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The automatic extraction of building outlines from high-resolution images is an important and challenging task. Convolutional neural networks have shown excellent results compared with traditional building extraction methods because of their ability to extract high-level abstract features from images. However, it is difficult to fully utilize the multiple features of current building extraction methods; consequently, the resulting building boundaries are irregular. To overcome these limitations, we propose a method for extracting buildings from high-resolution images using a multifeature convolutional neural network (MFCNN) and morphological filtering. Our method consists of two steps. First, the MFCNN, which consists of residual connected unit, dilated perception unit, and pyramid aggregation unit, is used to achieve pixel-level segmentation of the buildings. Second, morphological filtering is used to optimize the building boundaries, improve the boundary regularity, and obtain refined building boundaries. The Massachusetts and Inria datasets are selected for experimental analysis. Under the same experimental conditions, the extraction results achieved with the proposed MFCNN are compared with the results of other deep learning models that have been commonly used in recent years: FCN-8s, SegNet, and U-Net. The results on both datasets reveal that the proposed model improves the F1-score by 3.31%-5.99%, increases the overall accuracy (OA) by 1.85%-3.07%, and increases the intersection over union (IOU) by 3.47%-8.82%. These findings illustrate that the proposed method is effective at extracting buildings from complex scenes. Index Terms-Building outline extraction, high-resolution images, morphological filtering, multifeature convolutional neural network (MFCNN). I. INTRODUCTION A S A critical component of basic urban geographic information, buildings play an important role in population estimation, change monitoring, urban planning, and smart city construction [1]-[3]. Consequently, the automatic extraction of Manuscript

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Because mobile virtual reality (VR) is both mobile and immersive, three-dimensional (3D) visualizations of disaster scenes based in mobile VR enable users to perceive and recognize disaster environments faster and better than is possible with other methods. To achieve immersion and prevent users from feeling dizzy, such visualizations require a high scene-rendering frame rate. However, the existing related visualization work cannot provide a sufficient solution for this purpose. This study focuses on the construction and optimization of a 3D disaster scene in order to satisfy the high frame-rate requirements for the rendering of 3D disaster scenes in mobile VR. First, the design of a plugin-free browser/server (B/S) architecture for 3D disaster scene construction and visualization based in mobile VR is presented. Second, certain key technologies for scene optimization are discussed, including diverse modes of scene data representation, representation optimization of mobile scenes, and adaptive scheduling of mobile scenes. By means of these technologies, smartphones with various performance levels can achieve higher scene-rendering frame rates and improved visual quality. Finally, using a flood disaster as an example, a plugin-free prototype system was developed, and experiments were conducted. The experimental results demonstrate that a 3D disaster scene constructed via the methods addressed in this study has a sufficiently high scene-rendering frame rate to satisfy the requirements for rendering a 3D disaster scene in mobile VR.

BackgroundThe treatment of metastatic breast cancer (MBC) remains a great clinical challenge as drug resistance frequently develops. Alternative agents that can overcome drug resistance would offer new therapeutic options. The primary aim of this phase II study was to evaluate the efficacy and safety of utidelone as a monotherapy or in combination with capecitabine in metastatic breast cancer patients previously treated with and resistant to anthracyclines and taxanes.MethodsIn two open-label, noncomparative clinical studies, patients with metastatic breast cancer who previously received anthracycline- and/or taxane-containing regimens were given (1) 25 to 35 mg/m2/day intravenously infused utidelone, once daily for 5 days, in combination with 14 days of 2000 mg/m2 capecitabine, divided in two equal daily oral doses or (2) 40 mg/m2/day intravenously infused utidelone, once daily for 5 days. These regimens were administered per each 21-day treatment cycle, and the maximum of treatment cycles allowed per protocol is 6. Objective response rate (ORR), progression-free survival (PFS), and tolerability were evaluated.ResultsIn the combination study, 33 patients completed a median of 6 cycles of therapy, which was the highest cycles a trial patient could receive under the criteria of the study protocol. Efficacy was evaluated (n = 32) with an ORR of 42.4 % (FAS, 95 % CI, 26.6, 60.9) and a median PFS of 7.9 (FAS, 95 % CI, 6.1, 9.8) months. The monotherapy study (n = 63) resulted in an ORR of 28.57 % (FAS, 95 % CI, 18.4, 40.6) and a median PFS of 5.4 (FAS, 95 % CI, 2.9, 9.8) months. In both studies, common toxicities associated with utidelone administration included peripheral neuropathy, fatigue, myalgia, and arthralgia, but the toxicities were limited and manageable. Notably, very mild myelosuppression, low liver and renal toxicities, and very limited gastrointestinal toxic effect were observed, in contrast to other agents in the same class.ConclusionsUtidelone showed promising efficacy, tolerability, and advantageous safety profiles in the treatment of patients with advanced anthracycline/taxane-refractory metastatic breast cancer and may offer new treatment options to overcome drug resistance.Trial registrationCHiCTR-TRC-13004205, registered on August 15, 2013.

Scientific and appropriate visualizations increase the effectiveness and readability of disaster information. However, existing fusion visualization methods for disaster scenes have some deficiencies, such as the low efficiency of scene visualization and difficulties with disaster information recognition and sharing. In this paper, a fusion visualization method for disaster information, based on self-explanatory symbols and photorealistic scene cooperation, was proposed. The self-explanatory symbol and photorealistic scene cooperation method, the construction of spatial semantic rules, and fusion visualization with spatial semantic constraints were discussed in detail. Finally, a debris flow disaster was selected for experimental analysis. The experimental results show that the proposed method can effectively realize the fusion visualization of disaster information, effectively express disaster information, maintain high-efficiency visualization, and provide decision-making information support to users involved in the disaster process.

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Emergency risk assessment of debris flows in residential areas is of great significance for disaster prevention and reduction, but the assessment has disadvantages, such as a low numerical simulation efficiency and poor capabilities of risk assessment and geographic knowledge sharing. Thus, this paper focuses on the construction of a VGE (virtual geographic environment) system that provides an efficient tool to support the rapid risk analysis of debris flow disasters. The numerical simulation, risk analysis, and 3D (three-dimensional) dynamic visualization of debris flow disasters were tightly integrated into the VGE system. Key technologies, including quantitative risk assessment, multiscale parallel optimization, and visual representation of disaster information, were discussed in detail. The Qipan gully in Wenchuan County, Sichuan Province, China, was selected as the case area, and a prototype system was developed. According to the multiscale parallel optimization experiments, a suitable scale was chosen for the numerical simulation of debris flow disasters. The computational efficiency of one simulation step was 5 ms (milliseconds), and the rendering efficiency was approximately 40 fps (frames per second). Information about the risk area, risk population, and risk roads under different conditions can be quickly obtained. The experimental results show that our approach can support real-time interactive analyses and can be used to share and publish geographic knowledge.