ISO/TC211 is a technical committee formed under ISO for standardization in the field of digital geographic information. Since 1994, ISO/TC211 has been establishing a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. Japan, as a member of TC211, has been contributing to the development of these standards from the early stage. This paper overviews ISO/TC211 and introduce related activities in Japan.
In ITS (Intelligent Transport Systems), road maps coded in network data structure with nodes (intersections) and links (routes), are indispensable, not only for car navigation system but also cooperative, connected and automated mobility. TC204/WG3 undertakes standardization of road map which includes functional specification of road maps, location reference methods and exchange formats between geographic data providers. This paper introduces current activity of TC204/WG3, as well as some hot topics in ITS community, such as MaaS, V2X and Dynamic Map.
In this paper, we introduce the international standardization activities on UAV related technology in ISO/TC20/SC16 Unmanned aircraft systems. We also introduce the status of international standard proposal from Japan on spatial data model : ISO 23629-7 “Data model related to spatial data for UAS and UTM”and the activities related to spatial data model in Japan.
In this study, we have developed a novel methodology for building change detection in the dense urban areas. Our approach is based on building recognition using aerial images and Digital Surface Models (DSMs) that allows detection of large and small buildings respectively. Large buildings are detected by a global thresholding of the DSMs using a height threshold in order to prevent the problem that one building may be divided into several portions. Small gable-roof and flat-roof buildings are detected individually according to the three-dimensional shape of the roofs so that buildings can be separated from each other more easily in densely built-up areas. Afterwards, change detection is implemented based on the result of building recognition, and only the DSMs are used for detecting the change of buildings in order to avoid the influence of image color variation. Also, in order to detect partially-changed buildings accurately, the increased and decreased height differences of two epochs of DSMs are extracted individually, and image morphological processing is performed to remove noise and extract actual changed areas. To assess the effectiveness of the proposed methodology, the change detection result has been verified by comparing to a visual interpretation result. The experimental results indicate 78.1% completeness with correctness of 52.3% in a dense built-up area, which demonstrate that our methodology can stably detect changed buildings with variation in height such as newly constructed, demolished, extended and structural alterations, and suppress false detection effectively.
We have been studying a imaging type water level measurement method using images recorded water gauges which look like rulers been stuck into rivers vertically. It is the feature of this unit that observed images are collated with the one prerecorded in the time of low water level in order to specify the water border position. We implemented a program based on our method and tested the performance using multiple actual video data acquired under different weather and sunshine conditions and so on. As the result, which shows that 24 hours continuous observation can be accomplished and the calculated value is satisfied within required accuracy at typical video data, we have made sure of the effectiveness of our method.
In recent years, extensive researches have been conducted to automatically generate high-resolution road orthophotos using images and laser point cloud data collected by a Mobile Mapping System (MMS). However, it is necessary to detect and mask out the areas of non-road objects in MMS images such as vehicles, bicycles, pedestrians and their shadows, in order to eliminate erroneous textures from the road orthophotos. Hence, we proposed a novel vehicle and its shadow detection method based on Faster R-CNN for improving the detection accuracy, especially the accuracy of detected regions. The experimental results showed that the recall of the proposed method was 93.9% (Intersection-over-Union＞0.7), which was 7.0% higher than 86.9% obtained by Faster R-CNN. Moreover the proposed method could identify the regions of vehicles and their shadows accurately and robustly in MMS images, even though the images contained various types of vehicles, different shadow directions, and partial occlusions. Furthermore, it was confirmed that the quality of road orthophoto generated using vehicle and its shadow masks by the proposed method was significantly improved as compared to those generated using no masks, vehicle masks and even the vehicle and its shadow masks by Faster R-CNN.