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This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data. [ABSTRACT FROM AUTHOR]

The joint use of hyperspectral image (HSI) and Light Detection And Ranging (LiDAR) data has been widely applied for land cover classification because it can comprehensively represent the urban structures and land material properties. However, existing methods fail to combine the different image information effectively, which limits the semantic relevance of different data sources. To solve this problem, in this paper, an Attention-guided Fusion and Classification framework based on Convolutional Neural Network (AFC-CNN) is proposed to classify the land cover based on the joint use of HSI and LiDAR data. In the feature extraction module, AFC-CNN employs the three dimensional convolutional neural network (3D-CNN) combined with a multi-scale structure to extract the spatial-spectral features of HSI, and uses a 2D-CNN to extract the spatial features from LiDAR data. Simultaneously, the spectral attention mechanism is adopted to assign weights to the spectral channels, and the cross attention mechanism is introduced to impart significant spatial weights from LiDAR to HSI, which enhance the interaction between HSI and LiDAR data and leverage the fusion information. Then two feature branches are concatenated and transferred to the feature fusion module for higher-level feature extraction and fusion. In the fusion module, AFC-CNN adopts the depth separable convolution connected through the residual structures to obtain the advanced features, which can help reduce computational complexity and improve the fitting ability of the model. Finally, the fused features are sent into the linear classification module for final classification. Experimental results on three datasets, i.e., Houston, MUUFL and Trento datasets show that the proposed AFC-CNN framework achieves better classification accuracy compared with the state-of-the-art algorithms. The overall accuracy of AFC-CNN on Houston, MUUFL and Trento datasets are 94.2%, 95.3% and 99.5%, respectively. [ABSTRACT FROM AUTHOR]

The joint utilization of multi-source data is of great significance in geospatial observation applications, such as urban planning, disaster assessment, and military applications. However, this approach is confronted with challenges including inconsistent data structures, irrelevant physical properties, scarce training data, insufficient utilization of information and an imperfect feature fusion method. Therefore, this paper proposes a novel binary-tree Transformer network (BTRF-Net), which is used to fuse heterogeneous information and utilize complementarity among multi-source remote sensing data to enhance the joint classification performance of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. Firstly, a hyperspectral network (HSI-Net) is employed to extract spectral and spatial features of hyperspectral images, while the elevation information of LiDAR data is extracted using the LiDAR network (LiDAR-Net). Secondly, a multi-source transformer complementor (MSTC) is designed that utilizes the complementarity and cooperation among multi-modal feature information in remote sensing images to better capture their correlation. The multi-head complementarity attention mechanism (MHCA) within this complementor can effectively capture global features and local texture information of images, hence achieving full feature fusion. Then, to fully obtain feature information of multi-source remote sensing images, this paper designs a complete binary tree structure, binary feature search tree (BFST), which fuses multi-modal features at different network levels to obtain multiple image features with stronger representation abilities, effectively enhancing the stability and robustness of the network. Finally, several groups of experiments are designed to compare and analyze the proposed BTRF-Net with traditional methods and several advanced deep learning networks using two datasets: Houston and Trento. The results show that the proposed network outperforms other state-of-the-art methods even with small training samples. [ABSTRACT FROM AUTHOR]

In view that cities will continue to house the majority of the world's population at an increasing rate in the face of climate change, this paper studies urban resilience by examining the response history of the mean-square displacement of the citizens of large cities prior to and upon historic natural hazards strike. The recorded mean-square displacements of large numbers of cellphone users from the cities of Houston, Miami, and Jacksonville when struck by hurricanes Harvey 2017, Irma 2017, and Dorian 2019 together with the recorded mean-square displacements of the citizens of Dallas and Houston when experiencing the 2021 North American winter storm suggest that large cities of average population density when struck by natural hazards are inherently resilient. The recorded mean-square displacements presented in this study also validate a mechanical model for cities, previously developed by the authors, that is rooted in Langevin dynamics and predicts that following a natural hazard, large cities revert immediately to their initial steady-state behavior and resume their normal, preevent activities. The inherent ability of large American cities to revert to their normal, preevent, steady-state response as evidenced in this study by the recorded mean-square displacement of their citizens needs to be further explored for other cities around the world with different resources, and socioeconomic structures. [ABSTRACT FROM AUTHOR]

The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UAS). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle - Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 days was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. [ABSTRACT FROM AUTHOR]

Traffic accidents directly influence public safety and economic development; thus, the prevention of traffic accidents is of great importance in urban transportation. The accurate prediction of traffic accidents can assist traffic departments to better control and prevent accidents. Thus, this paper proposes a deep learning method named attention-based residual dilated network (ARDN), to extract essential information from multi-source datasets and enhance accident prediction accuracy. The method utilizes bidirectional long short-term memory to model sequential information and incorporates an attention mechanism to recalibrate weights. Furthermore, a dilated residual layer is adopted to capture long term information effectively. Feature encoding is also employed to incorporate natural language descriptions and point-of-interest data. Experimental evaluations of datasets collected from Austin and Houston demonstrate that ARDN outperforms a range of machine learning methods, such as logistic regression, gradient boosting, Xgboost, and deep learning methods. The ablation experiments further confirm the indispensability of each component in the proposed method. [ABSTRACT FROM AUTHOR]

