Cross-scale Urban Land Cover Mapping: Empowering Classification through Transfer Learning and Deep Learning Integration

Urban land cover mapping is essential for effective urban planning and resource management. Thanks to its ability to extract intricate features from urban datasets, deep learning has emerged as a powerful technique for urban classification. The U-net architecture has achieved state-of-the-art land cover classification performance, highlighting its potential for mapping urban trees at different spatial scales. However, deep learning approaches often require large, labeled datasets, which are challenging to acquire for specific urban contexts. Transfer learning addresses this limitation by leveraging pre-trained deep learning models on extensive datasets and adapting them to smaller urban datasets with limited labeled samples. Transfer learning can enhance classification performance and generalization ability. In this study, we proposed a novel cross-scale framework that integrates transfer learning and deep learning for urban land cover mapping. The framework utilizes pre-trained deep learning models, trained on diverse urban datasets, as a foundation for classification. These models are then finetuned using transfer learning techniques on smaller urban datasets, tailoring them to the specific characteristics of the target urban context. To evaluate the effectiveness and feasibility of the proposed framework, extensive evaluations are conducted across different cities and years. Performance metrics such as accuracy and dice score are employed to assess the framework\u27s classification capabilities. The results of this study contribute to advancing the field of urban classification by demonstrating the effectiveness and feasibility of the cross-scale framework. By combining transfer learning and deep learning, the framework improves classification accuracy, efficiency, and scalability in urban land cover mapping tasks. Leveraging the strengths of transfer learning and deep learning holds great promise for accurate and efficient urban land cover mapping, providing valuable insights for urban planning and resource management decision-making

Comparison of rumen bacteria distribution in original rumen digesta, rumen liquid and solid fractions in lactating Holstein cows

Microbial diversity in different fractions of rumen content. a, the OTU numbers in original, solid or liquid fraction samples. b, Chao1 index in original, solid or liquid fraction samples. c, Simpson index based on OTUs in original, solid, and liquid fraction samples. HFD: High fiber diet; HED: High energy diet. Data are presented as Mean ± SD. Figure S2. Analysis of similarity (ANOSIM) in different groups. ANOSIM results are presented with box plot when bacteria communities are grouped by diet (a), cows (b), and ruminal content fractions (c) using Bray-Curtis metric based on OTUs. Figure S3. Venn plot for shared OTUs. a, OTUs in HFD and HED. b, OTUs in original, solid and liquid fractions. Figure S4. Ruminal bacteria change in different fractions of rumen content at genera level. LEfSe histogram demonstrating taxonomic differences among different fractions in HFD group (a) and HED group (b) respectively, LDA scores above 2 and P value smaller than 0.05 were shown. LEfSe: linear discriminant analysis (LDA) effect size. Figure S5. Influence of rumen fractions on biomarker taxa abundance. p_: phylum; c_: class; o_: order; f_: family; g_: genus. Data was presented as Mean ± SD. Figure S6. Predominant rumen bacteria at genera level. a, predominant genera higher than 1% in proportion in all samples. b, distribution of predominant genera in each fractions. (DOC 1371 kb

Current views of drought research: experimental methods, adaptation mechanisms and regulatory strategies

Drought stress is one of the most important abiotic stresses which causes many yield losses every year. This paper presents a comprehensive review of recent advances in international drought research. First, the main types of drought stress and the commonly used drought stress methods in the current experiment were introduced, and the advantages and disadvantages of each method were evaluated. Second, the response of plants to drought stress was reviewed from the aspects of morphology, physiology, biochemistry and molecular progression. Then, the potential methods to improve drought resistance and recent emerging technologies were introduced. Finally, the current research dilemma and future development direction were summarized. In summary, this review provides insights into drought stress research from different perspectives and provides a theoretical reference for scholars engaged in and about to engage in drought research

A Deformable Configuration Planning Framework for a Parallel Wheel-Legged Robot Equipped with Lidar

The wheel-legged hybrid robot (WLHR) is capable of adapting height and wheelbase configuration to traverse obstacles or rolling in confined space. Compared with legged and wheeled machines, it can be applied for more challenging mobile robotic exercises using the enhanced environment adapting performance. To make full use of the deformability and traversability of WHLR with parallel Stewart mechanism, this paper presents an optimization-driven planning framework for WHLR with parallel Stewart mechanism by abstracting the robot as a deformable bounding box. It will improve the obstacle negotiation ability of the high degree-of-freedoms robot, resulting in a shorter path through adjusting wheelbase of support polygon or trunk height instead of using a fixed configuration for wheeled robots. In the planning framework, we firstly proposed a pre-calculated signed distance field (SDF) mapping method based on point cloud data collected from a lidar sensor and a KD -tree-based point cloud fusion approach. Then, a covariant gradient optimization method is presented, which generates smooth, deformable-configuration, as well as collision-free trajectories in confined narrow spaces. Finally, with the user-defined driving velocity and position as motion inputs, obstacle-avoidancing actions including expanding or shrinking foothold polygon and lifting trunk were effectively testified in realistic conditions, demonstrating the practicability of our methodology. We analyzed the success rate of proposed framework in four different terrain scenarios through deforming configuration rather than bypassing obstacles

