Presents corrections to the paper, (Consistent Geometric Patterns Observed in Three-Dimensional Displacement of Nationwide GNSS Stations in Japan).

Rapid urbanization in Southeast Asia poses substantial challenges to sustainable development; however, long-term monitoring remains constrained by multi-sensor inconsistencies, persistent tropical cloud cover, and validation uncertainties. This study developed an integrated framework to map and compare urban expansion over two decades (2000–2024) across five selected megacities—Bangkok, Hanoi, Manila, Jakarta, and Ho Chi Minh City—assessing areal expansion and patch connectivity patterns. Urban areas were detected using spectral indices (Normalized Difference Built-up Index and Urban Index) from harmonized Landsat 5, 7, 8, and 9 imagery and corrected using a novel cascading Temporal Pixel Locking algorithm enforcing monotonic growth through backward propagation from the 2020–2024 reference period. Validation against three complementary reference datasets yielded overall accuracies of 0.81–0.92 and F1-scores of 0.82–0.92. Results reveal a total expansion of 1384 km2 (average growth of 52.7%), with city-level rates ranging from 16.9% in Manila to 75.1% in Ho Chi Minh City. Patch-based analysis identified distinct development phases from isolated emergence to spatially connected saturation. Six-dimensional comparative analysis identified three structural typologies via Ward linkage hierarchical clustering: Mature Dense Core cities (Manila and Jakarta), characterized by high maturity and strong spatial compactness; Fragmented Rapid Expansion cities (Hanoi and Ho Chi Minh City), defined by high growth rates and low structural coherence; and Bangkok as a singleton High-Intensity Expansion Hub, distinguished by exceptionally high patch expansion activity relative to its urban maturity. Jakarta achieved the highest Urban Development Score (0.74), whereas Hanoi recorded the lowest (0.24). The open-source framework is transferable to rapidly urbanizing tropical regions worldwide.

In the above article [1], we made a mistake in (2). We correct it as follows. \begin{align*} \left[ {\begin{array}{c} {V_{SAR}^{ASC}}\\ {V_{SAR}^{DES}}\\ 0 \end{array}} \right] \!\!\!=\!\!\! & \left[ {\begin{array}{ccc} { - \cos {{\varphi }_{ASC}}\sin {{\theta }_{ASC}}}&{\sin {{\varphi }_{ASC}}\sin {{\theta }_{ASC}}}&{\cos {{\theta }_{ASC}}}\\ { - \cos {{\varphi }_{DES}}\sin {{\theta }_{DES}}}&{\sin {{\varphi }_{DES}}\sin {{\theta }_{DES}}}&{\cos {{\theta }_{DES}}}\\ 0&1&0 \end{array}}\! \right] \\ \left[ {\begin{array}{c} {{{V}^e}}\\ {{{V}^n}}\\ {{{V}^u}} \end{array}} \right] =& \left[ {\begin{array}{ccc} {u_e^{ASC}}&{u_n^{ASC}}&{u_u^{ASC}}\\ {u_e^{DES}}&{u_n^{DES}}&{u_u^{DES}}\\ 0&1&0 \end{array}} \right]\left[ {\begin{array}{c} {{{V}^e}}\\ {{{V}^n}}\\ {{{V}^u}} \end{array}} \right]. \tag{2} \end{align*}

Because of its high reproductivity, PolSAR is a suitable sensor for change detection in urban areas. Although many methods of change detection have been proposed, methods focused on polarimetric states transformation are rarely adapted. Through eigenvalue calculations, polarimetric basis which maximizes the polarimetric sensitivity can be calculated, and if this basis is fixed, quantitative change detection is available. The result from this method shows the obvious change in the target area, which is 'Umekita project 2nd' in Osaka city. However, changes outside the target area were also larger so that the change detection was not very effective from a relative viewpoint. To solve this problem, algorithms which surpass unnecessary changes in urban areas should be developed, and deeper understanding of scattering mechanisms in urban areas is needed.