Sea level rise, as a result of climate change, is expected to drive coastal hazards that could bring significant damages to coastal regions in the future. However, high uncertainties remain in the projections of sea level rise from different climate scenarios and sea level rise prediction models. Quantification and integration of these uncertainties are essential to better inform coastal planning and decision making for climate adaptation, critical for infrastructure sustainability and resilience. This paper advances knowledge cross-cutting structural engineering and climate change in the face of multihazards via a novel framework termed the Probabilistic Sea Level Rise Hazard Analysis (PSLRHA). This study uses the current generation of models and protocols from the climate science research community to better portray the future climate and project sea level rise. The aggregation process produces the probability of exceeding a specific sea level rise threshold at a certain location and facilitates the creation of the global sea level rise hazard map. The relative importance of each climate scenario and sea level rise contributing models are demonstrated via the deaggregation process. We identify the models that have most contribution to extreme sea level rise thresholds, with large fluctuations in the high thresholds among ice sheet models. Finally, we show the practical implementation of PSLRHA results via compound flooding analyses using Houston as an illustrative example. [ABSTRACT FROM AUTHOR]

Hyperspectral images contain rich features in both spectral and spatial domains, which bring opportunities for accurate recognition of similar materials and promote various fine-grained remote sensing applications. Although deep learning models have been extensively investigated in the field of hyperspectral image classification (HSIC) tasks, classification performance is still limited under small sample conditions, and this has been a longstanding problem. The features extracted by complex network structures with large model size are redundant to some extent and prone to overfitting. This paper proposes a low-rank constrained attention-enhanced multiple feature fusion network (LAMFN). Firstly, factor analysis is used to extract very few components that can describe the original data using covariance information to perform spectral feature preprocessing. Then, a lightweight attention-enhanced 3D convolution module is used for deep feature extraction, and the position-sensitive information is supplemented using a 2D coordinate attention. The above widely varying spatial–spectral feature groups are fused through a simple composite residual structure. Finally, low-rank second-order pooling is adopted to enhance the convolutional feature selectivity and achieve classification. Extensive experiments were conducted on four representative hyperspectral datasets with different spatial–spectral characteristics, namely Indian Pines (IP), Pavia Center (PC), Houston (HU), and WHU-HongHu (WHU). The contrast methods include several advanced models proposed recently, including residual CNNs, attention-based CNNs, and transformer-based models. Using only five samples per class for training, LAMFN achieved overall accuracies of 78.15%, 97.18%, 81.35%, and 87.93% on the above datasets, which has an improvement of 0.82%, 1.12%, 1.67%, and 0.89% compared to the second-best model. The running time of LAMFN is moderate. For example, the training time of LAMFN on the WHU dataset was 29.1 s, and the contrast models ranged from 3.0 s to 341.4 s. In addition, ablation experiments and comparisons with some advanced semi-supervised learning methods further validated the effectiveness of the proposed model designs. [ABSTRACT FROM AUTHOR]

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage. [ABSTRACT FROM AUTHOR]

Rock music is an integral part of American culture. This paper presents a study of sensing and analysing over 57,000 rock music live performances between 2007 and 2017. Spatial traces of 575 rock music artists performing in concerts nationwide were collected from a major music streaming platform Spotify. Location-based concert data were analysed to explore economic and geographic factors linked to the landscape of rock music live performance and to reveal the importance of population demographics and leisure and hospitality (LH) economics to the culture and music industries from a spatial aspect. Over 90% of rock concerts between 2007 and 2017 were found in 250 counties. The aim of the study is to specify and develop a model that reasonably accounts for spatial heterogeneity present in the concert data. By regressing rock concert data against demographic data and LH establishment data, ordinary least squares (OLS) models were better fitted in metropolitan counties than non-metropolitan counties. Spatial dynamics of concerts were revealed by local R2 values and the obtained structure in the form of spatial heterogeneity was then explained using geographically weighted regression (GWR) models. High population density and LH services in industry-leading cities such as New York City, Los Angeles, Chicago and Houston exhibit advantages in explaining rock concert distributions. Findings from the models reflect the live music industry's interrelationships to the LH industry and suggest LH services being essential considerations in selecting concert destinations for rock musicians. [ABSTRACT FROM AUTHOR]

This paper examines the vulnerability of road networks to two types of disruptions by modeling the percolation dynamics in road networks under different disruption scenarios. The objective of this paper is threefold: (1) to examine if the theoretical network robustness measure proposed in the literature is applicable for measuring the integrity of road networks during disruptions; (2) to unveil the impacts of network size on the overall vulnerability of road networks; and (3) to compare the performance profile of road networks to random and nonrandom types of disruptions. To that end, this study first modeled the road system in a community as a planar graph. Then, the percolation dynamic in the road network during the flood is captured by assigning different removal probabilities to nodes in the road network according to Bayes' rule that take floodplain types, node-elevation, and street-grade as inputs. In the end, an overall road network robustness measure and its temporal changes were obtained and for random and nonrandom scenarios, using road networks with different sizes. The results were compared in order to characterize the vulnerability of road networks under different scenarios. The proposed method was applied to the road network in central Houston during Hurricane Harvey. The results show that: (1) the theoretical network robustness measure is applicable to assess the road network robustness; (2) compared to the random percolation model, the probability (Bayes' rule) based percolation could lead to a greater decrease in the network robustness; and (3) the percolation profiles of the road networks with different sizes are not significantly different. The findings of this study could not only inform resilience enhancing decisions by the stakeholders but also could serve as a foundation for future vulnerability related research. [ABSTRACT FROM AUTHOR]

In this paper, we propose two multimodal circular distributions which are suitable for modeling circular data sets with two or more modes. Both distributions belong to the regular exponential family of distributions and are considered as extensions of the von Mises distribution. Hence, they possess the highly desirable properties, such as the existence of non-trivial sufficient statistics and optimal inferences for their parameters. Fine particulates (PM2.5) are generally emitted from activities such as industrial and residential combustion and from vehicle exhaust. We illustrate the utility of our proposed models using a real data set consisting of fine particulates (PM2.5) pollutant levels in Houston region during Fall season in 2019. Our results provide a strong evidence that its diurnal pattern exhibits four modes; two peaks during morning and evening rush hours and two peaks in between. [ABSTRACT FROM AUTHOR]

