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Found 27 papers, 8 papers with code
Learn to Recover Visible Color for Video Surveillance in a Day
In silicon sensors, the interference between visible and near-infrared (NIR) signals is a crucial problem.
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation.
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.
Ranked #1 on
Traffic Prediction
on Beijing Traffic
Hybrid Feature Embedding For Automatic Building Outline Extraction
Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment.
Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets
The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency.
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling
In recent years, single-frame image super-resolution (SR) has become more realistic by considering the zooming effect and using real-world short- and long-focus image pairs.
Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared.
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points.
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.
Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization
We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies.
Event-Aware Multimodal Mobility Nowcasting
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.
Differentiable Projection for Constrained Deep Learning
In this paper, we propose to use a differentiable projection layer in DNN instead of directly solving time-consuming KKT conditions.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.
An open GPS trajectory dataset and benchmark for travel mode detection
In this study, we propose and open GPS trajectory dataset marked with travel mode and benchmark for the travel mode detection.
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
Adapting Vehicle Detector to Target Domain by Adversarial Prediction Alignment
While recent advancement of domain adaptation techniques is significant, most of methods only align a feature extractor and do not adapt a classifier to target domain, which would be a cause of performance degradation.
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic.
Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks
Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment.
Deep-learning coupled with novel classification method to classify the urban environment of the developing world
Image encompassing 70% of the urban space was used for training and the remaining 30% was used for testing and validation.
VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction
In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events
Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.
Semantic Segmentation for Urban Planning Maps based on U-Net
The results indicate the proposed method can serve as a viable tool for urban planning map semantic segmentation task with high accuracy and efficiency.
Visual Graph Mining
In this study, we formulate the concept of "mining maximal-size frequent subgraphs" in the challenging domain of visual data (images and videos).
When 3D Reconstruction Meets Ubiquitous RGB-D Images
3D reconstruction from a single image is a classical problem in computer vision.
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns
Graph matching and graph mining are two typical areas in artificial intelligence.
Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models
We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes.
