This study proposes a method for geometric transformation of a ground area in mobile camera images without cameras' internal parameters for registering them to Geographic Information System (GIS) database. If mobile camera images can be registered in GIS, it will be possible to share the latest geographic information by combining it with crowdsourcing in times of disaster. To achieve it, it is necessary to map the mobile camera images to the GIS image database. However, it is difficult to estimate the direct correspondence because most of the mobile camera images are landscape images, and the perspective of the ground area differs significantly. In contrast, GIS image data contain directly downward images. If the cameras' internal parameters and the posture at the shooting are known, it is possible to convert the image to top-view. However, considering the premise of crowdsourcing image collection, these parameters are not necessarily known. Therefore, we develop a method to correct the perspective distortion of the ground area in the image and convert it to a top-eye view even when the camera parameters are unknown.

In this study, an image scaling method to improve the accuracy of the image registration between images using different imaging devices is proposed. It is known that conventional keypoint detection, description, and matching methods do not work well between images with different spatial resolutions, such as those captured by drones and satellites. Thus, we propose a method to improve the geometric accuracy of image registration through an adaptive combination of super-resolution and low- resolution images and downscaling to high-resolution images based on the assumption that artificial structures are perceived as relatively simple shapes in top-view images. If the superresolution factor is too high, artifacts are generated, and the accuracy of the corresponding matching points will be decreased. Thus, estimating the highest super-resolution factor while avoiding the artifacts is necessary. Using the super-resolution factor, super-resolution processing of satellite images and downscaling to drone images are simultaneously performed. This is followed by corresponding points matching to achieve high estimation accuracy in the image registration process. Through quantitative evaluation experiments performed using pairs of images with 12 times difference in spatial resolutions, we demonstrated that high-accuracy image registration is possible by applying super-resolution processing at a factor of 4 to 6.

To establish advanced analytical methods for preserving cultural heritage, this research proposes a method to generate a time-lapse image with a super-long temporal interval. The key issue is to realize an image collection method using crowdsourcing and a method to improve the matching accuracy between images of cultural heritage buildings captured 50 to 100 years ago and current images. As degradation and damage to the appearance of cultural heritage buildings occurs due to ageing, rebuilding, and renovation, image features of the timed images are changed. This decreases the accuracy of the matching process that uses the appearance of patch-region. In addition, we need to give more consideration to incorrect feature correspondence that is prominent in buildings with considerable symmetry. We aim to solve these difficulties by applying an Autoencoder and a guided matching method. Our method involves utilizing the function of crowdsourcing, which can easily obtain the current image captured at the same position and orientation as the past image. We propose this method to address the inability to obtain the correspondence points between two images when observation times are significantly different.

In recent years, research efforts have enabled using the internet in disaster areas, and information technology (IT) is expected to improve our comprehension and evaluation of disasters. In disaster areas, crowdsourcing can be employed to secure human resources and controlled-task distribution. In crowdsourcing, many workers with publicly defined tasks (also called microtasks) are used. A simplified and more efficient microtask framework is required for disaster areas due to the lack of human resources and facilities. In this paper, we describe a method that incorporates information collection from a disaster area into microtasks with machine processing support.

In recent years, the pillars of the World Heritage Angkor Thom Bayon temple have become a problem of deterioration due to moss breeding. We aim to generate an image to support observation of moss breeding on a pillar. Even under environment that prevent image processing, we can achieve accurate overlay processing by combining corresponding points between images and 3D shapes. In order to generate the timelapse image of the observation target, many accurate images of different capturing timings are necessary. We are going to use a lot of images collected by crowdsourcing for time lapse images. In this research, we use two crowdsourcing models with the "capturing image of the target region" and the "classification of the captured images" as the micro task. Therefore, image acquisition using crowdsourcing and generation of time lapse image are looped. Time lapse image will be more accurate by repeating this flow.

Thanks to the rapid progress of the Internet and mobile devices, information related to disaster areas can be collected through the Internet. To grasp the degree of damage in a disaster situation, the use of crowdsourcing for coordinating the individual efforts (micro tasks) of an enormous number of users (workers) on the Internet has been drawing attention as a means of quickly solving problems. However, the information gathered from the Internet is huge and diverse, so it is difficult to formulate as a crowdsourcing task. This paper proposes a conversion platform for the images of a disaster site photographed by various users as information about the site, integrating the images into a single map using 3D image processing, and providing the map to crowdsourcing as a micro task.

Since over one million tourists annually visit the Angkor ruins, the effect on the buildings from the vibrations caused by these tourists is a huge problem for maintaining them. Such organisms as bryophytes, which adhere to the surface of the stones of the ruins, is another factor that damages them. Using crowdsourcing and 3D reconstruction technology, we are organizing a proactive preservation project for the Angkor Thom Bayon Temple, which is a world cultural heritage site. We evaluated its damaged parts and visualized the damaged state.