Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more effectively than traditional interpretation methods, which face challenges like high workloads and poor repeatability. In this study, we aim to enhance the inventorying of Agricultural Plastic Greenhouses (APGs) by improving the extraction accuracy of their locations and numbers through remote sensing techniques. Utilizing GF-7 satellite imagery, we propose an enhanced U-Net convolutional neural network (CNN) model that incorporates edge information expansion and a joint loss function to optimize performance. The primary objective is to provide a rapid and accurate method for mapping APGs, which is crucial for effective agricultural management and environmental monitoring. The U-Net network's accuracy was enhanced by 1.1% after expanding 3 x 3 sample edge information, and further by 1.9% by combining edge extension and loss function constraints. Our results demonstrate that the modified U-Net model significantly improves extraction accuracy compared to traditional methods, thereby facilitating better inventory management and planning for agricultural cash crops. This advancement not only supports farmers in optimizing resources but also contributes to sustainable agricultural practices by enabling precise monitoring of APG distribution.Implications: Compared to traditional interpretation methods, which suffer problems such as heavy workloads, small adaptation ranges and poor repeatability, deep learning network models can better extract target features from remote sensing images. In this study, we used GF-7 image data to improve the traditional U-Net convolutional neural network (CNN) model. The Canny operator and Gaussian kernel (GK) function were used for sample edge expansion, and the binary cross-entropy and GK functions were used to jointly constrain the loss. Finally, APGs were accurately extracted and compared to those obtained with the original model. The results indicated that the APG extraction accuracy of the U-Net network was improved through the expansion of sample edge information and adoption of joint loss function constraints.

Semantic segmentation of high-resolution remote sensing imagery is pivotal in decision-making and analysis in a wide array of sectors, including but not limited to water management, agriculture, military operations, and environmental protection. This technique offers detailed and precise feature information, facilitating an accurate imagery interpretation. Despite its importance, existing methods often fall short as they lack a mechanism for spatial location feature screening. These methods tend to treat all extracted features on an equal footing, neglecting their spatial relevance. To overcome these shortcomings, we introduce a groundbreaking approach, the Spatially Adaptive Interaction Network (SAINet), designed for dynamic feature interaction in remote sensing semantic segmentation. SAINet integrates a spatial refinement module that leverages local context information to filter spatial locations and extract prominent regions. This enhancement allows the network to concentrate on pertinent areas, thereby improving the quality of feature representation. Furthermore, we present an innovative spatial interaction module that utilizes a spatial adaptive modulation mechanism. This mechanism dynamically selects and allocates spatial position weights, fostering effective interaction between local salient areas and global information, which in turn boosts the network’s segmentation performance. The adaptability of SAINet allows it to capture more informative features, leading to a significant improvement in segmentation accuracy. We have validated the effectiveness and capability of our proposed approach through experiments on widely recognized public datasets such as DeepGlobe, Vaihingen, and Potsdam.

Maintenance and repair of expressways are becoming increasingly important due to the growing frequency of their use. Accurate pavement crack information extraction helps with routine maintenance and reduces the risk of traffic accidents. The traditional 2D crack image detection method has limitations and cannot effectively obtain depth information. Three-dimensional crack extraction from 3D point cloud has become a new solution that can capture pavement crack information more comprehensively and accurately. However, the existing algorithms are not effective in the feature extraction of cracks due to the different and irregular shapes and sizes of pavement cracks and interference from the external environment. To solve this, a new method for detecting pavement cracks in point clouds, namely point attention net (PAN), is herein proposed. It uses a two-branch attention fusion module to focus on space and feature information in the cloud and capture features of crack points at different scales. It also uses the Poly Loss function to solve the imbalance of foreground and background points in pavement point cloud data. Experiments on the LNTU-RDD-LiDAR dataset were carried out to verify the effectiveness of the proposed method. Compared with the traditional method and the latest point cloud segmentation technology, the performance indexes of mIoU, Acc, F1, and Rec achieved significant improvement, reaching 75.4%, 91.5%, 75.4%, and 67.1%, respectively.

The lack of large-scale, multi-scene, and multi-type pavement distress training data reduces the generalization ability of deep learning models in complex scenes, and limits the development of pavement distress extraction algorithms. Thus, we built the first large-scale dichotomous image segmentation (DIS) dataset for multi-type pavement distress segmentation, called ISTD-PDS7, aimed to segment highly accurate pavement distress types from natural charge-coupled device (CCD) images. The new dataset covers seven types of pavement distress in nine types of scenarios, along with negative samples with texture similarity noise. The final dataset contains 18,527 images, which is many more than the previously released benchmarks. All the images are annotated with fine-grained labels. In addition, we conducted a large benchmark test, evaluating seven state-of-the-art segmentation models, providing a detailed discussion of the factors that influence segmentation performance, and making cross-dataset evaluations for the best-performing model. Finally, we investigated the effectiveness of negative samples in reducing false positive prediction in complex scenes and developed two potential data augmentation methods for improving the segmentation accuracy. We hope that these efforts will create promising developments for both academics and the industry.

路面裂缝自动检测是评价道路状况的主要手段,现有的自动检测方法不能保持裂缝提取的连续性,为此文章提出了一种基于空间拓扑损失的路面裂缝连续提取方法.在该方法中,使用空间拓扑损失函数训练全卷积神经网络,以提升裂缝提取的连续性.文章在Crack500数据集中对裂缝提取网络进行训练和测试,分别以均方误差、Dice和空间拓扑作为损失函数提取路面裂缝.实验结果表明,组合使用均方误差和空间拓扑损失函数可以显著改善裂缝检测的拓扑结构,有效提升裂缝提取的连续性.

