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Foreign objects on power transmission lines carry a significant risk of triggering large-scale power interruptions which may have serious consequences for daily life if they are not detected and handled in time. To accurately detect foreign objects on power transmission lines, this paper proposes a TL-Yolo method based on the Yolov8 framework. Firstly, we design a full-dimensional dynamic convolution (ODConv) module as a backbone network to enhance the feature extraction capability, thus retaining richer semantic content and important visual features. Secondly, we present a feature fusion framework combining a weighted bidirectional feature pyramid network (BiFPN) and multiscale attention (MSA) module to mitigate the degradation effect of multiscale feature representation in the fusion process, and efficiently capture the high-level feature information and the core visual elements. Thirdly, we utilize a lightweight GSConv cross-stage partial network (GSCSP) to facilitate efficient cross-level feature fusion, significantly reducing the complexity and computation of the model. Finally, we employ the adaptive training sample selection (ATSS) strategy to balance the positive and negative samples, and dynamically adjust the selection process of the training samples according to the current state and performance of the model, thus effectively reducing the object misdetection and omission. The experimental results show that the average detection accuracy of the TL-Yolo method reaches 91.30%, which is 4.20% higher than that of the Yolov8 method. Meanwhile, the precision and recall metrics of our method are 4.64% and 3.53% higher than those of Yolov8. The visualization results also show the superior detection performance of the TL-Yolo algorithm in real scenes. Compared with the state-of-the-art methods, our method achieves higher accuracy and speed in the detection of foreign objects on power transmission lines. [ABSTRACT FROM AUTHOR]

Semi-supervised object detection has become a hot topic in recent years, but there are still some challenges regarding false detection, duplicate detection, and inaccurate localization. This paper presents a semi-supervised object detection method with multi-scale regularization and bounding box re-prediction. Specifically, to improve the generalization of the two-stage object detector and to make consistent predictions related to the image and its down-sampled counterpart, a novel multi-scale regularization loss is proposed for the region proposal network and the region-of-interest head. Then, in addition to using the classification probabilities of the pseudo-labels to exploit the unlabeled data, this paper proposes a novel bounding box re-prediction strategy to re-predict the bounding boxes of the pseudo-labels in the unlabeled images and select the pseudo-labels with reliable bounding boxes (location coordinates) to improve the model's localization accuracy based on its unsupervised localization loss. Experiments on the public MS COCO and Pascal VOC show that our proposed method achieves a competitive detection performance compared to other state-of-the-art methods. Furthermore, our method offers a multi-scale regularization strategy and a reliably located pseudo-label screening strategy, both of which facilitate the development of semi-supervised object detection techniques and boost the object detection performance in autonomous driving, industrial inspection, and agriculture automation. [ABSTRACT FROM AUTHOR]

In the last decade, the spatio-temporal patterns of PM2.5 on various scales, ranging from global, continent, and country to regional levels, has been the focus of considerable studies. However, these studies on spatio-temporal variability have concentrated primarily on changes in the spatial distribution patterns of regional PM2.5 concentrations and ignored temporal characteristics at a local site from a heterogeneous surface, such as local variability, persistence, and stability of PM2.5 exposure. Understanding the temporal characteristics of PM2.5 concentration changes at the local scale will help determine the local impacts of PM2.5, such as local exposure risk and vulnerability to PM2.5. This study aims to reveal the local characteristics of temporal variation at the scale of a prefecture-level city and its distinct-varying patterns from those at the provincial scale by using the annual satellite-derived PM2.5 concentration product from 2000 to 2015. The evolutionary trends, stability, and persistence of annual changes were discovered with a set of time series analysis methods, such as linear regression analysis + F-test, coefficient of variation method, and Hurst index. This study uses PM2.5 product data for a total of 16 years, from 2000 to 2015, and uses time series analysis methods, such as Theil–Sen median trend analysis + Mann–Kendall test, one-dimensional linear regression analysis + F-test, coefficient of variation method, and Hurst index, to reveal the temporal variation characteristics and spatial patterns of PM2.5 in Jiangsu Province. The results show that the increasing trends or slopes of annual averaged PM2.5 concentrations in Jiangsu Province are not consistent at the prefecture-level city scale, but they are consistent in northern, central and southern Jiangsu at a larger upward trend since 2000. The areas with significant increasing trends are concentrated in Xuzhou and Lianyungang and other northern cities. From the viewpoint of variability, the areas in medium and high variability are mainly aggregated in the areas north of the Yangtze River. According to the combination of persistence analysis and trend analysis, future variation in PM2.5 concentrations indicates an inverse persistence for an increasing trend, meaning the air quality decline in Jiangsu will slow. [ABSTRACT FROM AUTHOR]

