The analysis of the relationship between regional resources and environment and human activities plays an important role in sustainable regional development. This study proposes the pressure-capacity-governance (PCG) model, an analytic framework for the assessment of the resources and environmental pressure (REP), carrying capacity (RECC) and governance (REG) levels over a large watershed scale, with the Yangtze River Economic Belt (YREB) as the study area. A limiting factor analysis is used to recognize the limiting factors of the regional RECC. The coupling analysis of resources and environmental pressure-capacity-governance identifies the regional potential and utilization direction. The research results are as follows. (1) The REP, RECC and REG levels of the YREB exhibit spatial differences. The REPs of the upper reaches are lower than those of the lower reaches, which does not match the RECC but matches the REG levels.(2) The proportions of unused land, water resources, and atmospheric environmental quality are the main limiting factors of the regional RECC. (3) The PCG analysis framework is used as the basis to divide the YREB into several subareas to analyse the resources and environmental potential carrying capacity and utilization direction of different types of region. This research may provide decision-making references for regional sustainable development at the large watershed scale.

To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model. The YOLOv3 showed a detection speed up to 27 FPS, indicating it is a real-time smoke detector. By comparing these algorithms with the current existing forest fire smoke detection algorithms, it can be found that the deep convolutional neural network algorithms result in better smoke detection accuracy. In particular, the EfficientDet algorithm achieves an average detection accuracy of 95.7%, which is the best real-time forest fire smoke detection among the evaluated algorithms.

Image pipelines arise frequently in modern computational photography systems and consist of multiple processing stages where each stage produces an intermediate image that serves as input to a future stage. Inspired by recent work on loop perforation [Sidiroglou-Douskos et al. 2011], this article introduces image perforation , a new optimization technique that allows us to automatically explore the space of performance-accuracy tradeoffs within an image pipeline. Image perforation works by transforming loops over the image at each pipeline stage into coarser loops that effectively “skip” certain samples. These missing samples are reconstructed for later stages using a number of different interpolation strategies that are relatively inexpensive to perform compared to the original cost of computing the sample. We describe a genetic algorithm for automatically exploring the resulting combinatoric search space of which loops to perforate, in what manner, by how much, and using which reconstruction method. We also present a prototype language that implements image perforation along with several other domain-specific optimizations and show results for a number of different image pipelines and inputs. For these cases, image perforation achieves speedups of 2 × --10 × with acceptable loss in visual quality and significantly outperforms loop perforation.

Temporal coherence is one of the central challenges for rendering a stylized line. It is especially difficult for stylized contours of coarse meshes or nonuniformly sampled models, because those contours are polygonal feature edges on the models with no continuous correspondences between frames. We describe a novel and simple technique for constructing a 2D brush path along a 3D contour. We also introduce a 3D parameter propagation and re-parameterization procedure to construct stroke paths along the 2D brush path to draw coherently stylized feature lines with a wide range of styles. Our method runs in real-time for coarse or non-uniformly sampled models, making it suitable for interactive applications needing temporal coherence.

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