"地籍房产测量"存在教学内容陈旧、教学和评价方法单一等问题.针对这些问题,文章提出了一种课堂教学创新方法,从课程思政、学情反推、内容重构、教学方法和教学评价方面进行全面改革.教学实践表明,提出的创新方法从多角度形象化地展现了教学内容,产生了易于理解的表象特征,显著地提高了课程目标的达成度.

Deep convolutional neural networks have become a popular tool for the automatic detection of pavement cracks. Despite their widespread use, the models currently available tend to emphasize pixel-level classification accuracy for cracks, often overlooking the critical aspect of crack continuity. Addressing this gap, the authors' research introduces a new method for the continuous detection of ultrafine pavement cracks, centred around the concept of topological loss. The authors' novel approach hinges on expressing the disconnectivity between the background areas in an image through the connectivity of the cracks themselves. The proposed loss function accomplishes this by penalizing the unnecessary disconnection of the background areas, thereby minimizing the risk of false-positive crack predictions. In this study, the authors trained and tested a crack-detection network using the Crack500 dataset, as well as a new dataset specifically compiled for ultrafine crack analysis. The experimental outcomes indicate that the amalgamation of the mean squared error and the authors' novel spatial topological loss function leads to a substantial improvement in the topological structure of ultrafine crack detection.

ArcGIS中地理的处理可以通过Python脚本语言来完成.Python脚本语言结合ArcGIS地理工具,实现工作流自动地完成,可以在很大程度上简化数据生产的步骤,提高工作效率,进而保证数据质量.现以辽宁某地区的资产数据和路网数据,依据线性参考获得桩号为实践案例,介绍了在ArcGIS中利用Python批处理地理数据,提供了自动执行批量处理的解决方案[1].

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg-an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

Recent convolutional neural networks have made significant advancements in the detection of road cracks. However, the lack of accurate crack training data reduces the generalisation ability of the deep model. In this Letter, a semi-automatic pavement crack labelling algorithm is proposed to solve the problem of insufficient training data. First, the modified C-V model is used to obtain the preliminary segmentation results. Second, the direction of the initial segmentation area is calculated by the ellipse fitting method, and the preliminary segmentation results are used as samples for accurate labelling. Finally, a multi-scale feature extraction module is proposed for learning rich deep convolutional features, which allows the acquired crack features under a complex background to be more discriminant. The experimental results were compared with the manual marking method, and this method can achieve accurate marking of crack images with a low amount of interaction, thereby significantly reducing the cost of ground-truth making. The results of the validation and comparison experiments on test data sets indicate that the proposed method can not only effectively identify cracks, but also overcome the interference of many factors in the environment.

Pavement crack detection and characterization is a fundamental part of road intelligent maintenance systems. Due to the high non-uniformity of cracks, topological complexity, and similar noise from crack texture, the challenge arises in this domain with automated crack detection and classification in a complex environment. In this work, an overarching framework for a universal and robust automatic method that simultaneously characterizes the type of crack and its severity level was developed. For crack detection, we propose a novel and efficient crack detection network that captures the crack context information by establishing a multiscale dilated convolution module. On this foundation, an attention mechanism is introduced to further refine the high-level features. Moreover, the rich features at different levels are fused in an upsampling module to generate more detailed crack detection results. For crack classification, a novel characterization algorithm is developed to classify the type of crack after detection. The crack segment branches are then merged and classified into four types: transversal, longitudinal, block, and alligator; the severity levels of cracks are assessed by calculating the average width and distance between the crack branches. The proposed crack detection method effectively detects crack information in a complex environment, and achieves the current state-of-the-art accuracy. Compared to manual classification results, the classification accuracy of transversal and longitudinal cracks is higher than 95%, and the classification accuracy of block and alligator is above 86%.

The integration of stock data such as land and housing is one of the key issues to achieve accurate and efficient real estate registration and data submission. In this paper, we summarized the technical idea and key points of implementation for land and hous-ing stock data integration based on practical stock data integration projects we have completed. Our work has important practical signif-icance in several aspects, including standardizing the operation mode of stock data integration, reducing the business process risk, im-proving the efficiency of real estate registration and satisfying the public expectations.

Open pit slope displacement monitoring for mine safety production plays an important role in safeguarding.The process is a long-term work, monitoring period to determine the reasonable or not directly affect the current situation of monitoring data and hu-man and material resources.Based on the mine slope deformation analysis stage, deduce the different stages of the cycle is calculated, set a reasonable observation period, so that it can capture the shape information in a timely manner landslide, and more accurately predict the trend of sliding.Monitoring an Anshan Iron open a few years for the data source, this method for calculating the cycle of slope slip through the analysis and mining stage matches found.So by this method can be more rational, scientific monitoring cycle setting, avoid setting the previous cycle of blindness, mine monitoring guiding role.

Open pit slope monitoring to the mine safety production plays an important protective role. But At the present stage,the method of the slope monitoring are relatively simple and are all monitored on a single point. Monitoring results can not make a very good performance overall change of slope. Three-dimensional la-ser scanning technology can be measured for a total change in the slope of the displacement. In this paper,the technology was used in open pit Mine of Anshan slope monitoring project,and experiments show that the technology can be well demonstrated changes in the overall slope ,and it consistent with the actual changes in the slope.