High-resolution Data Research Articles

Coffee bean graded based on deep net models

Coffee is a widely consumed beverage, and sorting coffee beans is a critical process that ensures high-quality graded coffee products. Coffee beans were graded into nine grades in robusta types. To automate the grading process, a deep learning-based approach was developed using a large dataset of high-resolution images and data augmentation techniques. In contrast to previous studies focusing on robusta type graded into six coffee bean grads, our research extends this framework by employing robusta type into nine grades with an outperformed accuracy. The proposed work uses four deep learning models, namely residual network 34(Resnet34), inception version 3 (Inception v3), efficient network bayesian optimization (EfficientNet-B0), and visual geometry group-16(VGG-16), where trained and evaluated for coffee bean classification into nine grades. The EfficientNet-B0 model exhibited outperformed accuracy, achieving 100% in distinguishing good and bad coffee beans, even in challenging lighting and background conditions.

Geo-spatial epidemiology of gallbladder cancer in Bihar, India

Recently, a substantial increase in gallbladder cancer (GBC) cases has been reported in Bihar, India. The region's groundwater can naturally contain harmful concentrations of arsenic, which appears to be epidemiologically linked to the unusually high incidence. However, the root causes remain largely unexplored. Recent findings of uranium in the state's groundwater may also have associations. This study investigates the geo-spatial epidemiology of GBC in Bihar, India—with a focus on the correlation between environmental carcinogens, particularly arsenic and uranium in groundwater, and the incidence of GBC. Utilizing data from 8460 GBC patients' registration records over an 11-year period at a single health center, the research employs Semi-parametric Geographically Weighted Poisson Regression (S-GWPR) to account for non-stationarity associations and explores significant factors contributing to GBC prevalence at a subdistrict level. The S-GWPR model outperformed the standard Poisson regression model. The estimates suggest that arsenic and uranium concentrations in groundwater did not present significant associations; however, this could be due to the lower resolution of this data at the district level, necessitating higher resolution data for accurate estimates. Other socio-environmental factors included demonstrated significant regional heterogeneity in their association with GBC prevalence. Notably, each 1 % increase in the coverage of well- and canal-irrigated areas is associated with a maximum of 3.0 % and 5.2 % rise in the GBC incidence rate, respectively, likely attributable to carcinogen exposure from irrigation water. Moreover, distance to the health center and domestic electricity connections appear to influence the number of reported GBC cases. The latter suggests that access to electricity might have facilitated the use of groundwater pumps—increasing exposure to carcinogens. The results underscore the necessity for targeted health policies and interventions based on fine-resolution spatial analysis, as well as ongoing environmental monitoring and research to better understand the multifaceted risk factors contributing to GBC.

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.

In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Advancing air quality monitoring: A low-cost sensor network in motion – Part I

In urban areas, high levels of air pollution pose significant risks to human health, emphasising the need for detailed air quality (AQ) monitoring. However, traditional AQ monitoring relies on the data from Reference Monitoring Stations, which are sparsely distributed and provide only hourly or daily data, failing to capture the spatial and temporal variability of air pollutant concentrations. Addressing this challenge, we introduce in this article the ExpoLIS system, an all-weather mobile AQ monitoring system that integrates various AQ low-cost sensors (LCSs), providing high spatio-temporal resolution data. This study demonstrates that the inclusion of an extended sampling device may mitigate the effect of the meteorological parameters and other disturbances on readings. At the same time, it did not reduce the quality of the data, both in static conditions and in motion, as we were able to maintain a certain level of agreement between the LCSs. In conclusion, the ExpoLIS system proves its versatility by enabling the collection of large quantities of accurate data, allowing a deeper understanding of the AQ dynamics in urban environments.

Submarine morphology of Pantelleria volcano: The interplay between volcanic and erosive-depositional processes modulated by sea-level fluctuations.