This paper is devoted to detect and classify intra-urban changes by jointly exploiting Sentinel-1 (S-1) SAR data and nighttime light data. By extracting urban extents and urban density maps from SAR data, changes in nighttime lights can be used to detect changes related to the level of activity in a specific portion of each urban areas. At the same time, changes in radar backscattering are prone to reveal changes in the two- and three-dimensional structures of the built-up. The combination of these multimodal datasets has already proved to be useful to discriminate urban change patterns at the city level. In this paper, instead, SAR datasets from S-1 are exploited, allowing the recognition of different intra-urban changes. Experimental results focus on fast growing (mega) cities in East Asia, allowing us to understand in a more detailed way how they are changing and evolving in all three dimensions. Examples for Nanjing, Shanghai, and Guangzhou (China), Saigon (Vietnam), and Vientiane (Laos) are discussed to prove this statement. [ABSTRACT FROM AUTHOR]

In this letter, an unsupervised 2-D and 3-D urban change detection scheme is proposed exploiting Quad-PolSAR data. Changes are extracted by segmenting the data into superpixels, to enhance the balance among change components and increase estimability of prior distributions. Positive and negative change components for built-up areas, in both the horizontal and the vertical directions, are properly extracted by assuming a multivariate Gaussian mixed model applied to a subset of polarimetric parameters at the superpixel level. The proposed method is tested on multitemporal Quad-PolSAR images and the results confirm its effectiveness. The selection of polarimetric decomposition measures that are most useful to the task is also experimentally justified. [ABSTRACT FROM PUBLISHER]

Feature representation is a classic problem in the machine learning community due to the fact that different representations can entangle and hide more or less the different explanatory factors of variation behind the raw data. Especially for scene classification, its performance generally depends on the discriminative power of feature representation. Recently, unsupervised feature learning attracts tremendous attention because of its ability to learn feature representation automatically. However, reliable performance of feature representations by unsupervised learning always requires a large number of features and complex framework of mid-level feature representation. To alleviate such drawbacks, this paper presents a new framework of mid-level feature representation, which does not need learn many convolutional features during the unsupervised feature learning process, and has few parameter settings. In detail, the unsupervised feature learning method, sparse autoencoder, is employed to learn relatively small number of convolutional features from input dataset, and then extended features are extracted from the learned features by a multiple normalized difference features extraction method to compose a derivative feature set. At mid-level feature representation stage, in order to avoid poor performance of standard pooling technology in solving problems brought by rotation and translation of scene images, global feature descriptors (histogram moments, mean, variance, standard deviation) are utilized to build mid-level feature representations of images. For validation and comparison purposes, the proposed approach is evaluated via experiments with two challenging high-resolution remote sensing datasets. The results demonstrate that the approach is effective, and shows strong performance for remotely sensed scene classification. [ABSTRACT FROM PUBLISHER]

Due to the lack of clear shape, texture characteristics, and abundant spectral or spatial information of urban objects, traditional per-/sub-pixel analysis and interpretation for moderate-resolution-remote sensing data are always confused by such shortcomings as dependence on special skills, requirements to a priori knowledge and training samples, complex process, time-consuming and subjective operations, etc.. In order to alleviate such disadvantages, an automatic approach is proposed to classify vegetation, water, impervious surface areas (dark and bright), and bare land from the Operational Land Imager (OLI) sensor data of Landsat-8 in urban areas, which can be employed by common users to automatically obtain land-cover maps for urban applications. In detail, a preliminary classification result is achieved based on a new vegetation and water masking index (VWMI), the normalized difference vegetation index (NDVI), and a new normalized difference bare land index (NDBLI), which are acquired automatically from the remote-sensing images based on available knowledge of spectral properties. VWMI is designed to extract vegetation and water information together with a simpler threshold, while NDBLI is developed to identify dark impervious surfaces and bare land in this work. A modification strategy is further proposed to improve preliminary classification results by a linear model. For this purpose, a stable sample selection method based on the histogram is developed to select training samples from the preliminary classification result and to build a non-linear support vector machine (SVM) model to reclassify the classes. For validation and comparison purposes, the proposed approach is evaluated via experiments with real OLI data of two study areas, Nanjing and Ordos. The results demonstrate that the approach is effective in automatically obtaining urban land-cover classification maps from OLI data for thematic analysis. [ABSTRACT FROM PUBLISHER]

Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998-2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35 μg/m3 significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran's I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors. [ABSTRACT FROM AUTHOR]