Automated Extraction of Road Information from Mobile Laser Scanning Data

Effective planning and management of transportation infrastructure requires adequate geospatial data. Existing geospatial data acquisition techniques based on conventional route surveys are very time consuming, labor intensive, and costly. Mobile laser scanning (MLS) technology enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced point cloud data in the format of three-dimensional (3D) point clouds. Today, more and more commercial MLS systems are available for transportation applications. However, many transportation engineers have neither interest in the 3D point cloud data nor know how to transform such data into their computer-aided model (CAD) formatted geometric road information. Therefore, automated methods and software tools for rapid and accurate extraction of 2D/3D road information from the MLS data are urgently needed. This doctoral dissertation deals with the development and implementation aspects of a novel strategy for the automated extraction of road information from the MLS data. The main features of this strategy include: (1) the extraction of road surfaces from large volumes of MLS point clouds, (2) the generation of 2D geo-referenced feature (GRF) images from the road-surface data, (3) the exploration of point density and intensity of MLS data for road-marking extraction, and (4) the extension of tensor voting (TV) for curvilinear pavement crack extraction. In accordance with this strategy, a RoadModeler prototype with three computerized algorithms was developed. They are: (1) road-surface extraction, (2) road-marking extraction, and (3) pavement-crack extraction. Four main contributions of this development can be summarized as follows. Firstly, a curb-based approach to road surface extraction with assistance of the vehicle’s trajectory is proposed and implemented. The vehicle’s trajectory and the function of curbs that separate road surfaces from sidewalks are used to efficiently separate road-surface points from large volume of MLS data. The accuracy of extracted road surfaces is validated with manually selected reference points. Secondly, the extracted road enables accurate detection of road markings and cracks for transportation-related applications in road traffic safety. To further improve computational efficiency, the extracted 3D road data are converted into 2D image data, termed as a GRF image. The GRF image of the extracted road enables an automated road-marking extraction algorithm and an automated crack detection algorithm, respectively. Thirdly, the automated road-marking extraction algorithm applies a point-density-dependent, multi-thresholding segmentation to the GRF image to overcome unevenly distributed intensity caused by the scanning range, the incidence angle, and the surface characteristics of an illuminated object. The morphological operation is then implemented to deal with the presence of noise and incompleteness of the extracted road markings. Fourthly, the automated crack extraction algorithm applies an iterative tensor voting (ITV) algorithm to the GRF image for crack enhancement. The tensor voting, a perceptual organization method that is capable of extracting curvilinear structures from the noisy and corrupted background, is explored and extended into the field of crack detection. The successful development of three algorithms suggests that the RoadModeler strategy offers a solution to the automated extraction of road information from the MLS data. Recommendations are given for future research and development to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use

Effects of drought stress on the seed germination and early seedling growth of the endemic desert plant Eremosparton songoricum (Fabaceae)

Eremosparton songoricum (Litv.) Vass. is an endemic and extremely drought-resistant desert plant with populations that are gradually declining due to the failure of sexual recruitment. The effects of drought stress on the seed germination and physiological characteristics of seeds and seedlings were investigated. The results showed that the germination percentage decreased with an increase of polyethylene glycol 6000 (PEG) concentration: -0.3 MPa (5 % PEG) had a promoting effect on seed germination, -0.9 MPa (15 % PEG)dramatically reduced germination, and -1.8 MPa (30 % PEG) was the threshold for E. songoricum germination. However, the contents of proline and soluble sugars and the activity of CAT increased with increasing PEG concentrations. At the young seedling stage, the proline content and CAT, SOD and POD activities all increased at 2 h and then decreased; except for a decrease at 2 h, the MDA content also increased compared to the control (0 h). These results indicated that 2 h may be a key response time point for E. songoricum to resist drought stress. The above results demonstrate that drought stress can suppress and delay the germination of E. songoricum and that the seeds accumulate osmolytes and augment the activity of antioxidative enzymes to cope with drought injury. E. songoricum seedlings are sensitive to water stress and can quickly respond to drought but cannot tolerate drought for an extended period. Although such physiological and biochemical changes are important strategies for E. songoricum to adapt to a drought-prone environment, they may be, at least partially, responsible for the failure of sexual reproduction under natural conditions

Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection Under Domain Shift

Self-supervised learning methods have achieved promising performance for anomalous sound detection (ASD) under domain shift, where the type of domain shift is considered in feature learning by incorporating section IDs. However, the attributes accompanying audio files under each section, such as machine operating conditions and noise types, have not been considered, although they are also crucial for characterizing domain shifts. In this paper, we present a hierarchical metadata information constrained self-supervised (HMIC) ASD method, where the hierarchical relation between section IDs and attributes is constructed, and used as constraints to obtain finer feature representation. In addition, we propose an attribute-group-center (AGC)-based method for calculating the anomaly score under the domain shift condition. Experiments are performed to demonstrate its improved performance over the state-of-the-art self-supervised methods in DCASE 2022 challenge Task 2

Effects of Inoculants (Chlorobium limicola and Rhodopseudo-monas palustris) on Nutrient Uptake and Growth in Cucumber

Rhizobacteria is a prosperous for promoting plant growth for the superiority of reducing environmental damages. Two Strains of Chlorobium limicola and Rhodopseudomonas palustris were supplied in the experiment as potential inoculants for cucumber. Significant enhancement of the availability of macronutrient elements N, P and K were observed in soil, and further improvement on the uptake of them was also obtained in cucumber plants. Accumulation of essential micronutrients of Fe and Zn were detected both in roots and in shoots. The two stains increased chlorophyll and carotinoid synthesis, plant height, stem diameter, wet weight and dry weight. Various dose has significantly effect on plant growth stimulation, C. Limicola with 107 cells mL-1 and R. Palustris with 108 cells mL-1 seem to be better on the whole

SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery

Abstract With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor Earth surface changes at fine scale and in great detail. Thus, semantic change detection (SCD), which is capable of locating and identifying "from-to" change information simultaneously, is gaining growing attention in RS community. However, due to the limitation of large-scale SCD datasets, most existing SCD methods are focused on scene-level changes, where semantic change maps are generated with only coarse boundary or scarce category information. To address this issue, we propose a novel convolutional network for large-scale SCD (SCDNet). It is based on a Siamese UNet architecture, which consists of two encoders and two decoders with shared weights. First, multi-temporal images are given as input to the encoders to extract multi-scale deep representations. A multi-scale atrous convolution (MAC) unit is inserted at the end of the encoders to enlarge the receptive field as well as capturing multi-scale information. Then, difference feature maps are generated for each scale, which are combined with feature maps from the encoders to serve as inputs for the decoders. Attention mechanism and deep supervision strategy are further introduced to improve network performance. Finally, we utilize softmax layer to produce a semantic change map for each time image. Extensive experiments are carried out on two large-scale high-resolution SCD datasets, which demonstrates the effectiveness and superiority of the proposed method

Microbial resistance promotes plant production in a four-decade nutrient fertilization experiment

There is a current lack of mechanistic understanding on the relationships between a soil microbial community, crop production, and nutrient fertilization. Here, we combined ecological network theory with ecological resistance index to evaluate the responses of microbial community to additions of multiple inorganic and organic fertilizers, and their associations with wheat production in a 35-year field experiment. We found that microbial phylotypes were grouped into four major ecological clusters, which contained a certain proportions of fast-growers, copiotrophic groups, and potential plant pathogens. The application of combined inorganic fertilizers and cow manure led to the most resistant (less responsive) microbial community, which was associated with the highest levels of plant production, nutrient availability, and the lowest relative abundance of potential fungal plant pathogens after 35 years of nutrient fertilization. In contrast, microbial community was highly responsive (low resistance) to inorganic fertilization alone or plus wheat straw, which was associated with lower crop production, nutrient availability, and higher abundance of potential fungal plant pathogens. Our work demonstrates that the response of microbial community to long-term nutrient fertilizations largely regulates plant production in agricultural ecosystems, and suggests that manipulating these microbial phylotypes may offer a sustainable solution to the maintenance of field productivity under long-term nutrient fertilization scenarios. © 2019 The Author

Deep Learning for Plant Identification in Natural Environment

Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry

Abundance of kinless hubs within soil microbial networks are associated with high functional potential in agricultural ecosystems

Microbial taxa within complex ecological networks can be classified by their universal roles based on their level of connectivity with other taxa. Highly connected taxa within an ecological network (kinless hubs) are theoretically expected to support higher levels of ecosystem functions than less connected taxa (peripherals). Empirical evidence of the role of kinless hubs in regulating the functional potential of soil microbial communities, however, is largely unexplored and poorly understood in agricultural ecosystems. Here, we built a correlation network of fungal and bacterial taxa using a large-scale survey consisting of 243 soil samples across functionally and economically important agricultural ecosystems (wheat and maize); and found that the relative abundance of taxa classified as kinless hubs within the ecological network are positively and significantly correlated with the abundance of functional genes including genes for C fixation, C degradation, C methanol, N cycling, P cycling and S cycling. Structural equation modeling of multiple soil properties further indicated that kinless hubs, but not provincial, connector or peripheral taxa, had direct significant and positive relationships with the abundance of multiple functional genes. Our findings provide novel evidence that the relative abundance of soil taxa classified as kinless hubs within microbial networks are associated with high functional potential, with implications for understanding and managing (through manipulating microbial key species) agricultural ecosystems at a large spatial scale

Efficacy and safety of different doses of epidural morphine coadministered with low-concentration ropivacaine after cesarean section: A retrospective cohort study

Objective: The optimal dose of epidural morphine after cesarean section (CS) still remains unknown when combined with low-concentration ropivacaine based on a continuous basal infusion (CBI) mode. The aim of this study was to assess the impact of different dose of epidural morphine plus ropivacaine on maternal outcomes.Materials and methods: Data of parturients who received epidural analgesia for CS at a teaching hospital from March 2021 to June 2022 were retrospectively collected. Parturients were divided into two groups (RM3 group and RM6 group) according to different medication regimens of morphine. The implementation of epidural analgesia was performed with 3 mg morphine in RM3 group and 6 mg morphine in RM6 group in combination with 0.1% ropivacaine via a CBI pump. The primary outcomes included pain intensity at rest and movement and the incidence of urinary retention and pruritus within postoperative 48 h. The secondary outcomes included the incidence and severity of postoperative nausea and vomiting (PONV) and pruritus, the rate of rescue analgesia and grading of motor Block.Results: Totally, 531 parturients were eligible for the final analysis, with 428 and 103 parturients in the RM3 group and RM6 group, respectively. There were no statistically significant differences in the visual analogue scores (VAS) at rest and movement within postoperative 48 h between the two groups (all p > 0.05). Compared with the RM6 group, the incidence of urinary retention was lower in the RM3 group within 48 h after CS (4.0% vs. 8.7%, p = 0.044). No significant difference was found in the incidence and severity of PONV and pruritus, the rate of rescue analgesia and grading of motor block between RM3 and RM6 groups.Conclusion: Epidural 3 mg morphine plus 0.1% ropivacaine in a CBI mode can provide equal efficacy and have lower incidence of urinary retention compared with 6 mg morphine after CS