Semi-FCMNet : semi-supervised learning for forest cover mapping from satellite imagery via ensemble self-training and perturbation

Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. To address this issue, this paper proposes a novel semi-supervised-learning-based semantic segmentation framework that leverages limited labelled and numerous unlabelled data, integrating multi-level perturbations and model ensembles. Our framework incorporates a multi-level perturbation module that integrates input-level, feature-level, and model-level perturbations. This module aids in effectively emphasising salient features from remote sensing (RS) images during different training stages and facilitates the stability of model learning, thereby effectively preventing overfitting. We also propose an ensemble-voting-based label generation strategy that enhances the reliability of model-generated labels, achieving smooth label predictions for challenging boundary regions. Additionally, we designed an adaptive loss function that dynamically adjusts the focus on poorly learned categories and dynamically adapts the attention towards labels generated during both the student and teacher stages. The proposed framework was comprehensively evaluated using two satellite RS datasets, showcasing its competitive performance in semi-supervised forest-cover-mapping scenarios. Notably, the method outperforms the fully supervised approach by 1–3% across diverse partitions, as quantified by metrics including mIoU, accuracy, and mPrecision. Furthermore, it exhibits superiority over other state-of-the-art semi-supervised methods. These results indicate the practical significance of our solution in various domains, including environmental monitoring, forest management, and conservation decision-making processes

Semi-supervised learning for forest fire segmentation using UAV imagery

Unmanned aerial vehicles (UAVs) are an efficient tool for monitoring forest fire due to its advantages, e.g., cost-saving, lightweight, flexible, etc. Semantic segmentation can provide a model aircraft to rapidly and accurately determine the location of a forest fire. However, training a semantic segmentation model requires a large number of labeled images, which is labor-intensive and time-consuming to generate. To address the lack of labeled images, we propose, in this paper, a semi-supervised learning-based segmentation network, SemiFSNet. By taking into account the unique characteristics of UAV-acquired imagery of forest fire, the proposed method first uses occlusion-aware data augmentation for labeled data to increase the robustness of the trained model. In SemiFSNet, a dynamic encoder network replaces the ordinary convolution with dynamic convolution, thus enabling the learned feature to better represent the fire feature with varying size and shape. To mitigate the impact of complex scene background, we also propose a feature refinement module by integrating an attention mechanism to highlight the salient feature information, thus improving the performance of the segmentation network. Additionally, consistency regularization is introduced to exploit the rich information that unlabeled data contain, thus aiding the semi-supervised learning. To validate the effectiveness of the proposed method, extensive experiments were conducted on the Flame dataset and Corsican dataset. The experimental results show that the proposed model outperforms state-of-the-art methods and is competitive to its fully supervised learning counterpart

TCDNet : tree crown detection from UAV optical images using uncertainty-aware one-stage network

Tree crown detection plays a vital role in forestry management, resource statistics and yields forecasting. RGB high-resolution aerial images have emerged as a cost-effective source of data for tree crown detection. To address the challenges in the detection using UAV optical images, we propose a one-stage object detection network, TCDNet. First, the network provides an attention enhancement feature extraction module to enable the model to distinguish between tree crowns and their complex backgrounds. Second, an efficient loss is introduced to enable it to be aware of the overlap between adjacent trees, thus effectively avoiding misdetection. The experimental results on two publicly available datasets show that the proposed network outperforms state-of-art networks in terms of precision, recall and mean average precision

A multiscale point-supervised network for counting maize tassels in the wild

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient. In this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild. Specifically, the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates, capturing rich contextual information at different scales more effectively. In addition, a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds. Finally, a new up-sampling module, UP-Block, is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process. Extensive experiments on 2 publicly available tassel-counting datasets, maize tassels counting and maize tassels counting from unmanned aerial vehicle, demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods. The model is publicly available at https://github.com/ShiratsuyuShigure/MLAENet-pytorch/tree/main

State Control and the Effects of Foreign Relations on Bilateral Trade

Do states use trade to reward and punish partners? WTO rules and the pressures of globalization restrict states’ capacity to manipulate trade policies, but we argue that governments can link political goals with economic outcomes using less direct avenues of influence over firm behavior. Where governments intervene in markets, politicization of trade is likely to occur. In this paper, we examine one important form of government control: state ownership of firms. Taking China and India as examples, we use bilateral trade data by firm ownership type, as well as measures of bilateral political relations based on diplomatic events and UN voting to estimate the effect of political relations on import and export flows. Our results support the hypothesis that imports controlled by state-owned enterprises (SOEs) exhibit stronger responsiveness to political relations than imports controlled by private enterprises. A more nuanced picture emerges for exports; while India’s exports through SOEs are more responsive to political tensions than its flows through private entities, the opposite is true for China. This research holds broader implications for how we should think about the relationship between political and economic relations going forward, especially as a number of countries with partially state-controlled economies gain strength in the global economy