Information extracted from aerial photographs is widely used in the fields of urban planning and design. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured three-dimensional (3D) virtual models with aerial photographs. Some aerial photographs include clouds, which degrade image quality. These clouds can be removed by using a generative adversarial network (GAN), which leads to improvements in training quality. Therefore, the objective of this research was to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs. In this study, using GAN to remove clouds in aerial photographs improved training quality. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with IoU = 0.651.

Architecture, engineering, and construction projects need to be promoted in harmony with the natural environment and with the aim of preserving people’s living environment. At the planning and design stage, decision-makers and stakeholders share and assess landscape images during and after construction in order to avoid as much uncertainty as possible when performing environmental impact assessment. Given the lack of a standard visualization method for future landscapes that do not yet exist, mixed reality (MR), which overlays virtual content onto a real scene, has attracted attention in the field of landscape design. One challenge in MR is occlusion, which occurs when virtual objects obscure physical objects that should be rendered in the foreground. In MR-based landscape visualization, the distance between the MR camera and real objects located in front of the virtual objects might vary and might be large, causing difficulty for existing occlusion handling methods. In the process of landscape design, an evidence-based approach has also become important. Landscape index estimation using semantic segmentation by deep learning, which can recognize the surrounding environment, has been actively studied for landscape assessment. In this study, semantic segmentation by deep learning was integrated into an MR system to enable dynamic occlusion handling and landscape index estimation for both existing and designed landscape assessment. This system can be operated on a mobile device with video communication over the internet by connecting to real-time semantic segmentation on a high-performance personal computer. The applicability of the developed system is demonstrated through accuracy verification and case studies.

In obstetrics and gynecology, internal examination, i.e., palpation of the cervix, is performed to diagnose the risk of miscarriage and preterm birth based on its hardness. However, the diagnosis is subjective and requires high skill. The introduction of technologies for quantifying cervical stiffness can increase the accuracy of the diagnosis. Various softness sensors have been proposed, but because the route to the cervix is curved and narrow, they are not suitable for the internal examination. Safety for pregnant women and fetuses must be also taken into consideration. In this paper, we propose a soft wearable tactile sensor made entirely of silicone rubber. The structure is simple and the sensing principle is based on acoustic reflection, which is safe because no electricity flows inside the body and its texture is soft. Furthermore, the sensor is worn at the clinician’s fingertip thus allowing him/her to make use of his/her skill. The sensor measures contact forces at three points of the fingertip considering human’s softness perception. Experimental results showed that the fabricated sensor is capable of determining two magnitudes of hardness (Young’s modulus 120 kPa and 230 kPa) within a fingertip orientation from 30° to 60°.

This paper aims to develop a wearable tactile sensor for measuring skin vibrations using a polyvinylidene fluoride (PVDF) film, which is a polymer piezo material. The sensor is worn on the finger pad where is remote from contact fingertip and detects skin-propagated vibrations when fingertip touches an object. The proposed sensor allows users to touch with bare fingers and to conduct active touch. A transfer function from vibrations applied on the fingertip to the sensor output is expressed by using a finger model, a sensor model, and an electric model of the PVDF film. On the basis of the transfer function, frequency response of the sensor is measured and estimation of vibrations is tested. Furthermore, the sensor output is investigated for three materials with different textures. Results show the validity and availability of the sensor.

We have developed a forceps-type tactile sensor for palpation of a tumor in laparoscopic surgery. The sensor responds to contact position and force at deformable cavity in the sensor probe by using acoustic reflection. The sensor has high applicability for laparoscopic surgery because of its simple structure and disuse of electrical elements within inserted part into patient's body. In this study, we try to measure softness of a contact tissue by using the sensor. Human can estimate softness from contact area and force. Indentation depth is not always required for softness perception. This perception mechanism suggests that our sensor has a possibility for softness measurement because the sensor can respond to contact position and force. Softness measurement contributes to tumor localization and improvement of diseases database. First, the sensor output characteristic is investigated for different soft samples. Then, a method of stiffness measurement using the sensor outputs is proposed. Experimental results show that the sensor has the capability of softness measurement without any additional sensors.

Building tall structures behind an aesthetic and historical building tends to destroy the good landscape. To avoid such situations, public agencies must regulate height of buildings and other structures near the landscape target. In order to check the visibility of portions of high, future structures, in this research, a new method using Augmented Reality (AR) was proposed. In this method, a number of virtual rectangular objects with a scale are located on the grid of 3D geographical model. And then, the virtual rulers are shown in an overlapping manner with the actual landscape from multiple viewpoints using the AR technology. The user measures the maximum skyline-preserving height for each rectangular object at a grid point. Using the measured data, the government or public agencies can establish appropriate height regulations for all surrounding areas of the target structures. To verify the proposed method, a system was developed deploying AR Toolkit and was applied to a scenic building. The performance of the system was checked and then, the errors of the obtained data were evaluated. In conclusion, the proposed method was evaluated feasible and effective.