Abstract. Label placement is one of the most essential tasks in the fields of cartography and geographic information systems. Numerous studies have been conducted on the automatic label placement for the past few decades. In this study, we focus on automatic label placement of area-feature, which has been relatively less studied than that of point-feature and line-feature. Most of the existing approaches have adopted a rule-based algorithm, and there are limitations in expressing the characteristics of label placement for area-features of various shapes utilizing handcrafted rules, criteria, objective functions, etc. Hence, we propose a novel approach for automatic label placement of area-feature based on deep learning. The aim of the proposed approach is to obtain the complex and implicit characteristics of area-feature label placement by manual operation directly and automatically from training data. First, the area-features with vector format are converted into a binary image. Then a key-point detection model, which simultaneously detect and localize specific key-points from an image, is applied to the binary image to estimate the candidate positions of labels. Finally, the final label placement positions for each area-feature are determined via simple post-process. To evaluate the proposed approach, the experiments with cadastral data were conducted. The experimental results show that the ratios of the estimation errors within 1.2 m (corresponding to one pixel of the input image) were 92.6% and 94.5% in the center and upper-left placement style, respectively. It implies that the proposed approach could place the labels for area-features automatically and accurately.
ABSTRACT:In recent years, extensive research has been conducted to automatically generate high-accuracy and high-precision road orthophotos using images and laser point cloud data acquired from a mobile mapping system (MMS). However, it is necessary to mask out nonroad objects such as vehicles, bicycles, pedestrians and their shadows in MMS images in order to eliminate erroneous textures from the road orthophoto. Hence, we proposed a novel vehicle and its shadow detection model based on Faster R-CNN for automatically and accurately detecting the regions of vehicles and their shadows from MMS images. The experimental results show that the maximum recall of the proposed model was high-0.963 (intersection-over-union>0.7) -and the model could identify the regions of vehicles and their shadows accurately and robustly from MMS images, even when they contain varied vehicles, different shadow directions, and partial occlusions. Furthermore, it was confirmed that the quality of road orthophoto generated using vehicle and its shadow masks was significantly improved as compared to those generated using no masks or using vehicle masks only.
In this paper, a novel technique to generate a high resolution and high precision Orthorectified Road Imagery (ORI) by using spatial information acquired from a Mobile Mapping System (MMS) is introduced. The MMS was equipped with multiple sensors such as GPS, IMU, odometer, 2-6 digital cameras and 2-4 laser scanners. In this study, a Triangulated Irregular Network (TIN) based approach, similar to general aerial photogrammetry, was adopted to build a terrain model in order to generate ORI with high resolution and high geometric precision. Compared to aerial photogrammetry, there are several issues that are needed to be addressed. ORI is generated by merging multiple time sequence images of a short section. Hence, the influence of occlusion due to stationary objects, such as telephone poles, trees, footbridges, or moving objects, such as vehicles, pedestrians are very significant. Moreover, influences of light falloff at the edges of cameras, tone adjustment among images captured from different cameras or a round trip data acquisition of the same path, and time lag between image exposure and laser point acquisition also need to be addressed properly. The proposed method was applied to generate ORI with 1 cm resolution, from the actual MMS data sets. The ORI generated by the proposed technique was more clear, occlusion free and with higher resolution compared to the conventional orthorectified coloured point cloud imagery. Moreover, the visual interpretation of road features from the ORI was much easier. In addition, the experimental results also validated the effectiveness of proposed radiometric corrections. In occluded regions, the ORI was compensated by using other images captured from different angles. The validity of the image masking process, in the occluded regions, was also ascertained.
Automatic recognition of buildings has been in demand for efficient digital mapping process or updating of existing information in geographical information applications. It requires effective stereo matching techniques that are applicable to urban area. However conventional techniques that make use of area-based, point-based or edge-based matching cannot generate satisfactory results for urban area because of occlusion or inability of mismatching recovery. This paper proposes a new stereo matching technique, which combines edge detection and nonlinear mapping, and is based on a process called Coincidence Enhancement Method (CEM) [4]. Edge segments, obtained from edge detection and stereo matching process, are used as constraints for edge enhancement in depth map. Experiments on edge detection, edge matching, CEM and edge enhanced CEM were performed with stereo aerial imageries of urban area. The results show that the proposed approaches of CEM with edge support are efficient for improving the matching results surrounding building's boundary.
Abstract. The historical castles (castellated walls), which are cultural heritages in Japan, require regular maintenance, and it is necessary to record the arrangement of individual wall stones in the maintenance work. Recently, image processing techniques are practiced to optimize maintenance and management of the infrastructure assets. In the previous study, we proposed an automatic method for efficiently extracting individual wall stone polygons by improved multiscale image segmentation technique. However, the problem has remained that wall stone polygons could not be extracted properly when there were no clear gaps or boundaries between stones. To address this problem, we improved the multiscale image segmentation technique used in our previous studies. The first improvement is that in the region growing process, selecting the best combination of a plurality of objects instead of two. The second improvement is the modification of the shape criterion to be used. Besides, we proposed three-stage Stacked cGAN for wall stone edge detection that enables us to complement areas with weak or broken boundaries of stone edges. This approach is composed of a coarse-to-fine based image-to-edges translation network. The edge images derived from this method are used as the additional channel in multiscale image segmentation with a higher weight compared to the other RGB channels. It was confirmed that the separation performance of individual wall stone polygons was improved by the proposed method. Furthermore, the proposed method is highly effective to reduce the difficulty in setting of the scale parameter, which is usually sensitive to segmentation results and requires trial and error.
Castellated walls are positioned as tangible cultural heritage, which require regular maintenance to preserve their original state. For the demolition and repair work of the castellated wall, it is necessary to identify the individual stones constituting the wall. However, conventional approaches using laser scanning or integrated circuits (IC) tags were very time-consuming and cumbersome. Therefore, we herein propose an efficient approach for castellated wall management based on an extended multiscale image segmentation technique. In this approach, individual stone polygons are extracted from the castellated wall image and are associated with a stone management database. First, to improve the performance of the extraction of individual stone polygons having a convex shape, we developed a new shape criterion named <i>convex hull fitness</i> in the image segmentation process and confirmed its effectiveness. Next, we discussed the stone management database and its beneficial utilization in the repair work of castellated walls. Subsequently, we proposed irregular-shape indexes that are helpful for evaluating the stone shape and the stability of the stone arrangement state in castellated walls. Finally, we demonstrated an application of the proposed method for a typical castellated wall in Japan. Consequently, we confirmed that the stone polygons can be extracted with an acceptable level. Further, the condition of the shapes and the layout of the stones could be visually judged with the proposed irregular-shape indexes.
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