High-resolution multibeam data integrated with seismic reflection profiles are used to identify and characterize the main primary volcanic and erosive-depositional features along the submarine part (about the 80%) of the active Pantelleria volcano located in the Sicily Channel. Volcanic features include lava flows, cones and elongated ridges. Lava flows are mainly recognized over the insular shelf, while volcanic cones and ridges are mostly concentrated along the steep submarine flanks, especially along the wider SE and NW ones. A strong volcano-tectonic interaction is envisaged for their formation, as indicated by their preferential elongation or alignment along the (main) SE-NW and (secondary) SW-NE directions that have controlled the evolution of the whole volcanic edifice. Erosive-depositional features mainly include small-scale landslide scars and narrow gullies affecting the edge of the insular shelf and overlying submarine depositional terraces. Gullies sometimes merge downslope in larger channels, whose formation is primarily controlled by the distribution of volcanic features and/or shelf sectors characterized by different age or lithologies. Based on the marked morphological differences between the different flanks of the Pantelleria volcano, we infer an overall migration of the volcanic activity from SE to NW over time. This migration is apparently in contrast with the presence of a much wider but shallower NW insular shelf with respect to the SE one. This anomaly can be explained through a two-stage model, with the formation, in the NW sector, of a polygenic shelf rejuvenated by volcanic progradation during the last eustatic hemicycle. The different depths of the insular shelf edge around the island also provide insights on vertical deformations that affected the Pantelleria volcano during the Late-Quaternary.

Pediatric three-dimensional quantitative cardiovascular computed tomography.

High-resolution, isotropic, 3-dimensional (D) data from pediatric cardiovascular computed tomography (CT) offer great potential for the accurate quantitative evaluation of pediatric cardiovascular and pulmonary vascular diseases. Recent pilot studies using pediatric 3-D cardiovascular CT have shown promising results in assessing cardiac function in conditions such as tetralogy of Fallot, cardiac defects with a hypoplastic ventricle, Ebstein anomaly, and in quantifying myocardial mass. In addition, the quantitative assessment of pulmonary vascularity is useful for evaluating differential right-to-left pulmonary vascular volume ratio, the effectiveness of pulmonary angioplasty, and predicting pulmonary hypertension. These initial experiences could broaden the role of pediatric cardiovascular CT in clinical practice. Furthermore, the current barriers to its widespread use, pertinent solutions to these problems, and new applications are discussed. In this review, the 3-D quantitative evaluations of cardiac function and pulmonary vascularity using high-resolution pediatric cardiovascular CT data are illustrated.

Research on Polar Operational Limit Assessment Risk Indexing System for Ships Operating in Seasonal Sea-Ice Covered Waters

The Polar Operational Limit Assessment Risk Indexing System (POLARIS) has been established as a viable framework for assessing operational capabilities and associated risks in polar waters. Despite its inherent suitability for high-latitude territories, ships navigating through seasonal ice-infested waters at lower latitudes also encounter critical safety, environmental, and economic issues exacerbated by the presence of ice. This necessitates a reliable and adaptable methodology that can serve as a reference for devising effective countermeasures. This study evaluated the use of POLARIS in the intricate ice conditions prevalent in the northern navigable waters (channels and anchorages) within Liaodong Bay of the Bohai Sea, located at relatively low latitudes. Using GF-4 satellite imagery, ice conditions were collected, and the POLARIS methodology was employed to calculate Risk Index Outcome (RIO) values for non-ice-strengthened vessels during the winter season of 2021–2022. The results showed that sectors 3, 4, 5, 7, 9, 10, and 11 within the northern part of Liaodong Bay exhibited a higher risk, with sectors 5 and 10 exhibiting the most significant risk, while sectors 1 and 2 demonstrated relatively lower risk levels. The concurrence of these findings with acknowledged ice patterns and local maritime practices confirms the applicability of the POLARIS methodology in saline, seasonally ice-covered seas. Notably, the combination of POLARIS with high-resolution satellite imagery facilitated a more precise and rapid assessment of ice risk, thereby enhancing situational awareness and informing decision-making processes in maritime operations under icy conditions. In addition, this study provides preliminary evidence that POLARIS is suitable for fine-scale scenarios, in addition to being applicable to sparse-scale scenarios, such as polar waters, especially with high-resolution ice data. At the same time, this study highlights the potential of POLARIS as a disaster prevention strategy and a tool for the maritime industry to address ice challenges.

Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics

Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70–80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine.

Image analysis of acoustic data and interpretation of rock stress orientations for geothermal exploration in Gothenburg borehole GE-1, SW Sweden

Abstract This study contributes to geothermal exploration in 1660–1520 Ma old, reworked bedrock in Sweden. Our primary objectives are to constrain the orientation of horizontal stresses, and to discuss implications for geothermal exploration. High-resolution acoustic televiewer image data reveal the downhole distribution of stress indicators (borehole breakouts, drilling-induced fractures and petal centreline fractures) and pre-existing structures (natural fractures, foliation). About 135 m of stress indicators are measured from 0.2–1.0 km. The results suggest a uniform NNW–SSE mean maximum horizontal stress orientation. A total of 1525 pre-existing structures (natural fractures, foliation) are mapped in borehole GE-1. The prevailing stress regime controls whether natural fractures and foliation are well-oriented for stimulation. For strike-slip and normal faulting stress regimes, well-oriented fractures steeply dip towards the WSW. For a reverse faulting stress regime, shallow dipping fractures are well-oriented for stimulation. The downhole distribution of stress indicators and other stress measurements in the region and other parts of Fennoscandia tentatively suggest a strike-slip stress regime, but additional studies are needed to constrain the complete stress field at study depth and towards engineered geothermal systems reservoir target depths. Our secondary objective is to highlight that interpretation of high-resolution acoustic data, particularly in metamorphic crystalline rocks, is subjective and that more guidelines for data interpretation are needed. The interactive interpretation of the images is based on visual analyses of complex pre-existing structures and stress indicators with highly variable shapes. The application of three methods for data analyses in the GE-1 borehole proposes that drilling-induced fractures are little influenced by the method applied. Interpretations on individual borehole breakout azimuths may, however, result in over 10° differences in orientation.

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0–125 sheep/km2, and the high-density area was mainly distributed in Khuvsgul, Arkhangai, Bulgan and part soums of Orkhon; (3) horses and cattle density were concentrated in 0–25 head/km2, mainly distributed in the southwest and central parts of the basin, with few high-density areas. This indicates that the RF simulation results effectively depict the characteristics of Selenge River Basin. Further study supported by Geodetector showed human activity was the main driver of livestock distribution in the basin. This study is expected to provide fundamental support for the precise regulation of animal husbandry in the Mongolian Plateau or other large steppe regions worldwide.

Spatiotemporal Characteristics and Rainfall Thresholds of Geological Landslide Disasters in ASEAN Countries

Drawing upon a comprehensive global database of landslides and utilizing high-resolution IMERG satellite precipitation data, this study investigates the spatial and temporal variations of landslide occurrences across the member states of the Association of Southeast Asian Nations (ASEAN). This study constructs a region-specific, graded warning system by formulating an average effective intensity–duration (I–D) rainfall threshold curve for each ASEAN member. Examination of 1747 landslide events spanning from 2006 to 2018 illustrates a significant association between the frequency of landslides in ASEAN regions and the latitudinal movement of local precipitation bands. Incidences of landslides hit their lowest in March and April, while a surge is observed from October to January, correlating with the highest mortality rates. Geographical hotspots for landslide activity, characterized by substantial annual rainfall and constrained landmasses, include the Philippine archipelago, Indonesia’s Java Island, and the Malay Peninsula, each experiencing an average of over 2.5 landslides annually. Fatalities accompany approximately 41.4% of ASEAN landslide events, with the Philippines and Indonesia registering the most substantial numbers. Myanmar stands out for the proportion of large-scale landslide incidents, with an average casualty rate of 10.89 deaths per landslide, significantly surpassing other countries in the region. The I–D rainfall threshold curves indicate that the Philippines experienced the highest precipitation levels before landslide initiation, whereas Myanmar has the threshold set at a considerably lower level.

