In September 2011, numerous deep-seated landslides accompanied with landslide dams were triggered by the torrential rain over the Kii Peninsula. The Ministry of Land, Infrastructure, Transport and Tourism (MLIT) conducted surveys based on the Sediment Disaster Prevention Act. At the initial stage of the emergency, the MLIT successfully found the landslide dams in the damaged area through analyzing the satellite SAR imagery. In this paper, we report the situation of the surveys that conducted during the disaster.

The new technology drone which appeared in around 2010 came into the limelight as concrete featured one of the robotization promotion and started an approach in public and private sectors. It was the theme that was important to the disaster investigation while the profit fields that inflected included an aerial photograph, surveying, the material transportation, guard. By the Hiroshima heavy rain earth and sand disaster that occurred in August, 2014, the shooting image by the drone caught the damage situation precisely, and the result was provided quickly from the Ministry of Land, Infrastructure, Transport and Tourism by each organization and contributed to next various measures planning.

In case of a large scale natural disaster, Geospatial Information Authority of Japan (GSI) provides necessary geospatial information to contribute to lifesaving rescue activities and disaster recovery measures operated by relevant disaster management organizations (DMOs). When the Kanto-Tohoku Heavy Rainfall Disaster in September 2015 occurred, GSI took aerial photographs and unmanned aerial vehicle movies, and produced presumed inundation-area maps of Joso district to supply DMOs with such geospatial information.

The LC-InSAR map by DInSAR showed a large number of ‘phase discontinuous lines' at locations other than the main trace of the surface rapture caused by the Kumamoto earthquakes. On the Line of this ‘phase discontinuous line', many places where minute surface displacement was observed were found. Most of the ‘phase discontinuity lines' are thought to represent minor surface displacements caused by the Kumamoto earthquake. It is considered that such a minor displacement was difficult to find out only by the field survey after the earthquake because the displacement was too small. It was confirmed that the LC-InSAR map is effective for detecting minute surface displacement.

A heavy rain accompanied by a front hit western Japan in July 2018, flooded the Takahashi River system and the Hijikawa River, and brought an inundation disaster nearby. Geospatial Information Authority of Japan (GSI) created and released provisional inundation depth maps by deciphering and analyzing the inundation area and depth using social media, aerial photographs, and DEM.

We attempted to extract building damage areas from satellite images using semantic segmentation. As a result of experiments using satellite images after the 2019 Typhoon No. 15 (Faxai), the recall of the blue sheet area was 0.48 and that of the building area with damage was 0.35 in the area-based evaluation. When the building shape was obtained from the ground truth data and the number of buildings was evaluated, the recall of buildings with damage was 0.62. The challenge is to improve the accuracy by training data from multiple regions.

A debris flow occurred due to heavy rain in Izusan, Atami City, Shizuoka Prefecture on July 3, 2021. In order to understand the situation of the debris flow, Geospatial Information Authority of Japan (GSI) measured the elevation using a laser scanner mounted on an unmanned aerial vehicle (UAV) three days after the disaster. In this area, airborne laser measurements were conducted as public survey in 2009 and 2019. These past data enabled GSI to calculate the elevation changes by comparing the results of laser measurements between three periods, including before and after the disaster. As a result, detailed and quantitative information including volume of residual soil is obtained. The results were released through GSI website on the same day as the UAV measurement. In this paper, its calculation process and results are reported.

Various kinds of cameras have been utilizing as onboard cameras in the construction of Intelligent Transport Systems. In recently, utilization of the high sensitivity consumer grade digital cameras at night is attracting attention from the viewpoint of avoiding the effects of sunlight and congestion of people and cars. However, due to the image taken by the onboard cameras is a perspective projection image, the image is projected small at the far from car and the greater the influence of the lens distortion the farther from the image center. In order to avoid the issues, lower part of projection image or a bird's-eye view image is used, but the imaging of the bonnet part due to the vehicle model and the tilt of the camera becomes a new issue. Furthermore, a bird's-eye view image at night has to be trimmed to coincide with irradiation range since the irradiation distance and range for headlight are limited. On the other hand, feature quantities such as vanishing points and feature points on the lane have been used for projective transformation from a perspective projection image to a bird's-eye view image, but the projective transformation based on the feature quantity is an ill-posed problem.

Therefore, this paper discusses the quantitative trimming method based on the projective transformation that does not depend on the feature quantities and coincide with irradiation range of headlight too.

This paper describes a laboratory test result of paddy yellowing rate estimation technique for smart agriculture. The paddy yellowing rate is important for determining the proper time of harvesting. It is required to measure the paddy yellowing rate in the field by image analysis. Since, this technological development is extremely difficult, this laboratory test was conducted as a preliminary step. The study area is Hozaki area in Akaiwa city and Shinogoze area in Okayama city, Okayama prefecture. Sampling and laboratory tests were conducted on 7^th October in 2019 and 9^th October in 2020, these times are 1 or 2 weeks before harvest. The number of sampling points is 47, three ears were collected from each point. The ear was photographed with Olympus digital camera, TG-5. Image data converted from raw to TIFF data. The paddy yellowing rate was calculated by dividing the number of blue seeds, means immature, by the total seeds. The explanatory variables were normalized difference vegetation index that combined two bands from blue, green and red band. As a result of the analysis using 2019 data, a linear regression model with the highest correlation coefficient (R ; 0.77) could be built by the normalized difference vegetation index using the combination of red and green band. An estimation model using 2020 data could not be constructed. It is probable that the ripening of this year was more advanced than previous year. From the result, it could be considered that the proposed method can be applied before the late ripening period.

A quick assessment of landslide damage in mountainous areas after a disaster occurs is important for planning of the disaster recovery action. For this assessment, deep learning AI is thought to be an effective method to grasp quickly the state after a disaster. The deep learning for image classification, however, needs a large amount of training and test data. To overcome this problem, a transfer learning is thought to be effective, especially, when much data is not available. In this paper, we compared between each result of four major pretrained CNN architectures (AlexNet, GoogLeNet, VGG-16, SqueezeNet) to be transfer learned, using pre- and post-disaster visible image information. At the result, GoogLeNet showed best collaspe recall rate 81.4%, and accuracy 85.7%. Also, VGG-16 showed the best non-collapse recall rate 93.1%. The remarkable point of this comparison is that collapse areas, which were classified as non-collapse areas by all of four model, belonged to ground involved large amount of sand and clods.