In epidemiology, the effective reproduction number R e is used to characterize the growth rate of an epidemic outbreak. If R e > 1 , the epidemic worsens, and if R e < 1 , then it subsides and eventually dies out. In this paper, we investigate properties of R e for a modified SEIR model of COVID-19 in the city of Houston, TX USA, in which the population is divided into low-risk and high-risk subpopulations. The response of R e to two types of control measures (testing and distancing) applied to the two different subpopulations is characterized. A nonlinear cost model is used for control measures, to include the effects of diminishing returns. Lowest-cost control combinations for reducing instantaneous R e to a given value are computed. We propose three types of heuristic strategies for mitigating COVID-19 that are targeted at reducing R e , and we exhibit the tradeoffs between strategy implementation costs and number of deaths. We also consider two variants of each type of strategy: basic strategies, which consider only the effects of controls on R e , without regard to subpopulation; and high-risk prioritizing strategies, which maximize control of the high-risk subpopulation. Results showed that of the three heuristic strategy types, the most cost-effective involved setting a target value for R e and applying sufficient controls to attain that target value. This heuristic led to strategies that begin with strict distancing of the entire population, later followed by increased testing. Strategies that maximize control on high-risk individuals were less cost-effective than basic strategies that emphasize reduction of the rate of spreading of the disease. The model shows that delaying the start of control measures past a certain point greatly worsens strategy outcomes. We conclude that the effective reproduction can be a valuable real-time indicator in determining cost-effective control strategies. [ABSTRACT FROM AUTHOR]

This paper contextualizes the most recent discovery of 95 forgotten graves of incarcerated laborers at a public school construction site within ongoing tensions around public history, race, and development in Sugar Land, Texas, a Houston-area bedroom community. Unearthed along with the graves is the state's long history of Black labor exploitation, from enslavement to convict leasing to employment with Imperial Sugar. In this article, I engage the haunting of Black laboring bodies in the landscape from the perspective of both that of a researcher and former resident of Fort Bend County confronted with the purposeful forgetting of Black geographies, bodies, and lives. I expose through critical analysis of government documents, online digital exhibits, maps, photos, and autoethnographic recollection of the area, the haunting of Black laboring bodies in not only the site of burial discovery but also two sites within the Sugar Land cultural landscape: Mayfield Park and the Imperial Sugar Refinery. I argue developers and government agencies perpetuate a mythic local history that, until the discovery of the 95, allowed them to disassociate itself from Sugar Land's history of Black labor exploitation before its incorporation as a city. The 95's haunting allows for an inventive awareness of the Black laboring bodies and thus redefines the cultural landscape rooted in plantation logics as a Black geography. I conclude with a discussion of the ways Black laboring bodies' haunting creates a space for a critical cultural landscape solution. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification has attracted wide attention in many fields. Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers, which has improved the HSI classification accuracy greatly. However, GNN-based methods have not been widely applied due to their time-consuming, inefficient information description as well as poor anti-noise robustness. To overcome the deficiencies, a novel multi-scale receptive fields graph attention neural network (MRGAT) is proposed for HSI classification in this paper. In this network, a superpixel segment method is adopted to abstract the original HSI local spatial features. A two-layer one-dimensional convolution neural network (1D CNN) spectral transformer mechanism, is designed to extract the spectral features of superpixels, with which the spectral features can be acquired automatically. Furthermore, graph edges are introduced into Graph Attention Network (GAT) to acquire the local semantic feature of the graph. Moreover, inspired by the transformer network, we design a novel multi-scale receptive field GAT to extract the local-global adjacent node-features and edges-features. Finally, a graph attention network and a softMax function are utilized for multi-receptive feature fusion and pixel-label predicting. On Pavia University, Salinas, and Houston 2013 datasets, the overall accuracies (OAs) of our MRGAT are 71.76%, 82.61%, and 63.82%, respectively. Moreover, the performances with limited labeled samples indicates that the MRGAT contains superior adaptability. Compared with the competitive classifiers, MRGAT achieves high classification efficiency verified by training time comparison experiment. [ABSTRACT FROM AUTHOR]

This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff conversion, and (c) a hydraulic model for simulating river inundation. This paper focuses on (1) demonstrating the application of the framework by applying it to the operation of a hypothetical eight-wetland system in the Cypress Creek watershed in Houston, Texas; and (2) comparing and discussing the performance of the two optimization methods under consideration. The results show that the GA and PS optimal solutions are very similar; however, the computational time required by PS is significantly shorter than that required by GA. The results also show that optimal dynamic water management can significantly mitigate flooding compared to the case without management. • We compare the performance of the GA and PS methods for forecasting optimal flow releases in a multi-storage system for flood control. • The results of the GA and PS methods are very similar, however the run time required by PS is significantly smaller than that required by GA. • Dynamic water management according to the optimization results can significantly mitigate flooding compared to the case without management. • A key factor for flood control is to partially empty the storage systems before the rainfall event and during the initial period of rainfall. [ABSTRACT FROM AUTHOR]

The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building's HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings. [ABSTRACT FROM AUTHOR]

