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"Landscape design Data processing."
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The mechanism of digitized landscape architecture design under edge computing
The purposes are to analyze the mechanism of digitized landscape architecture design and stablize the garden landscape image display in constructing garden landscape digitization platform. According to previous research and mobile edge computing, a scheme of digitized landscape architecture design is proposed based on edge computing. This scheme uses discrete elevation calculation to preserve the landscape design image’s frame. It adopts the Roberts edge detection and Laplacian operator for high-level stable preservation of landscape images. Simultaneously, the displayed image is stablized using edge computing algorithms. Simulation experiments are performed to verify the effectiveness of the proposed scheme of digitized landscape architecture design based on mobile edge computing. Results demonstrate that the discrete elevation calculation algorithm can avoid low visual rendering in the 3D image generation process, optimize the seed point matching of edge correlation, and ensure image clarity and stability. The edge computing algorithm can fundamentally avoid the problem of image shaking. The impact of different algorithm models on the classification and accuracy of landscape images is analyzed through parameter optimization. Compared with some latest models, the proposed landscape design scheme based on edge computing has better accuracy. The average accuracy can reach more than 90%, and the Kappa coefficient remains at 86.93%. The designed garden landscape digitization platform can stably display 3D garden landscape images while avoiding the shaking of 3D images, which can provide a theoretical basis and practical value for designing and planning landscape architecture.
Journal Article
Multi-modal deep learning for intelligent landscape design generation: A novel CBS3-LandGen model
With the acceleration of the global urbanization process, landscape design is facing increasingly complex challenges. Traditional manual design methods are gradually unable to meet the needs for efficiency, precision, and sustainability. To address this issue, this paper proposes an intelligent landscape design generation model based on multimodal deep learning, namely CBS3-LandGen. By integrating image data, text data, and generation optimization techniques, this model can generate landscape plans that meet the design objectives within limited time and resources.Specifically, the model employs the ConvNeXt network to process image data, uses the BART model to analyze text information, and optimizes the generation effect through StyleGAN3. This multimodal architecture enables the model to perform excellently in terms of image generation quality, text generation consistency, and the fusion of images and text. In the experiments, we trained and tested the model using the DeepGlobe and COCO datasets. The results show that on the DeepGlobe dataset, the Frechet Inception Distance (FID) is 25.5 and the Inception Score (IS) is 4.3; on the COCO dataset, the FID is 30.2 and the IS is 4.0. These results demonstrate the superiority of CBS3-LandGen in generation tasks, especially in aspects such as image quality, diversity, and multimodal data fusion. The method proposed in this paper provides new ideas for intelligent landscape design and promotes the integration of landscape design and deep learning technologies. Future research will further optimize the model’s performance, improve training efficiency, and expand its application potential in practical landscape design, urban planning, ecological protection, and other fields. The code for implementing CBS3-LandGen Model is available at https://github.com/LMZ81/CBS3-LandGen.git .
Journal Article
Digital city landscape planning and design based on spatial information technology
With the development of information technology and China’s urban economy, the concept of smart cities has been proposed many times. Planning and designing cityscapes are essential in the process of building smart cities. There are many factors to consider when designing and planning traditional cityscapes, and these factors must be studied in combination with modern developments and characteristics. Information technology can provide massive data support and technical support for these studies. With the support of information technology, cities and modern technology can be closely connected. This paper proposes research on digital cityscape planning and design under spatial information technology using literature methods, field survey methods, comparative survey methods, and other methods. First, we deeply study spatial information technology and theoretical knowledge of digital cityscape planning and design; then, we design a spatial gray-level co-occurrence matrix model, collect urban vegetation data maps via remotely sensed images, and finally analyze the planning and design of digital cityscapes. After landscape planning, the vegetation was rated at 38.94 points, approximately 12 points higher than the rating given before planning. This result shows that after reasonable landscape planning and design, the needs of people for green plants can be met well, and the ecological value of digital urban spatial landscapes can be fully utilized. These results are important for maintaining the diversity of urban biodiversity, regulating the regional climate, and maintaining urban spaces, and the ecological balance of the entire city also plays a vital role.
Journal Article
Multiscale spatial planning to maintain forest connectivity in the Argentine Chaco in the face of deforestation
Agricultural expansion threatens biodiversity due to habitat loss and fragmentation. In the Gran Chaco, a global deforestation hotspot, rampant cropland and pasture expansion raise concerns about the sustainability of these land-use changes. Zoning policies were recently enacted in the Argentine Chaco to balance agriculture and conservation, yet the environmental outcomes of implementing these policies remain unclear. Here, we focused on the province of Formosa (Argentina) to evaluate how fully implementing zoning there would affect forest loss and connectivity, and how multiscale landscape planning could enhance environmental outcomes. Specifically, we simulated potential future forest cover for different spatial planning scenarios to assess the effect of (a) implementing regional corridors and (b) enacting additional policies to minimize forest fragmentation at the plot level, under both high and low deforestation rates. We then quantified forest connectivity and fragmentation using morphological image segmentation and landscape indices. Our results show that implementing regional corridors reduced the extent of potential deforestation by 650,000 ha (43%), and this alone strongly increased forest connectivity compared with scenarios without corridors. However, how deforestation would be carried out at the plot level was critically important. Plot-level spatial planning could have a strong and positive effect on mitigating fragmentation and on maintaining connectivity, even in scenarios with high deforestation rates (i.e., reducing the number of forest fragments by up to 35%, increasing the core forest by up to 6%). Moreover, under high deforestation rates, implementing regional corridors and plot-level design had a strong complementary effect on mitigating forest fragmentation (17% less forest fragments than when implementing either of the two strategies alone). Our analyses clearly highlight the opportunities of multiscale spatial planning and the need to complement broad-scale zoning with plot-level landscape design in order to mitigate the negative impacts of deforestation in the Chaco and other active agricultural frontiers.
Journal Article
A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.
Journal Article