Man-made structure extraction is crucial for urban planning, environmental monitoring, and disaster management. While optical sensors are affected by weather and lighting conditions, synthetic aperture radar (SAR) provides consistent imaging capabilities. This study utilizes Polarimetric Synthetic Aperture Radar (PolSAR) data and advanced scattering decomposition to enhance classification accuracy. Microwave scattering data from concrete blocks at various angles were collected in an anechoic chamber to train machine learning models, which were then applied to the Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) satellite imagery. To address misclassification between man-made structures and natural areas, we implemented a three-step refinement: (1) confidence-based Polarimetric Orientation Angle (POA) correction and (2) adaptive scattering decomposition to redistribute power between double-bounce (Pd) and volume (Pv) scattering, and (3) region of interest (ROI)-based statistical refinements further improved class separation. The method significantly reduced misclassification errors, demonstrating its effectiveness in extracting man-made structures.

This study explored the possibility of using currently available data to forecast when and where a large earthquake could occur in the near future. Such forecasts require measurements and understanding of past and current three-dimensional (3D) displacement patterns. We analyzed the geometric patterns of 3D displacement using coordinate data from over 1,300 global navigation satellite system stations in Japan. We found that the monthly displacement velocities of a station were on a single flat plane, although a significantly large earthquake had occurred, and the normal planes of all stations were on a single quadratic curve surface. Moreover, the sum of the absolute differences in 15-d velocities indicated a significant displacement over a wide area after the occurrence of a large earthquake.

In Japan, where workers are in short supply, wide-area surveillance using SAR (Synthetic Aperture Radar) imagery is attracting attention. However, it is not known where and how many PS points can be obtained until PSInSAR analysis is performed, and as a result, the PS points may not be available in the area to be analyzed. Therefore, in this study, we applied PSInSAR to SAR images generated by a SAR image simulator to verify whether the distribution of PS points can be known in advance. As a result, the validity of the method was demonstrated, since the results showed the same trend as that of real images. In the future, we will examine a quantitative evaluation method and verify whether it is effective in other regions as well.

Flood early warning systems (FEWS) play a crucial role in mitigating flood damage. To optimize their effectiveness, it is important to understand how people respond to warnings and prepare for flooding events. The key factors influencing social preparedness include (1) direct and (2) indirect experiences of floods and (3) trust in warnings. However, existing socio-hydrological models do not incorporate all these elements. To include these elements for social preparedness, we propose a stylized model that allows multiple regions to influence one another (i.e., regional interactions). We investigate the dynamics of social preparedness in a society composed of regions with varying infrastructure levels (e.g., levee heights) and explore strategies for developing a socially efficient FEWS. Numerical analyses reveal that in a society that has a region characterized by a low infrastructure level (i.e., a region with frequent floods), regional interactions lead to a pronounced cry wolf effect due to false alarms from other regions, diminishing social preparedness in the low-infrastructure region. These interactions also prevent a warning strategy that optimizes the natural science-based index (i.e., threat score) from maximizing social efficiency. Conversely, in a society that has a region characterized by a high infrastructure level (i.e., a region with infrequent floods), regional interactions enhance the efficiency of FEWS by improving social preparedness through indirect experiences with floods. These findings suggest that as regional heterogeneity increases, it becomes increasingly vital for forecasters to consider social aspects (e.g., people's experiences, trust, and interactions) when establishing a socially efficient FEWS.

Pavement management has traditionally relied on human-based decisions. In many countries, however, the pavement stock has recently increased, while the number of management experts has declined, posing the challenge of how to efficiently manage the larger stock with fewer resources. Compared to efficient computer-based techniques, human-based methods are more prone to errors that compromise analysis and decisions. This research built a robust probabilistic pavement management model with a safety metric output using inputs from image processing tested against the judgment of experts. The developed model optimized road pavement safety. The study explored image processing techniques considering the trade-off between processing cost and output accuracy, with annotation precision and intersection over union (IoU) set objectively. The empirical applicability of the model is shown for selected roads in Japan.

Persistent scatterer interferometry of multitemporal synthetic aperture radar (SAR) satellite images provides deformation along the radar line-of-sight direction. Three-dimensional (3-D) land deformation can be estimated by combining observations from multiple sources and directions, such as multi-temporal SAR images acquired in ascending and descending orbits, with global navigation satellite system (GNSS) data. A bias can be included in the 3-D deformation estimates when the interpolated GNSS data are inconsistent with the actual local deformation. In this study, we propose an optimal method for estimating 3-D land subsidence from SAR images acquired using dual orbits and GNSS data and detecting such bias. It calculates two sets of 3-D ground deformation velocities: one from ascending images and GNSS data and the other from descending images and GNSS data. The subtracted ground deformation velocities have significant values when the interpolated GNSS are inconsistent with ground deformation velocities from SAR observations. We demonstrated the validity using simulated images. In addition, we examined the number of observation equations that had the least effect on the bias. We applied this method to the Kansai International Airport in Japan using advanced land observing satellite 2/phased array type L-band SAR 2 ascending and descending images. We assessed the validity of the observation equation models based on the viewpoints of root mean squared errors and the relative residual error generated in the estimation. We found that when the inconsistency due to the interpolation is available, the optimal model uses four observation variables: two LOS deformation velocities and interpolated East-West and North-South deformation velocities from GNSS observations.

