The occurrence of landslides has increased in recent years due to climate change. It is of the utmost importance to conduct an early assessment of the landslide areas to make appropriate decisions. Previous studies have proposed various methods to estimate landslide areas with high accuracy. However, those methods have black-box characteristics: the process of estimation and the causes of misestimation are sometimes not known.Also, even in bad weather, SAR satellites can observe the ground over a wide area. On the other hand, if the topography of the ground is complicated, this can negatively influence the observed data. For the improvement of the estimation method, it is important to identify how is the effect of complicated ground. Therefore, this study aims to clarify the influence of topographic slope data on the estimation of landslide areas using SAR images. In this study, four models were constructed to estimate landslide area from SAR images and topographic slope data. The analysis area was divided into four regions based on the slope angle and slope direction data values, and their precision was compared. The results showed that the precision varied from 3.7% to 5.3% depending on the model when compared among the four divided regions. By visualizing the estimation results, it was also possible to confirm that the relationship between the topography and SAR observations influenced the estimation.

In this study, SAR data, DEM, basic geographic data, and mobile phone GPS location data were used to construct flood area estimation models for the city of Nagano and the surrounding area in case of the October 2019 flood in the Chikuma River Basin in Nagano Prefecture, Japan, as an example. The effect of mobile phone GPS location data on the flood area estimation model was evaluated by taking advantage of the ability of SAR to pass through clouds and the accurate recording of population movement by mobile phone GPS location data. The results of the study showed that the combined data could construct a flood prediction model with an accuracy of approximately 0.8. Specifically, the accuracy of the flood area estimation model without GPS data was 0.799, and the accuracy of the flood area estimation model with GPS data was 0.815. It was worth noting that GPS data could have a positive impact on the accuracy of the model in more populated building areas. This study was extended to the flood area estimation models and determines the importance of GPS data in improving model accuracy, which has practical implications for improving flood disaster monitoring and response.

Accurately predicting the long-term demand for public bicycle systems (PBS) is crucial for policy implementations such as operator rebalancing. With the continuous advancement of deep learning techniques, including self-attention mechanisms and graph convolution networks (GCNs), we can better understand the nonlinear relationship of spatio-temporal data. Despite increasing interest in long-series transportation forecasting, there are very few long-term spatio-temporal predictions focused on PBS data. In this paper, we introduce a novel spatio-temporal attentional graph convolutional network (ST-AGCN) designed to predict demand accurately over a 24-hour horizon, addressing the limitations of existing models and the characteristics of PBS data. The core innovation of our model is the integration of a probabilistic self-attention mechanism (Probsparse self-attention) for predicting long-term temporal dependencies and Chebyshev GCN for extracting complex spatial dependencies within the encoder-decoder framework. This integration significantly reduces computational time. Additionally, we devise a PBS data-based spatial weighting calculation method to generate the spatio-temporal graph, which facilitates capturing more inter-station relationships to improve computational efficiency and accuracy. Experimental results compared with seven baseline models on real-world datasets demonstrate that ST-AGCN outperforms state-of-the-art baselines in both accuracy and computational efficiency.

This study addresses the combination of satellite and ground particulate matter with a diameter less than 2.5 microns (PM2.5) in Thailand. Thailand's Pollution Control Department (PCD) and Bangkok's Air Quality and Noise Management Division gathered PM2.5data between 2011 and 2020. NASA's Earth Observing System Data and Information System (EOSDIS) retrieves all MODIS satellite data. According to this study, Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Elevation (EV) may be employed as PM2.5predictors from ground monitoring sites in Thailand. The model showed better performance when including time (Week of year (WOY), Year), indicating seasonal fluctuations in PM2.5. The generated model uses Spearman's correlation and stepwise regression model. This research determined which satellite data is appropriate for estimating PM2.5in Thailand.

In this study, a function to estimate tsunami inundation depth using pre- and post-event high-resolution synthetic aperture radar (TerraSAR-X) data was derived and the performance was evaluated. After the tsunami disaster, it is important to identify an extensive impact caused by a tsunami disaster. Tsunami inundation depth is an important index to expect building damage because it has a strong correlation with the amount of building damage. However, it was diffcult to estimate the tsunami inundation depth from satellite image. This study aims at developing a method to estimate tsunami inundation depth by integrating remote sensing technology and tsunami engineering. The method for estimating tsunami inundation depth consists of two steps, 1) Change detection of pre- and post-event TerraSAR-X data that captured affected areas due to the 2011 Tohoku earthquake and tsunami, 2) Estimation of tsunami inundation depth. The new function showed good performance with the correlation coefficient of R = 0.68.

In this study, a practical methodology was presented to map damaged buildings using high resolution synthetic aperture radar (SAR) images and post-event building damage data from the 2013 Super Typhoon Haiyan, in Tacloban city, the Philippines. To detect destroyed structures, we focused on the changes in the radar signal within footprints of buildings between pre- and post-event SAR images. The method was tested using a 1.0 m resolution COSMO-SkyMed SAR images taken over Tacloban city, the Philippines. The method proves, with 73% accuracy in this case, to be suitable for estimating destroyed buildings.