Examining the Percent Canopy Cover and Health of Winter Wheat in No-Till and Conventional Tillage Plots Using a Drone

The percent canopy cover and health of winter wheat are important crop performance indicators. Thus, understanding how tillage management practices affect these indicators is beneficial for improving crop performance and consequently yield. The availability of high-resolution drone data with spectral characteristics provides an opportunity to examine the percent canopy cover and health of winter wheat in different tillage systems. This is because the use of drones provides real-time high spatial resolution and temporal images to effectively monitor winter wheat conditions throughout the growing season. Nonetheless, very limited studies have utilized drone data for assessing the percent canopy cover and health conditions of winter wheat for different tillage practices. This study aimed to examine the percent canopy cover and health of winter wheat in no-till and conventional tillage plots using a drone. We used the mean Normalized Difference Vegetation Index (NDVI) ± Standard Deviation (SD) (0.89 ± 0.04) of winter wheat for the growth stages of tillering, jointing, and boot/heading to generate the percent wheat canopy cover. The Normalized Difference Red-Edge (NDRE) produced for winter wheat at the middle and late growth stages was used as a proxy for wheat health condition. We found that the mean percentage canopy cover of winter wheat was about 4% higher in no-till compared to conventional tillage plots in most of the growing season. The mean NDRE ± standard error (SE) of winter wheat was about 0.44 ± 0.01 and 0.43 ± 0.01 for no-till and conventional tillage plots, respectively, during the mid- and late growth stages. There was no significant difference in either the percent canopy cover or health of winter wheat between no-till and conventional tillage plots. The results generated in this study could be used to support farmers’ decision-making process regarding tillage practices and wheat crop performance.

Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes

The rapid global distribution of COVID-19 vaccines, with over a billion doses administered, has been unprecedented. However, in comparison to most identified clinical determinants, the implications of individual genetic factors on antibody responses post-COVID-19 vaccination for breakthrough outcomes remain elusive. Here, we conducted a population-based study including 357,806 vaccinated participants with high-resolution HLA genotyping data, and a subset of 175,000 with antibody serology test results. We confirmed prior findings that single nucleotide polymorphisms associated with antibody response are predominantly located in the Major Histocompatibility Complex region, with the expansive HLA-DQB1*06 gene alleles linked to improved antibody responses. However, our results did not support the claim that this mutation alone can significantly reduce COVID-19 risk in the general population. In addition, we discovered and validated six HLA alleles (A*03:01, C*16:01, DQA1*01:02, DQA1*01:01, DRB3*01:01, and DPB1*10:01) that independently influence antibody responses and demonstrated a combined effect across HLA genes on the risk of breakthrough COVID-19 outcomes. Lastly, we estimated that COVID-19 vaccine-induced antibody positivity provides approximately 20% protection against infection and 50% protection against severity. These findings have immediate implications for functional studies on HLA molecules and can inform future personalised vaccination strategies.

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Urban lakes change extraction using time series GaoFen-1 satellite imagery

Abstract. Urban lakes serve an indispensable role in maintaining the ecological balance of cities, ensuring flood safety, and providing recreational spaces for tourism. With the development of human activities and economic, the extent of urban lakes are inevitably influenced. Currently, the ability to detect detailed temporal changes in urban lake areas using high resolution data still has limitations. This study proposed a novel method by combining time series Gaofen-1 (GF-1) remote sensing data and random forest machine learning algorithm to explore the urban lakes change Zhushan Lake located in Wuhan. The research conducted the extraction of surface water for Zhushan Lake and its surrounding pit-ponds from 2013 to 2020. And then, a quantitative analysis of the characteristics and driving factors of lake changes is conducted. We find that (1) the accuracy of surface water extraction using the random forest classification method consistently exceeded 96%. The Kappa coefficient ranges from a minimum of 0.86 to a maximum of 0.99. (2) A noticeable decline was observed in the water areas of Zhushan Lake and its surrounding pit-ponds, predominantly along the northwestern shoreline and in the eastern pond regions. This decline is primarily attributed to pressures from building construction. The methodology proposed in this study is suitable for the area management of lakes in urban areas.

