Developing Greenways Under a Top-Down Institutional Structure: A Case Study in the Pearl River Delta, China

Greenways are linear open spaces and networks of lands that are planned, designed and managed for ecological, scenic, recreational and cultural purposes (Little, 1990; Ahern, 1995; Fabos, 1995; Erickson, 2004; Jongman & Pungetti, 2004). Greenways are commonly structured by natural or humanmade features such as rivers, ridgelines, railroads, canals or roads (Erickson, 2004). In the international greenway movement, greenways no longer had only a beautification and recreational function, but expanded to multiple objectives, such as habitat protection, flood hazard reduction, water quality improvement, historical preservation, education and interpretation (Searns, 1995; Tan, 2006). Moreover, the concept of greenways shows high compatibility with diverse forms, which is “a route which is good from an environmental point of view” (Turner, 1998). In China, the modern greenway movement started in 2010, when Guangdong Provincial Government initiated a three-year political campaign to construct the Pearl River Delta (PRD) Greenway Network. In 2014, the PRD greenways had a total length of 8,909 kilometers, connecting all 46 districts/counties in the PRD metropolitan region. The PRD Greenway Network has been assumed to be a feasible and low-cost approach to tackling with Chinese urbanism issues. Consequently, it is becoming an influential model for the greenway development at the national scale. Eleven provinces in China have recently planned or implemented provincial greenways, and many cities are working on municipal greenways. The development of greenways is beginning to stimulate the growth of greenway literature in China. Most of existing studies focus on early efforts and visions of greenway functions and benefits based on western experiences (Yu, Li, & Li, 2006). However, there is a lack of comprehensive profile of the rapidly developed greenways. Therefore, this paper will review the forms, functions and qualities of greenways based on a series of case studies

Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties

A robust topology optimization (RTO) approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach

RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning

The wide-angle lens shows appealing applications in VR technologies, but it introduces severe radial distortion into its captured image. To recover the realistic scene, previous works devote to rectifying the content of the wide-angle image. However, such a rectification solution inevitably distorts the image boundary, which changes related geometric distributions and misleads the current vision perception models. In this work, we explore constructing a win-win representation on both content and boundary by contributing a new learning model, i.e., Rectangling Rectification Network (RecRecNet). In particular, we propose a thin-plate spline (TPS) module to formulate the non-linear and non-rigid transformation for rectangling images. By learning the control points on the rectified image, our model can flexibly warp the source structure to the target domain and achieves an end-to-end unsupervised deformation. To relieve the complexity of structure approximation, we then inspire our RecRecNet to learn the gradual deformation rules with a DoF (Degree of Freedom)-based curriculum learning. By increasing the DoF in each curriculum stage, namely, from similarity transformation (4-DoF) to homography transformation (8-DoF), the network is capable of investigating more detailed deformations, offering fast convergence on the final rectangling task. Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations. The code and dataset are available at https://github.com/KangLiao929/RecRecNet.Comment: Accepted to ICCV 202