To predict atmospheric conditions in an urban environment, the land surface processes must be accurately described through the use of detailed land use (LU) and land cover (LC) data. Use of the U.S. Geological Survey (USGS) 25-category data, currently in the Fifth-generation Mesoscale Model (MM5), with the Noah land surface model (LSM) and MRF (medium-range forecast) planetary boundary layer (PBL) schemes resulted in the over-prediction of daytime temperatures in the Houston downtown area due to the inaccurate representation as a completely impervious surface. This bias could be corrected with the addition of canopy water in the urban areas from the evapotranspiration effects of urban vegetation. A more fundamental approach would be to utilize an LULC dataset that represents land surface features accurately. The Texas Forest Service (TFS) LULC dataset established with the LANDSAT satellite imagery correctly represents the Houston–Galveston–Brazoria (HGB) area as mixtures of urban, residential, grass, and forest LULC types. This paper describes how the Noah LSM and PBL schemes in the MM5 were modified to accommodate the TFS-LULC data. Comparisons with various meteorological measurements show that the MM5 simulation made with the high resolution LULC data improves the boundary layer mixing conditions and local wind patterns in the Houston Ship Channel, which is a critically important anthropogenic emission area affecting the HGB air pollution problems. In particular, when the synoptic flows are weak, the improved LULC data simulates the asymmetrically elongated Houston heat island convergence zone influencing the location of the afternoon Gulf of Mexico sea-breeze front and the Galveston Bay breeze flows. This paper is part I of a two-part study and focuses on the meteorological simulation. In part II, effects of using the different meteorological inputs on air quality simulations are discussed. [Copyright &y& Elsevier]

The number and intensity of flood events have been on the rise in many regions of the world. In some parts of the U.S., for example, almost all residential properties, transportation networks, and major infrastructure (e.g., hospitals, airports, power stations) are at risk of failure caused by floods. The vulnerability to flooding, particularly in coastal areas and among marginalized populations is expected to increase as the climate continues to change, thus necessitating more effective flood management practices that consider various data modalities and innovative approaches to monitor and communicate flood risk. Research points to the importance of reliable information about the movement of floodwater as a critical decision-making parameter in flood evacuation and emergency response. Existing flood mapping systems, however, rely on sparsely installed flood gauges that lack sufficient spatial granularity for precise characterization of flood risk in populated urban areas. In this paper, we introduce a floodwater depth estimation methodology that augments flood gauge data with user-contributed photos of flooded streets to reliably estimate the depth of floodwater and provide ad-hoc, risk-informed route optimization. The performance of the developed technique is evaluated in Houston, Texas, that experienced urban floods during the 2017 Hurricane Harvey. A subset of 20 user-contributed flood photos in combination with gauge readings taken at the same time is used to create a flood inundation map of the experiment area. Results show that augmenting flood gauge data with crowdsourced photos of flooded streets leads to shorter travel time and distance while avoiding flood-inundated areas. [ABSTRACT FROM AUTHOR]

Introduction: From 1976 to present the ANSMET program has collected more than 23,000 meteorites, 43 teams have been to the ice searching for meteorites in various locations. After collection and packaging the meteorites are shipped to JSC on a freezer truck and remain frozen until they are processed. Due to the pandemic the team did not go to the ice in 2020 or 2021. The types and numbers of meteorites that have been classified include 975 carbonaceous chondrites, 146 enstatites, 513 achondrites, 68 stony, 127 irons, 30 R chondrites, 7205 H's, 15 L Chondrite Impact Melts, 9198 L's, and 3890 LL's. Although 80-85% of the collected meteorites fall in the ordinary chondrite group, the other ~15% represent rare types of achondrites and carbonaceous chondrites. These rare meteorites include 32 lunar meteorites, 15 Martian meteorites, scores of various types of carbonaceous chondrites, and unique achondrites. The classification is an initial classification, and some meteorites might get reclassified after more extensive research. Also due to the pandemic the lab was closed for most of 2020 and part of 2021, so there are still samples from the 2018 and 2019 field seasons that are unclassified that we are presently processing. JSC has recently acquired a X-CT which can now be used to scan some of the special/unique meteorites to see if anything of scientific value is visible before breaking. Some of these meteorites can be viewed on the Astromaterials website. The Antarctic meteorites are processed in a cleanroom at JSC in Houston, TX [1]. The meteorites are dried in a nitrogen glove box for 24 to 48 hours. The meteorites are then photographed, measured, weighed and exterior and interior features are described. The meteorite is chipped and a representative sample, either a 1-3 g chip or thin section is sent to the Smithsonian Institution for classification. After classification and approval by the Nomenclature Committee of the Meteoritical Society, they are announced in the Antarctic Meteorite Newsletter [2-3], which is published twice per year (fall and spring) so that scientists may review which meteorites are available to study. Requests for Antarctic Meteorite samples are welcomed from research scientists, regardless of their current state of funding for meteorite studies. To date close to 3800 requests have been submitted for meteorites and approximately 500 investigators worldwide are active in the study of meteorites from the collection. Research on these samples has been published in >1950 peer reviewed articles (through 2021); a listing of papers for any meteorite sample can be generated by accessing http://curator.jsc.nasa.gov/antmet/referencesearch.cfm. Antarctic meteorite samples requested by scientists can be prepared in several different ways. Most samples are prepared as chips, either using a rock splitter or using a chisel and chipping bowl. In special situations, a researcher may request a meteorite slab in which case the samples are cut using a diamond-bladed bandsaw inside of a dry nitrogen glove box. The meteorites are always cut in a 100% liquid-free environment. Additionally, thin/thick sections of Antarctic meteorites are also prepared at JSC. The meteorite thin section lab at JSC can prepare standard 30-micron thin sections, thick sections of variable thickness (100 to 200 microns), or demountable sections using superglue, all sections are prepared without using water. New meteorite discoveries by the ANSMET program provide a cost-effective method for obtaining samples of previously unsampled bodies, allowing scientists to learn more about the origin, composition, and evolution of the solar system (e.g., see detailed articles on specific meteorite types in [4]). [ABSTRACT FROM AUTHOR]

First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Yuki Ogawa is first author on ‘Endogenously expressed Ranbp2 is not at the axon initial segment’, published in JCS. Yuki is a postdoc in the lab of Matthew Rasband at the Baylor College of Medicine, Houston, USA, investigating how the cytoskeletal proteins are organized in neurons by using CRISPR/Cas9-based genome engineering. [ABSTRACT FROM AUTHOR]

Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions. • The study developed a method that integrates computer vision and flow direction model to assess surface drainage condition. • This study applied the developed method to 50 street blocks in a neighborhood in Houston, Texas. • The developed model can identify key drainage and land features within a street block with an accuracy of 88.5%. • The results can show problematic areas susceptible to water ponding and inform drainage maintenance decisions. [ABSTRACT FROM AUTHOR]

Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations: 1- a unique adaptation of the coupled residual networks to address multi-sensor data classification; 2- a smart auxiliary training via adjusting the loss function to address classifications with limited samples; and 3- a unique design of the residual blocks to reduce the computational complexity while preserving the discriminative characteristics of multi-sensor features. The proposed classification framework is evaluated using three different remote sensing datasets: the urban Houston university datasets (including Houston 2013 and the training portion of Houston 2018) and the rural Trento dataset. The proposed framework achieves high overall accuracies of 93.57%, 81.20%, and 98.81% on Houston 2013, the training portion of Houston 2018, and Trento datasets, respectively. Additionally, the experimental results demonstrate considerable improvements in classification accuracies compared with the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents a MATLAB framework for forecasting optimal flow releases in a multi-storage system for flood control. This framework combines four widely-used models intended for (1) performing hydrologic analysis for a watershed and for level-pool routing in the storage systems, (2) simulating river inundation, (3) solving the optimization problem of determining hourly optimal flow releases in a multi-storage system, and (4) data management and plotting. The integration of all software is performed in MATLAB, which is a state-of-the-art and an easy-to-use environment for integrating computation, visualization, and programming. This paper focuses on (1) presenting the MATLAB scripts for interfacing the aforementioned four software, (2) describing the rationale for the objective function of the optimization, and (3) demonstrating the practical use of the MATLAB framework by applying it to the operation of a hypothetical eight-pond system in the Cypress Creek watershed in Houston, Texas. The results of the aforementioned application show that this framework could help in mitigating flooding. • This paper presents MATLAB scripts for interfacing four models of hydrology, hydraulics, optimization and data management. • The MATLAB scripts are exemplified through the flood control operation of a hypothetical eight-pond system. [ABSTRACT FROM AUTHOR]

• The scheduling model for seaport integrated energy system is proposed accounting of the operating flexibility of berth allocation problems, reefer area and cold-ironing. • The energy models of BAP, reefer area and cold-ironing management are presented in the first time, which is the basic models for the demand response research of seaport integrated system. • Dynamic pricing mode is incorporated with the proposed scheme for seaport. • The effectiveness of the proposed scheme in seaport energy management and cost reduction is verified with real-world data. As a bridge to connect the land and sea, the emissions within a seaport have significant effects on the urban environment. In order to reduce gas emissions, electrification of seaports has been a promising trend. Besides the integrated thermal network for cold-chain supply, the future seaport can be viewed as a transportation integrated energy system, and the coordination between the shipside and portside will highly influence the system behaviors of seaport. In this paper, the energy models of two basic ship-port coordination, i.e., on-shore power supply management (cold-ironing) and berth allocation are proposed, and an integrated energy system scheduling model is proposed to simultaneously meet the electric and thermal power demands. The port of Houston is set as the test case to show the validity of proposed method and the simulation results demonstrate berth allocation can provide adequate flexibility to facilitate the seaport operation. [ABSTRACT FROM AUTHOR]

An extensive set of in situ and laboratory test data is presented for a footing load test site east of Houston, Texas, in desiccated Beaumont clay. The in situ test program included standard and cone penetration tests (CPTs), the latter of which was selected for statistical analysis to produce vertical and horizontal random field model parameters for corrected cone tip resistance. Given the relatively high sampling frequency of the cone tip resistance in the vertical direction, the vertical random field model parameters were determined using the modified Bartlett's test statistic with fitted autocorrelation models subject to a strict fitting criterion. Horizontal random field model parameters were generated by collapsing the two-dimensional distribution of the CPTs to a one-dimensional representation and by using less stringent evaluation of the autocorrelation. The results of this study indicate that Beaumont clay exhibits greater inherent spatial variability than previously reported at other clay sites, which is attributed to the secondary structure of the desiccated clay. A companion paper discusses the performance of a footing load test in the context of spatial and transformation uncertainty. [ABSTRACT FROM AUTHOR]

This paper examines the interplay between historic preservation, archaeology, mass media, and competing historical narratives, focusing on one urban community in Texas. After its founding by emancipated African Americans, Freedmen's Town became the "Mother Ward" for Houston's growing black professional class. By the late 20th century, due to demographic shifts, a lack of zoning, and weak historic preservation ordinances, it had become one of the poorest neighborhoods in the city (despite being listed on the National Register of Historic Places). The remaining historical structures are now being drowned in a gentrification-driven sea of townhomes and lofts. Some new residents embrace the neighborhood's history, but others use media and politics to reject it actively, as they enact traditional stereotypes about race, poverty, and class. Within this context, those of us involved with the Community Archaeology Research Institute, Inc., have been attempting to do community-based (not "placed") archaeology and encourage local-history and public-policy communities to avoid "writing oft" the neighborhood as "lost." [ABSTRACT FROM AUTHOR]