Practical considerations for using low-cost sensors to assess wildfire smoke exposure in school and childcare settings.

More frequent and intense wildfires will increase concentrations of smoke in schools and childcare settings. Low-cost sensors can assess fine particulate matter (PM2.5) concentrations with high spatial and temporal resolution. We sought to optimize the use of sensors for decision-making in schools and childcare settings during wildfire smoke to reduce children's exposure to PM2.5. We measured PM2.5 concentrations indoors and outdoors at four schools in Washington State during wildfire smoke in 2020-2021 using low-cost sensors and gravimetric samplers. We randomly sampled 5-min segments of low-cost sensor data to create simulations of brief portable handheld measurements. During wildfire smoke episodes (lasting 4-19 days), median hourly PM2.5 concentrations at different locations inside a single facility varied by up to 49.6 µg/m3 (maximum difference) during school hours. Median hourly indoor/outdoor ratios across schools ranged from 0.22 to 0.91. Within-school differences in concentrations indicated that it is important to collect measurements throughout a facility. Simulation results suggested that making handheld measurements more often and over multiple days better approximates indoor/outdoor ratios for wildfire smoke. During a period of unstable air quality, PM2.5 over the next hour indoors was more highly correlated with the last 10-min of data (mean R2 = 0.94) compared with the last 3-h (mean R2 = 0.60), indicating that higher temporal resolution data is most informative for decisions about near-term activities indoors. As wildfires continue to increase in frequency and severity, staff at schools and childcare facilities are increasingly faced with decisions around youth activities, building use, and air filtration needs during wildfire smoke episodes. Staff are increasingly using low-cost sensors for localized outdoor and indoor PM2.5 measurements, but guidance in using and interpreting low-cost sensor data is lacking. This paper provides relevant information applicable for guidance in using low-cost sensors for wildfire smoke response.

Qinghai-Tibet Plateau Glacier-DEM product derived from the Chinese stereo mapping satellites

Abstract. The Digital Elevation Model (DEM) is an important basic data for glacier research on the Qinghai-Tibet Plateau. With the rapid development of China 's stereo mapping satellite, it is possible to obtain high-precision DEM in the glacier area of the Qinghai-Tibet Plateau. In this study, the stereo images and laser altimetry data of ZY-3 and GF-7 satellites were comprehensively used, and the matching and joint adjustment methods of laser elevation control points and stereo images were proposed. A high-precision DEM product in glacier area was established, and the elevation accuracy of DEM was verified by using UAV data and ICESat-2 data. The results show that the accuracy of GF-7 DEM is better than 1.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 2.8 m. The accuracy of ZY-3 DEM is better than 3.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 5.0 m. Compared with the open source DEM with medium spatial resolution, the DEM elevation accuracy based on domestic stereo mapping satellite images is better, and the grid is finer and more detailed to describe the texture characteristics of the end of the glaciers. Compared with the high spatial resolution data set HMA DEM, the ZY-3 DEM is slightly worse and the GF-7 DEM is better, and both the ZY-3 DEM and GF-7 DEM are better than the HMA DEM in terms of coverage integrity in the Qinghai- Tibet Plateau. Chinese stereo mapping satellites can generate high-precision DEM in the glacier area, and can provide more accurate and reliable terrain reference data than the existing open free data for the study of glaciers in the Qinghai-Tibet Plateau.

