A rapid mapping of landslides following a disaster is important for coordinating emergency response and limiting rescue delays. A synthetic aperture radar (SAR) can provide a solution even in harsh weather and at night, due to its independence of weather and light, quick response, no contact and broad coverage. This study aimed to conduct a comprehensive exploration on the intensity and coherence information of three Advanced Land Observing Satellite-2 (ALOS-2) SAR images, for rapid massive landslide mapping in a pixel level, in order to provide a reference for future applications. Applied data were two pre-event and one post-event high-resolution ALOS-2 products. Studied area was in the east of Iburi, Hokkaido, Japan, where massive shallow landslides were triggered in the 2018 Hokkaido Eastern Iburi Earthquake. Potential parameters, including intensity difference (d), co-event correlation coefficient (r), correlation coefficient difference ( $∆r$ ), co-event coherence ( $\mathsf{\gamma }$ ), and coherence difference ( $∆\mathsf{\gamma }$ ), were first selected and calculated based on a radar reflection mechanism, to facilitate rapid detection. Qualitative observation was then performed by overlapping ground truth landslides to calculated parameter images. Based on qualitative observation, an absolute value of d ( $d_{abs1}$ ) was applied to facility analyses, and a new parameter ( $d_{abs2}$ ) was proposed to avoid information loss in the calculation. After that, quantitative analyses of the six parameters ( $d_{abs1}$ , $d_{abs2}$ , r, $∆r$ , $\mathsf{\gamma }$ and $∆\mathsf{\gamma }$ ) were performed by receiver operating characteristic. $d_{abs2}$ and $∆r$ were found to be favorable parameters, which had the highest AUC values of 0.82 and 0.75, and correctly classified 69.36% and 64.57% landslide and non-landslide pixels by appropriate thresholds. Finally, a discriminant function was developed, combining three relatively favorable parameters ( $d_{abs2}$ , $∆r$ , and $∆\mathsf{\gamma }$ ) with one in each type, and achieved an overall accuracy of 74.31% for landslide mapping.

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-km resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011–2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R²) and root mean square error (RMSE), and the results indicate high accuracy for training (R²: 0.95, RMSE: 5.58 μg/m³), validation (R²: 0.78, RMSE: 11.18 μg/m³), and testing (R²: 0.71, RMSE: 8.79 μg/m³) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas.

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 introduces a novel method for estimating hourly concentrations of particulate matter 2.5 μm (PM _2.5 ) using satellite data. The pollution control department of Thailand collected hourly PM _2.5 data nationwide in 2020. NASA's Earth Observing System Data and Information System encompasses all moderate resolution imaging spectroradiometer satellite data. We employed aerosol optical depth (AOD), land surface temperature (LST), normalized difference vegetation index (NDVI), and elevation (EV) in our analysis. The approach incorporates a weighted sum contrast log-linear regression model that integrates satellite data, allowing for the examination of small-scale hourly variations in PM _2.5 concentrations. The results reveal a high correlation between hourly PM _2.5 levels and AOD, LST, NDVI, EV, time, and week of the year in terms of spatial distribution, with an R ² value of 53.8%. The mean hourly PM _2.5 concentration was 23.1 μ g/m ³ , displaying elevated concentrations during the dry season (November to March) and peak hours (8 to 11 a.m . and 8 to 12 p.m .). Positive correlations between AOD and PM _2.5 , especially when AOD exceeded 0.52, and between LST and PM _2.5 , particularly when LST exceeded 33.9 °C, along with NDVI ranging from −0.08 to 0.18 and EV above 67.9 m, resulted in higher PM _2.5 levels than the overall mean. The proposed model proved valuable for interpretation and practical application, offering comparable estimated hourly PM _2.5 concentrations at a 1-km resolution with monitoring stations. This suggests that researchers or policymakers may use the model to understand hourly PM _2.5 fluctuations and their impact on human health and the environment.