Abstract: A previous hypothesis advanced from observational studies such as METROMEX suggests that the intensity, frequency, and organization of cumulus convection may be impacted by the forcing of enhanced merger activity downstream of urban zones. A resulting corollary is that cities may exert an indirect anthropogenic “forcing” of parameters related to convection and associated phenomena such as lightning and precipitation. This paper investigates the urban-merger hypothesis by examining the role of convective cell mergers on the existence and persistence of the Houston lightning “anomaly”, a local maximum in cloud-to-ground (CG) lightning activity documented to exist over and east of Houston. Using eight summer seasons of peak columnar radar reflectivity, CG lightning data and a cell-tracking algorithm, a two-dimensional cell merger climatology is created for portions of Eastern Texas and Louisiana. Results from the tracking and analysis of over 3.8 million cells indicate that merger-driven enhancements in convection induce a positive response (O 46%) in ground flash densities throughout the domain, with areas of enhanced lightning typically being co-located with areas of enhanced merger activity. However, while mergers over the Houston area (relative to elsewhere in the domain) do result in more vigorous convective cells that produce larger CG flash densities, we find that CG lightning contributions due to mergers are distributed similarly throughout the domain. Hence while we demonstrate that cell mergers do greatly impact the production of lightning, the urban cell merger hypothesis does not uniquely explain the presence of a local lightning maximum near and downstream of Houston. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to investigate the effects of the growth of the Houston metropolitan area on the characteristics and intensity of convection and precipitation. For this purpose, we implemented the Town Energy Budget (TEB) urban model into Regional Atmospheric Modeling System (RAMS). The Landsat Thematic Mapper National Land Cover Data (NLCD) for the Houston area corresponding to the years 1992, 2001 and 2006 were used for an objective experimental design of land-use sensitivity experiments. We analyzed the impact on two distinct groups of convective cells triggered by the sea breeze circulation on August 24 2000 and compared the model simulated precipitation to RADAR data. The first group of storms occurred southwest of the city and was not influenced by the urban cloud condensation nuclei (CCN), while the second was and occurred north of the city (downwind) a few hours later. The effect of land-use on convection and precipitation was dramatic for the storms SW of the city and it was linked to a monotonic increase in the intensity of the sea breeze. The intensification of the sea-breeze circulation when using the 1992, 2001, and 2006 NLCD land use datasets generated a monotonic increase of the total precipitated volume of 9, 11, and 25%, respectively (over a run with no city). Due to increased exposure to aerosols, the upper levels of the convective cells downwind of the city were invigorated by a greater latent heat release linked to higher amounts of liquid water transported to supercooled levels. However, precipitation did not show a monotonic behavior when we varied the intensity of the urban aerosol sources. With the highest aerosol concentrations, riming growth of ice particles became so inefficient that larger amounts of condensate was transported upwards into the storm anvil, contributing to a reduction in precipitation efficiency. [Copyright &y& Elsevier]

In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class. [ABSTRACT FROM AUTHOR]

Abstract: Scientists and educators at Baylor College of Medicine are using space life sciences research areas as themes for middle school science and health instructional materials. This paper discusses study findings of the most recent unit, Food and Fitness, which teaches concepts related to energy and nutrition through guided inquiry. Results of a field test involving more than 750 students are reported. Use of the teaching materials resulted in significant knowledge gains by students as measured on a pre/post assessment administered by teachers. In addition, an analysis of the time spent by each teacher on each activity suggested that it is preferable to conduct all of the activities in the unit with students rather than allocating the same total amount of time on just a subset of the activities. [Copyright &y& Elsevier]

This paper considers group testing when the probability of response is increasing across the levels of an observed covariate. We illustrate how previously known results in order-restricted inference can be extended to situations wherein data are collected according to a group-testing protocol, and we derive maximum likelihood estimators for proportions under the increasing order restriction and group-testing model. Finally, we show how the use of group testing can dramatically reduce the bias and mean squared error of isotonic regression estimators obtained from one-at-a-time testing. These proposed methods are illustrated using data from an observational HIV study conducted in Houston, Texas. [ABSTRACT FROM AUTHOR]

The mission operations directorate (MOD) at the Johnson Space Center (JSC) is responsible for the planning and operation of human space flight missions. MOD is being challenged with sustaining and developing new operations capabilities to support increasingly demanding requirements and to improve its processes to accomplish these missions at higher levels of safety, mission success, and effectiveness. Automation is being considered as an enabling technology to meet the aforementioned challenges. The synergistic combination of flight controllers and intelligent software providing the function of ‘operations assistants’ (OA) is being pursued as the key implementation of this technology in the Mission Control Center (MCC).The flight control team (FCT) assesses the condition and operability of the major systems such as electrical power, thermal control, life support, communications, altitude control, and data handling at MCC. OA assist the flight controllers with their tasks of monitoring the status and health of the flight systems. They also help maintain the flight controller''s awareness of the operations being performed during the mission and help assure that operational objectives are being met. The station duty officer (SDO) performs the lead operations role for the International Space Station (ISS) during quiescent times when FCT and the flight director are off-duty. SDOs assess the condition and operability of the major ISS systems. This assessment involves monitoring and controlling several periodic processes on systems such as the electrical power, thermal control, life support, communications, altitude control, and data handling systems. The SDO is also responsible for coordinating operations with the Russian FCT. The OA will help the SDO maintain an awareness of all the processes performed on board and will assist with the responses to anomalous conditions. The OA for this position will support the concept of reduced control center staffing during quiescent times. The purpose of this paper is twofold: (1) to present a unique two-stage specification methodology that combines data flow diagrams and petri nets and (2) to apply the proposed methodology in a complex space station system. [Copyright &y& Elsevier]

This paper analyses folk constructions of syphilis in a predominately African-American community in Houston, Texas. In-depth interviews with 17 African-Americans were conducted to describe the folk models of syphilis present. Interviews centred on prevalence transmission, risk, personal protection and detection, relationships and expectations for the future. Physical appearance, the earwax test, the match test and the smell test were used to 'detect' syphilis infection. Respondents conflated the symptoms of several STDs including syphilis, gonorrhoea, herpes and chlamydia into a single class. The study findings indicate miscommunication regarding STD information and use of different models (folk vs. medical) to explain risk of infection and prevention. The use of folk constructions in syphilis interventions would provide a better understanding of the community's knowledge and perceptions of STDs and a better focus for future education and prevention programmes. [ABSTRACT FROM AUTHOR]