In this letter, a new approach is proposed to classify tsunami-induced building damage into multiple classes using pre- and post-event high-resolution radar (TerraSAR-X) data. Buildings affected by the 2011 Tohoku earthquake and tsunami were the focus in developing this method. In synthetic aperture radar (SAR) data, buildings exhibit high backscattering caused by double-bounce reflection and layover. However, if the buildings are completely washed away or structurally destroyed by the tsunami, then this high backscattering might be reduced, and the post-event SAR data will show a lower sigma nought value than the pre-event SAR data. To exploit these relationships, a rapid method for classifying tsunami-induced building damage into multiple classes was developed by analyzing the statistical relationship between the change ratios in areas with high backscattering and in areas with building damage. The method was developed for the affected city of Sendai, Japan, based on the decision tree application of a machine learning algorithm. The results provided an overall accuracy of 67.4% and a kappa statistic of 0.47. To validate its transferability, the method was applied to the town of Watari, and an overall accuracy of 58.7% and a kappa statistic of 0.38 were obtained.

In this study, the extent of the flooded areas by the Super Typhoon Haiyan in the Philippines were extracted using ASTER VNIR images taken over Tacloban city in the Visayas. In order to constraint the affected area, we employed the normalize difference vegetation and water indices (NDVI and NDWI) from the pre- and post-event images. The extension of the flooded area was determined by comparing the index characteristics before and after the event. A phase-based change detection method indices was applied to classify the affected area into three classes according to the changes between the pre- and post-images. Through NDWI the flooded areas were detected despite the moderate resolution of ASTER images. In addition, the phase-based analysis successfully detected level of change within the affected area that may be correlated to the damage observed on field surveys. The results from the phase-based analysis were verified with damage levels obtained through visual damage inspection using high resolution satellite images.

In this study, a semi-automated method to estimate building damage in a tsunami affected area is developed using pre- and post-event high-resolution synthetic aperture radar (TerraSAR-X) data. For development, some coastal areas affected by the 2011 Tohoku earthquake tsunami were focused. The method for estimating building damage consists of three steps, 1) To detect flooded areas by the tsunami, 2) To detect built-up areas, 3) To estimate building damage inside the flooded built-up areas. The previously proposed methods using high-resolution SAR data needs building footprint data for estimating building damage[1]. However, this problem was improved by developing a new method which does not need building footprint data to estimate building damage caused by the tsunami. The developed method was validated on the other test sites and the estimated results showed good consistency with the ground truth data.

Using the aerial photos published by Geospatial Information Authority of Japan (GSI), the authors visually inspected the building damage to identify the structural vulnerability in the tsunami affected area due to the 2011 Tohoku earthquake tsunami. First, the electronic map of buildings and the aerial photos were superimposed on GIS. Then, visual inspection of building damage was conducted in Iwate and Miyagi Prefectures, and in order to identify the structural vulnerability in each municipality, damage probabilities(PD) were calculated by taking a ratio of the number of devastated buildings those were washed away over the number of total buildings exposed by the tsunami. Finally, the relationship between the number of devastated buildings and the number of fatalities were discussed to identify local vulnerability against the tsunami.

The 11 March 2011 Tohoku, Japan earthquake caused gigantic tsunamis and widespread devastations. Various satellites quickly captured the details of affected areas, and were used for emergency response. In this study, high-resolution pre- and post-event TerraSAR-X (TSX) intensity images were used to identify damaged buildings. Since the damaged buildings show changes in backscattering intensity, they can be detected by calculating the difference. A GIS map was introduced to identify individual damaged buildings and investigate their characteristics. According to the side-looking nature of SAR sensors, the buildings' shapes obtained from the GIS map were converted to match their locations in the TSX images. Then washed-away and damaged buildings were extracted using the changed area of SAR intensity within a building's wall and outline. The results were compared with visual interpretation results, and the accuracy of the proposed method was confirmed.

The paper outlines new research findings and hereof generated products in the field of earth observation and modeling technologies to support emergency response measures. Based on the recent earthquake and tsunami disaster in Japan (March 2011) examples will be given for new methodological developments and products to support emergency response strategies more effectively.

The authors developed a method of object-based satellite image analysis using high-resolution post-tsunami satellite image to detect and map tsunami impact. The method is applied to QuickBird 4 band pan-sharpened composite image acquired in Banda Aceh, Indonesia, and the ground objects are classified into six ; vegetation, water, soil, building, road and debris, for mapping the impact of the 2004 Sumatra Andaman earthquake tsunami.

The present paper reports a preliminary result of searching tsunami-affected area using recent advances of GIS analysis and remote sensing combined with a numerical modeling of tsunami propagation/inundation and world population database. Applying the method of searching tsunami affected area to the 2009 Samoa earthquake tsunami and the 2010 Chilean earthquake tsunami, the potential tsunami affected area have been detected at some coastal cities/communities. The results are utilized to detecting tsunami impacted area for conducting disaster relief activities.