Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

Road profile extraction and analysis are essential tasks in transportation asset management. Current approaches use vehicle-borne laser sensors in order to precisely measure the variations in elevation along a specific route. However, a complicated sensor mechanism, such as in the mobile light detection and ranging (LiDAR) system, is involved and the resulting quality is compromised owing to multiple factors. In this study, an image-based approach for extracting road profiles is proposed. It requires only a single camera sensor and a low-cost laser module and is capable of collecting road profiles along both the longitudinal and transverse directions. A detailed methodology is first presented in this paper, followed by a simulation evaluation and a case study. The case study illustrates that the quality of the extracted profiles based on the proposed approach achieves millimeter accuracy. Consequently, an accurate and cost-efficient road profile analysis becomes possible when the proposed approac...

Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.

We report on the blueshift of energy gap and the nitrogen rearrangement resulting from the thermal treatment of GaAsSbN samples. The composition ranges of N and Sb are from 2% to 3% and from 7% to 11%, respectively. By comparing the photoluminescence peak energy and absorption energy gap, we found that the thermal annealing leads to compositional homogenization. When the annealing temperature is higher than $750°\text{C}$ , the local vibration mode (LVM) absorbance of isolated substitutional nitrogen ${\text{N}}_{\text{As}}$ starts to increase with the temperature. This finding supports the argument that nitrogen pairs thermally dissociate to isolated nitrogen. In additions, the increase of LVM absorbance accompanies the blueshift of energy gap, suggesting that the blueshift is the consequence of the nitrogen rearrangement. The decomposition of nitrogen pairs has also been observed by photoreflectance spectroscopy (PR). Multiple peaks were resolved from the PR spectra of as-grown GaAsSbN. They are relevant to transitions from regions with different nitrogen pairings and isolated nitrogen. As the annealing temperature increases, the peaks gradually merge to that of isolated nitrogen, indicating the decomposition of nitrogen pairings.

Feature conjugation is a major task in modern-day spatial analysis and contributes to efficient integration across multiple data sets. In this study, an efficient approach that utilizes the intensity information provided in most light detection and ranging (LIDAR) data sets for feature conjugation is proposed. First, a two-dimensional (2D) intensity map is generated based on the original intensity-coded LIDAR observables in three-dimensional (3D) space. The 2D map is further transformed into a regularly sampled image, and an image feature detection technique is subsequently applied to identify point conjugations between a pair of intensity maps. Finally, the paired conjugations in the image space are mapped backward into the LIDAR space, and the object coordinates of the conjugate points can be verified and obtained. Based on the numerical results from a real world case study, it is illustrated that by fully exploring the existing spectral information, a reliable feature conjugation across multipl...