Orthorectification of Bridges from High Resolution Satellite Images

Abstract. High-resolution satellite imagery requires high-resolution height reference data to create orthorectified images. High-precision digital surface data and LiDAR data are expensive to acquire quickly and accurately. Facility databases, on the other hand, are very efficient because once established, the information can be utilized persistently. The temporal resolution between the structure in the database and the captured image affects the ortho resolution created. Landmarks such as bridges are rarely subject to changes after construction. This makes facility database a suitable material to generate orthophotos of bridge structures. In this paper, we used a bridge database to perform orthorectification for structures with height variations. We removed relief displacement from satellite imagery at different angles and verified orthorectification feasibility for bridges of different heights and lengths. The orthoimage was overlaid with a topographic map to verify the orthorectification visually. As a result, we confirmed that the facility database was able to generate orthophotos of the bridges. The length and slope of bridge structures indicated the need for precise database updates in the future research.

New source model for the 1771 Meiwa tsunami along the southern Ryukyu Trench inferred from high-resolution tsunami calculation

The 1771 Meiwa tsunami which struck the southern Ryukyu Islands (Sakishima Islands) had greater than 22 m run-up height, leaving about 12,000 casualties in its wake. At many places, the tsunami inundation or lack of inundation is well recorded in historical documents. Several tsunami source models have been proposed for this event using historical records as constraints of tsunami calculations. Nevertheless, the source model remains under discussion. This study re-evaluated the tsunami wave source model of the 1771 Meiwa tsunami using high-resolution (10 m mesh) bathymetric and topographical data for tsunami calculation, the latest historical record dataset, and seismological knowledge. Results demonstrated that a tsunami earthquake along the southern Ryukyu Trench was the likely cause of the 1771 event. However, it is noteworthy that assumption of a large slip with 30 m is necessary for a shallow and narrow region (fault depth = 5 km, fault width = 30 km, Mw = 8.49) of the plate boundary in the Ryukyu Trench, which is far larger than previously thought. This requirement of very large initial water level change at the source might involve not only the fault rupture along the plate boundary but also deformation by splay faults, inelastic deformation of unconsolidated sediments near the trench axis, and/or giant submarine landslides. Results also show that the effects of fault parameters on the run-up were quite different depending on the offshore coral reef width. This phenomenon strongly constrained the fault width to 30 km. Our tsunami ray tracing analysis further revealed the effects of bathymetry on tsunami propagation. It is noteworthy that meter-long huge tsunami boulders tend to be distributed along the specific coasts at which the tsunami was concentrated by bathymetric effects. This finding suggests that past tsunamis, including the 1771 event, might have affected the specific coral reefs on Sakishima Islands repeatedly, which is crucially important for understanding the heterogeneous distribution of tsunami boulders. This feature might also be useful to elucidate the effects of large tsunamis on the corals and reefs because a direct comparison of coral reefs that are damaged and not damaged by tsunami waves is testable in narrow areas in the case of the Sakishima Islands.

Mobility behaviors shift disparity in flood exposure in U.S. population groups

The current characterization of flood exposure is largely based on residential location of populations; however, location of residence only partially captures the extent to which populations are exposed to flood hazards. An important, yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. In this study, we analyzed large-scale, high-resolution location-intelligence data collected from anonymous mobile phone users to characterize human mobility patterns and the resulting flood exposure in coastal counties of the United States. We developed the metric of mobility-based exposure based on dwell time in places located in the 100-year floodplain. The results of examining the extent of mobility-based flood exposure across demographic groups reveal significant disparities across race, income, and education level groups. The results show that Black and Asian, economically disadvantaged, and undereducated populations in US coastal cities are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to that of residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among different socio-demographic groups. The results highlight that urban flood risk assessments should not only focus on the level of flood exposure to residences, but also should consider mobility-based exposure to better learn the disparities in flood exposure among social groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people's life activities and enable a better characterization of disparity in populations' exposure to flood hazards beyond their place of residence. The findings of this study have important implications for urban planners, flood managers, and city officials in terms of accounting for mobility-based flood exposure in flood risk management plans and actions.