Economic, Social, and Ecological Impact Evaluation of Traffic Network in Beijing–Tianjin–Hebei Urban Agglomeration Based on the Entropy Weight TOPSIS Method

In recent years, with the rapid development of urban transportation network in China, many problems have been exposed, especially in the Beijing–Tianjin–Hebei (BTH) region. Under the call of sustainable development, it is of great significance to evaluate the economic, social, and ecological (ESE) impact of transportation network in BTH urban agglomeration for promoting the sustainable development of transportation ESE in BTH urban agglomeration. In this paper, 12 indicators in the field of transportation are selected to build the evaluation index system of ESE effects of transportation network in BTH urban agglomeration. By using entropy weight TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model and the Jenks natural breaks classification method, the ESE impacts of transportation network in 13 cities of BTH from 2013 to 2017 are analyzed from the temporal and spatial dimensions. The research shows that: (1) From 2013 to 2017, the economic impact degree of traffic network shows an annual fluctuation trend, the social impact degree increases year by year, and the ecological impact degree decreases year by year; (2) For the cities of BTH, the ESE impact assessment results of transportation network from 2013 to 2017 can be divided into seven clusters. Except Handan City, the ESE impact assessment categories of other cities’ transportation network have been improved, but the proportion of cities in the transition period is still large, especially the “Low-Low-Low” cities. The types of cities in the transitional period need to be focused. It is still a heavy burden to realize the ESE coordination and sustainable development of BTH urban agglomeration transportation network

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index

A Seamline Optimization Approach Based on Watershed Segmentation for Aerial Image Mosaicking

Seamline optimization is a key step in the process of aerial image seamless mosaicking.This paper presents a novel algorithm of seamline optimization for aerial image mosaicking by adaptive marker-based watershed segmentation.The preferred region is determined by the difference of the region achieved by adaptive marker-based watershed segmentation. Then, the minimum binary heap Dijkstra's algorithm is adopted to determine the final seamlines in the preferred region. The experimental results show that the seamline determined by our method can avoid crossing obvious stand-alone objects. Compared with other algorithms,our method has higher feasibility and higher speed