To prevent damage from landslide disasters, traffic regulation based on records is implemented before disasters occur in Japan. Logistics and accordingly economic activities are halted once the traffic regulation is implemented. There are problems that the operation of the traffic regulation tends to be redundant in terms of temporal duration and spatial coverage. In this paper, to consider the effect of topography and land deformation and resolve the problems of redundant traffic regulation, we attempted to predict the land deformation using spatio-temporal statistical models whose objective variable was deformation estimated PSInSAR and explanatory variables were accumulated rainfall and maximum gradient angle. Three statistical models: low-rank GP model, separable covariance model, and product-sum covariance model were used. According to the results of experiments, three spatio-temporal models showed similar predictions; relatively small deformations were well fitted while relatively large deformations were poorly fitted. Since land deformation due to landslides is relatively large, it should be considered the measures to improve the prediction of larger deformations.

Electric shorting induced by tall vegetation is one of the major hazards affecting power transmission lines extending through rural regions and rough terrain for tens of kilometres. This raises the need for an accurate, reliable, and cost-effective approach for continuous monitoring of canopy heights. This paper proposes and evaluates two deep convolution neural network (CNN) variants based on Seg-Net and Res-Net architectures, characterized by their small number of trainable weights (nearly 800,000) while maintaining high estimation accuracy. The proposed models utilize the freely available data from Sentinel-2, and a digital surface model to estimate forest canopy heights with high accuracy and a spatial resolution of 10 metres. Various factors affect canopy height estimation, including topography signature, dataset diversity, input layers, and model structure. The proposed models are applied separately to two powerline regions located in the northern and southern parts of Thailand. The application results show that the proposed Encoder-Decoder CNN Seg-Net model presents an average mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination R 2 of 1.38 m, 1.85 m, and 0.87, respectively, and is nearly 4.8 times faster than the CNN Res-Net model in conversion. These results prove the proposed model's capability of estimating and monitoring canopy heights with high accuracy and fine spatial resolution.

高速道路の維持管理業務において，道路や橋脚の劣化に対する補修だけでなく，災害発生時の事前・事後対策も重要な業務である．頻発する土砂災害への備えとして，本論文では人工衛星に搭載された合成開口レーダ(synthetic aperture radar: SAR)で取得された時系列画像を用いて，重点的に監視すべき箇所を絞り込む手法を提案する．時系列SAR解析の手法を用いて，強い散乱を示す地点の累積地盤変動量を推定し，その後高速道路沿いの一定範囲の平均累積地盤変動量を算出するものである．本研究での推定結果は，実際に土砂災害が発生した箇所において前年時の豪雨後から変動が始まっている様子を示している．よって提案手法は高速道路管理者の維持管理業務に取り入れられる可能性を有する実用的な手法と言える．

In recent years, Japan has experienced a lot of accidental heavy rains inducing landslides. To reduce damage from landslides, it is necessary to take appropriate countermeasures through constant monitoring of ground deformation. Among various methods for monitoring ground deformation, SAR is superior in that it can observe a wide area with high accuracy. However, SAR image analysis is not always appropriate for some geomorphological conditions. In this study, we have developed a simulator capable of generating phase components in SAR images with arbitrary topography and ground deformation and show the usefulness of the simulator. It is confirmed that the simulated SAR images, generated based on the basic equation of phase variation caused by reflections from features, can be applied to existing ground deformation estimation methods in the same way as authentic SAR images. We also simulated some cases in which the topography and ground deformation were rotated relative to the satellite orbit. The experiments found that the characteristics and range of the ground deformation, which can be predicted from the line-of-sight (LOS) deformation estimated by PSInSAR, differed depending on the relationship between the satellite orbit and the direction of the movement.