Preparation, microstructure and tensile properties of two dimensional MXene reinforced copper matrix composites

The dispersion homogeneity of particles has a significant influence on the mechanical properties of particle reinforced metal matrix composites. In this study, a new Cu matrix composites with uniform dispersion of submicron and nanoscale MXene reinforcement particles were fabricated by the methods of high energy ball milling and hot pressing sintering. The microstructure evolution of MXene-Cu composite powders were studied in detail. When the initial MXene content is less than 3 vol%, the MXene particles can be dispersed homogenously in Cu matrix after 12 h ball milling. The refinement and dispersion of the MXene particles are mainly achieved by the cold welding and plastic deformation of composite particles in ball milling process. The XRD and TEM results reveal that these MXene reinforcement particles have been transformed to cubic TiCx in the final MXene/Cu composites. The tensile test results indicate that the UTS and elongation of the MXene/Cu composites are closely related to the dispersion homogeneity of the MXene particles. The UTS and elongation of the 1MXene/Cu and 3MXene/Cu composites increase with increasing the ball milling time. The UTS of the 3MXene/Cu-12 h can reach to 314 MPa with an elongation of 11.1%. The maximum UTS of the 5MXene/Cu-6 h can reach to 354 MPa. However, further increase in milling time would result in the UTS and elongation decline. The fracture surface analysis further testifies that the 3MXene/Cu-12 h composite presents a typical plastic fracture feature, while the 5MXene/Cu composite with various ball milling time all present brittle fracture characteristic for the severe agglomeration of MXene particles

Interactive segmentation in aerial images: a new benchmark and an open access web-based tool

Deep learning has gradually become powerful in segmenting and classifying aerial images. However, in remote sensing applications, the lack of training datasets and the difficulty of accuracy assessment have always been challenges for the deep learning based classification. In recent years, interactive semantic segmentation proposed in computer vision has achieved an ideal state of human-computer interaction segmentation. It can provide expert experience and utilize deep learning for efficient segmentation. However, few papers discussed its application in remote sensing imagery. This study aims to bridge the gap between interactive segmentation and remote sensing analysis by conducting a benchmark study on various interactive segmentation models. We assessed the performance of five state-of-the-art interactive segmentation methods (Reviving Iterative Training with Mask Guidance for Interactive Segmentation (RITM), FocalClick, SimpleClick, Iterative Click Loss (ICL), and Segment Anything (SAM)) on two high-resolution aerial imagery datasets. The Cascade-Forward Refinement approach, an innovative inference strategy for interactive segmentation, was also introduced to enhance the segmentation results. We evaluated these methods on various land cover types, object sizes, and band combinations in the datasets. SimpleClick model consistently outperformed the other methods in our experiments. Conversely, the SAM performed less effectively than other models. Building upon these findings, we developed an online tool called RSISeg for interactive segmentation of remote sensing data. RSISeg incorporates a well-performing interactive model that is finetuned with remote sensing data. Compared to existing interactive segmentation tools, RSISeg offers robust interactivity, modifiability, and adaptability to remote sensing data

Microstructure and tensile properties of Ni nano particles modified MXene reinforced copper matrix composites

The wettability of reinforcement particles with metal matrix plays a vital role in particles dispersion in matrix and their interfacial bond. In this study, the Ni-MXene hybrids were firstly fabricated by molecular-level mixing and chemical reduction. Then the Ni-MXene-Cu composite powders were prepared by high energy ball milling, and the Ni-MXene/Cu composites were further prepared by hot pressing the composite powders. The wettability of MXene and Ni-MXene with Cu matrix was studied by the Cu diffusion phenomena at the interface. The study results indicate that the initial MXene has a poor wettability with Cu matrix because of the surface functional groups, while the Ni-MXene can wet well with the matrix by interdiffusion of Cu and Ni elements at the interface. The good wettability promotes Cu matrix diffuse to the inside of the agglomerated MXene particles, and thus to effectively improve the tensile properties of the composites. Both the UTS and the elongation of the Ni-MXene/Cu composites are obvious higher than those of MXene/Cu composites, especially in the composites with lower milling time. The maximum UTS and elongation of the 3(Ni-MXene)/Cu-12 h can reach to 325 MPa and 16.1%, respectively. The tensile properties and the failure mechanism were further analyzed and discussed by comparing the fracture surface morphologies