improved from 82.38% to 86.28% and the M-IOU was improved from 76.27% to 81.81%. Second, three groups of HDC with expansion rates of [1,2,5] were integrated into AE U-net to replace the four convolution operations. HDC module brought larger receptive field and reduced the loss of spatial information. The experimental results proved that HDC module was helpful to improve the Acc of image segmentation results from 94.18% to 95.81% and the Recall from 78.69% to 80.48%. Combined with U-net, the F1 score, AUC, Acc and M-IOU of the network proposed in this paper had significantly improved. It proved that AE U-net with HDC model would have very important research value and application prospect for modern medicine. [ABSTRACT FROM AUTHOR]

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. [ABSTRACT FROM AUTHOR]

• Segmentation algorithms based on deep neural network achieve good results in medical images. • These algorithms need to be improved in breast tumor ultrasound image segmentation. • Only improve convolution module of the left encoding path in Attention U-net. • Combing mixed attention loss function and hierarchical training strategy improves tumor segmentation accuracy. Breast cancer poses a great threat on women health due to its high malignant rate. In China, ultrasound screening is the commonly-used method for breast cancer diagnosis, and the localization and segmentation of the lesions in ultrasound images are helpful for breast cancer detection. In this paper, an Improved U-net based on Mixed Attention Loss Function (Improved U-net MALF) model was proposed and employed to segment the breast tumors in ultrasound images. Firstly, based on the attention U-net network framework, the residual convolution module and extended residual convolution module were used to replace the convolution module of the coding path, so as to extract more detailed features of ultrasound breast tumor without increasing the computational cost. Secondly, on the basis of the traditional cross entropy loss function, four attention loss functions were integrated. The proportion of the four attention function loss values in the total loss value was measured by the texture consistency index of the feature map to further highlight the tumor target. The combined application of residual block and hybrid attention loss function improves the segmentation accuracy of breast tumor greatly. The results show that our model presented excellent segmentation results as compared to other Deep Networks when tested breast ultrasound images. A variety of quantitative indicators provided performances above 80 % including accuracy, specificity and sensitivity. It illustrates that the proposed Improved U-net MALF model can accurately segment breast lesions and thus has a good prospect for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Accurate and efficient extraction of cultivated land data is of great significance for agricultural resource monitoring and national food security. Deep-learning-based classification of remote-sensing images overcomes the two difficulties of traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) when extracting the cultivated land: (1) the limited performance when extracting the same land-cover type with the high intra-class spectral variation, such as cultivated land with both vegetation and non-vegetation cover, and (2) the limited generalization ability for handling a large dataset to apply the model to different locations. However, the "pooling" process in most deep convolutional networks, which attempts to enlarge the sensing field of the kernel by involving the upscale process, leads to significant detail loss in the output, including the edges, gradients, and image texture details. To solve this problem, in this study we proposed a new end-to-end extraction algorithm, a high-resolution U-Net (HRU-Net), to preserve the image details by improving the skip connection structure and the loss function of the original U-Net. The proposed HRU-Net was tested in Xinjiang Province, China to extract the cultivated land from Landsat Thematic Mapper (TM) images. The result showed that the HRU-Net achieved better performance (Acc: 92.81%; kappa: 0.81; F1-score: 0.90) than the U-Net++ (Acc: 91.74%; kappa: 0.79; F1-score: 0.89), the original U-Net (Acc: 89.83%; kappa: 0.74; F1-score: 0.86), and the Random Forest model (Acc: 76.13%; kappa: 0.48; F1-score: 0.69). The robustness of the proposed model for the intra-class spectral variation and the accuracy of the edge details were also compared, and this showed that the HRU-Net obtained more accurate edge details and had less influence from the intra-class spectral variation. The model proposed in this study can be further applied to other land cover types that have more spectral diversity and require more details of extraction. [ABSTRACT FROM AUTHOR]

China's rural population has declined markedly with the acceleration of urbanization and industrialization, but the area under rural homesteads has continued to expand. Proper rural land use and management require large-scale, efficient, and low-cost rural residential surveys; however, such surveys are time-consuming and difficult to accomplish. Unmanned aerial vehicle (UAV) technology coupled with a deep learning architecture and 3D modelling can provide a potential alternative to traditional surveys for gathering rural homestead information. In this study, a method to estimate the village-level homestead area, a 3D-based building height model (BHM), and the number of building floors based on UAV imagery and the U-net algorithm was developed, and the respective estimation accuracies were found to be 0.92, 0.99, and 0.89. This method is rapid and inexpensive compared to the traditional time-consuming and costly household surveys, and, thus, it is of great significance to the ongoing use and management of rural homestead information, especially with regards to the confirmation of homestead property rights in China. Further, the proposed combination of UAV imagery and U-net technology may have a broader application in rural household surveys, as it can provide more information for decision-makers to grasp the current state of the rural socio-economic environment. [ABSTRACT FROM AUTHOR]

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides. [ABSTRACT FROM AUTHOR]

Remote sensing has become a primary technology for monitoring raft aquaculture products. However, due to the complexity of the marine aquaculture environment, the boundaries of the raft aquaculture areas in remote sensing images are often blurred, which will result in 'adhesion' phenomenon in the raft aquaculture areas extraction. The fully convolutional network (FCN) based methods have made great progress in the field of remote sensing in recent years. In this paper, we proposed an FCN-based end-to-end raft aquaculture areas extraction model (which is called UPS-Net) to overcome the 'adhesion' phenomenon. The UPS-Net contains an improved U-Net and a PSE structure. The improved U-Net can simultaneously capture boundary and contextual information of raft aquaculture areas from remote sensing images. The PSE structure can adaptively fuse the boundary and contextual information to reduce the 'adhesion' phenomenon. We selected laver raft aquaculture areas in eastern Lianyungang in China as the research region to verify the effectiveness of our model. The experimental results show that compared with several state-of-the-art models, the proposed UPS-Net model performs better at extracting raft aquaculture areas and can significantly reduce the 'adhesion' phenomenon. [ABSTRACT FROM AUTHOR]