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Found 27 papers, 5 papers with code
ELEV-VISION-SAM: Integrated Vision Language and Foundation Model for Automated Estimation of Building Lowest Floor Elevation
By evaluating various vision language models, integration methods, and text prompts, we identify the most suitable model for street view image analytics and LFE estimation tasks, thereby improving the availability of the current LFE estimation model based on image segmentation from 33% to 56% of properties.
Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human-infrastructure Systems Performance
To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience and their interplay based on features related to the coupled human-infrastructure systems performance.
Rethinking Urban Flood Risk Assessment By Adapting Health Domain Perspective
The proposed perspective focuses on three pillars for examining flood risk: (1) inherent susceptibility, (2) mitigation strategies, and (3) external stressors.
Beyond Quantities: Machine Learning-based Characterization of Inequality in Infrastructure Quality Provision in Cities
While a growing of body of literature has recognized the importance of characterizing infrastructure inequality in cities and provided quantified metrics to inform urban development plans, the majority of the existing approaches focus primarily on measuring the quantity of infrastructure, assuming that more infrastructure is better.
Chasing Fairness in Graphs: A GNN Architecture Perspective
To this end, we aim to achieve fairness via a new GNN architecture.
Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features
The interpretability of the model outcomes enables feature analysis for specifying the determinants of resilience in areas within each resilience level, allowing for the identification of specific resilience enhancement strategies.
Unraveling Fundamental Properties of Power System Resilience Curves using Unsupervised Machine Learning
Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate.
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning
The insights gleaned from this study offer fresh perspectives on the relationship among urban features and their interplay with environmental hazard exposure disparities, informing the development of more integrated urban design policies to enhance social equity and environmental injustice issues.
Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas
Urban flood risk emerges from complex and nonlinear interactions among multiple features related to flood hazard, flood exposure, and social and physical vulnerabilities, along with the complex spatial flood dependence relationships.
MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features
Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events.
FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation
Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management.
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features
We further explore urban development and design strategies that could mitigate cancer prevalence, focusing on green space, developed areas, and total emissions.
ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images
The depth from the camera to the door bottom was obtained from the depthmap paired with the Google Street View image.
Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity
Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies.
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes
To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories.
Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
In this work, a novel transformer-based network is proposed for assessing building damage.
FMP: Toward Fair Graph Message Passing against Topology Bias
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i. e., message passing) behind GNNs inducing unfairness issue remains unknown.
A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features
We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting.
Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities
In this study, we propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas and their interactions captured by human mobility network to obtain vector representations of these areas.
Generalized Demographic Parity for Group Fairness
We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing.
Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models.
Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding
Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 Hurricane Harvey in Harris County (Texas, USA).
Revealing Critical Characteristics of Mobility Patterns in New York City during the Onset of COVID-19 Pandemic
Using data sources related to population density, aggregated population mobility, public rail transit use, vehicle use, hotspot and non-hotspot movement patterns, and human activity agglomeration, we analyzed the inter-borough and intra-borough moment for New York City by aggregating the data at the borough level.
Physics and Society Social and Information Networks
Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management
People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management.
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises
The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes.
DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data.
A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data
The objective of this study is to create and test a hybrid deep learning model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural Network-Fully Convolutional Network), for urban flood prediction and situation awareness using channel network sensors data.