At Rice University, we have undertaken a project to construct a framework for generating high-level problem solving languages that can achieve high performance on a variety of platforms. The underlying strategy, called telescoping languages, builds problem-solving systems from domain-specific libraries and scripting languages by extensively preanalyzing libraries to produce a precompiler that optimizes library calls within the scripts as if they were primitives in the underlying language. Our study of applications written in Matlab by the signal processing group at Rice University has identified a collection of old and new optimizations that show promise for this particular domain. Two promising new optimizations are procedure vectorization and procedure strength reduction. By transforming these programs, at source level, according to the strategies described in this paper, we were able to achieve speedups ranging from a factor of 1.1 to 1.6 over the entire applications—with speedups for individual functions as high as 3.3. [ABSTRACT FROM AUTHOR]

In this paper we examine scientific evidence and related uncertainties in two steps of benefit — cost analyses of ozone reduction: estimating the health improvements attributable to reductions in ozone and determining the appropriate monetary values of these improvements. Although substantial evidence exists on molecular and physiologic impacts, the evidence needed to establish concentration — response functions is somewhat limited. Furthermore, because exposure to ozone depends on factors such as air conditioning use, past epidemiologic studies may not be directly applicable in unstudied settings. To evaluate the evidence likely to contribute significantly to benefits, we focus on four health outcomes: premature mortality, chronic asthma, respiratory hospital admissions, and minor restricted activity days. We determine concentration — response functions for these health outcomes for a hypothetical case study in Houston, Texas, using probabilistic weighting reflecting our judgment of the strength of the evidence and the possibility of confounding. We make a similar presentation for valuation, where uncertainty is due primarily to the lack of willingness-to-pay data for the population affected by ozone. We estimate that the annual monetary value of health benefits from reducing ozone concentrations in Houston is approximately $10 per person per microgram per cubic meter (24-hr average) reduced (95% confidence interval, $0.70-$40). The central estimate exceeds past estimates by approximately a factor of five, driven by the inclusion of mortality. We discuss the implications of our findings for future analyses and determine areas of research that might help reduce the uncertainties in benefit estimation. Key words: benefit — cost analysis, epidemiology, exposure assessment, monetary valuation, ozone, premature mortality. Environ Health Perspect 109:1215-1226 (2001). [Online 24 November 2001] http://ehpnet1.niehs.nih... [ABSTRACT FROM AUTHOR]

Research shows that religion continues to be an important identity marker for new immigrants in the United States. However, immigrant groups differ in the ways they integrate religious and ethnic identities and the emphasis they place on each. In this paper, we argue that majority or minority status of their religious affiliation in the home and host countries is an important, but overlooked, factor in understanding strategies concerning religious and ethnic identities. By comparing two Chinese congregations, a Chinese Buddhist temple and a Chinese Christian church in Houston, Texas, we analyze what happens when an immigrant group moves from majority status in the home country to minority status in the United States (Chinese Buddhists) and when a minority group (Chinese Christians in China) become part of the Christian majority in the United States. We conclude by arguing the importance of going beyond U.S. borders and taking into account factors in their home countries in attempts to understand patterns of adaptation of the new immigrants. [ABSTRACT FROM AUTHOR]

In this paper we show that immigrant religious institutions tend to assume many elements of a congregational structure and a community center model of functioning, characteristics usually not found in their countries of origin. Based on data from the Religion, Ethnicity, New Immigrants Research (RENIR) project in Houston, Texas, we found, however, that the two dimensions are distinct and largely unrelated to one another. While each serves as a vehicle to engender high levels of member commitment to the religious institution and serves to meet both religious and material needs of the immigrants, congregations vary in the degree to which they develop the two major elements of congregationalism. [ABSTRACT FROM AUTHOR]

In this paper, we present a three-stage algorithm for finding numerical solutions for optimal control problems. The algorithm first performs an exhaustive search through a discrete set of widely dispersed solutions which are representative of large subregions of the search space; then, it uses the search results to initialize a Monte Carlo process that searches quasi-randomly for a best solution; then, it finally uses a Newton-type iteration to converge to a solution that satisfies mathematical conditions of local optimality. We demonstrate our methodology on an epidemiological model of the coronavirus disease with testing and distancing controls applied over a period of 180 days to two different subpopulations (low-risk and high-risk), where model parameters are chosen to fit the city of Houston, Texas, USA. In order to enable the user to select his/her preferred trade-off between (number of deaths) and (herd immunity) outcomes, the objective function includes costs for deaths and non-immunity. Optimal strategies are estimated for a grid of (death cost) × (non-immunity cost) combinations, in order to obtain a Pareto curve that represents optimum trade-offs. The levels of the four controls for the different Pareto-optimal solutions over the 180-day period are visually represented and their characteristics discussed. Three different variants of the algorithm are run in order to determine the relative importance of the three stages in the optimization. Results from the three algorithm variants are fairly consistent, indicating that solutions are robust. Results also show that the Monte Carlo stage plays an especially prominent role in the optimization, but that all three stages of the process make significant contributions towards finding lower-cost, more effective control strategies. [ABSTRACT FROM AUTHOR]

While it is well known where the best entrepreneurial ecosystems can be found in general, such information is missing in the case of sustainability entrepreneurial ecosystems. These are entrepreneurial ecosystems with high shares of startups that are active in industries which address the Sustainable Development Goals through innovation. Not knowing what the best sustainability entrepreneurial ecosystems are, inhibits sustainability startups to locate to regions with the most favourable supportive conditions. This paper addresses the above-mentioned gap by establishing a ranking of entrepreneurial ecosystems according to their share of sustainability enterprises. For this purpose, we have analysed the websites of 19,997 startups in the 28 largest entrepreneurial ecosystems according to the Startup Genome project using Latent Dirichlet Allocation. We find that the highest proportion of sustainability startups among these entrepreneurial ecosystems is located in Boston, followed by Houston, Seattle and Lagos, respectively. A qualitative comparative analysis of the causal patterns underlying our results reveals that high GDP in combination with either (1) high shares of female founders of startups or (2) high shares of non-religious people in the population induce entrepreneurial ecosystems with relatively high levels of sustainability enterprises. Our work aims to stimulate further scholarly interest in sustainability-oriented entrepreneurial ecosystems. Policy makers can use the causal drivers to increase the share of sustainability startups in their region. [ABSTRACT FROM AUTHOR]

This paper reports the results of an investigation designed to change teacher-student interactions with mainstreamed students in middle school social studies classes. Fourteen of 40 teacher volunteers received a six-session intervention emphasizing key variables from the teacher effectiveness and special education literature. Results indicate significant differences between experimental and control teachers on a pre- and post-contrast as well as on a follow-up (maintenance) contrast. Homogeneous and heterogeneous grouping effects, independent of the intervention, also are seen. Implications of the findings are presented in terms of future training and research efforts. [ABSTRACT FROM AUTHOR]

Twitter has significant potential as a source of Volunteered Geographic Information (VGI), as its content is updated at high frequency, with high availability thanks to dedicated interfaces. However, the diversity of content types and the low average accuracy of geographic information attached to individual tweets remain obstacles in this context. The contributions in this paper relate to the general goal of extracting actionable information regarding the impact of natural hazards on a specific region from social platforms, such as Twitter. Specifically, our contributions describe the construction of a model classifying whether given spatio-temporal coordinates, materialized by raster cells in a remote sensing context, lie in a flooded area. For training, remotely sensed data are used as the target variable, and the input covariates are built on the sole basis of textual and spatial data extracted from a Twitter corpus. Our contributions enable the use of trained models for arbitrary new Twitter corpora collected for the same region, but at different times, allowing for the construction of a flooded area measurement proxy available at a higher temporal frequency. Experimental validation uses true data that were collected during Hurricane Harvey, which caused significant flooding in the Houston urban area between mid-August and mid-September 2017. Our experimental section compares several spatial information extraction methods, as well as various textual representation and aggregation techniques, which were applied to the collected Twitter data. The best configuration yields a F1 score of 0.425, boosted to 0.834 if restricted to the 10% most confident predictions. [ABSTRACT FROM AUTHOR]

Roadside channel systems are critical for the management of stormwater runoff and the protection of the structural integrity of roads; and thus, require systematic evaluation and maintenance. However, the evaluation of these systems remains ad hoc due to the lack of efficient inspection methods. This paper contributes to filling this gap by providing an automated process for the inspection and evaluation of roadside channel systems using data obtained from mobile lidar (Light Detection and Ranging) scanners. The Cloth Simulation Filtering algorithm was employed to split lidar point clouds into bare earth and object datasets, and then compute six key attributes of roadside channels based on the bare earth dataset. The six attributes are: channel depth, bottom width, side slope, longitudinal slope, and length and density of subsurface pipes and culverts. To test and demonstrate the new automated process, it was applied to six street sections in the City of Houston, Texas. The computed attributes were compared to the design and maintenance guidelines set by the City of Houston and Harris County. The evaluation results indicate that every channel examined in this study has its own condition issues and improvement needs. While no channel section in this study was in full compliance with the guidelines, no channel was utterly incompatible either. The results show that the developed automated process can effectively and efficiently evaluate roadside channels, providing an alternative to conventional manual inspection methods. Future work could use the output of the developed method to assess the risk of localized flooding and inform both the municipal government and property owners about effective mitigation measures. • A method for automating the evaluation of roadside drainage channels in urban settings has been developed and demonstrated. • The automated evaluation method consists of computing six key attributes of roadside channels from mobile lidar raw data. • The potential for evaluating roadside channels at shorter distances to inform targeted improvements is presented. [ABSTRACT FROM AUTHOR]

This paper presents an integrated framework that combines a community's physical vulnerability to access disruption to critical facilities and their tolerance for such access disruption to services in order to inform the targeted communities protection and build an equitable resilience enhancement plan. The first component of the proposed framework includes a percolation simulation model capable of integrating the road network disruption probability into the flood propagation and encapsulates the road network's access to critical facilities (i.e., healthcare facilities). The discovered spatial reach of an areas' physical vulnerability dependence is 9 miles. Besides, physical disruptions in road networks and loss of access to emergency services (such as healthcare) have varying impacts on different sub-populations. To consider this aspect, the second component of the proposed framework involves a disruption tolerance index (DTI) to examine communities' tolerance towards access disruption to healthcare facilities in the face of the flooding. The proposed framework recognizes the importance of both infrastructure and human perspective of the vulnerability assessment and is tested using empirical data from Harris County, Texas, in the case of road network disruptions due to fluvial flooding. Houston, the fourth-largest city in the United States, is within Harris County. Integrated spatial analysis result reveals different spatial clusters of vulnerability across the study region and provides important insights regarding the critical infrastructure protection prioritization and hazard mitigation planning. The spatial clusters also unveil the existence of a homogeneous spatial pattern where similar vulnerable areas stay together. The proposed framework could be adopted by other cities and different critical facilities to enable decision-makers, infrastructure managers, and city planners to better evaluate their community vulnerability. [ABSTRACT FROM AUTHOR]