Search Results for author:
Found 36 papers, 14 papers with code
GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learning
The region2vec methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering.
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
Using our benchmark, we evaluate five open-weight and proprietary LLMs, each with three frameworks: direct prompting, OpenHands CodeAct, and self-debug.
Fine-Tuning is Fine, if Calibrated
For example, fine-tuning a pre-trained classifier capable of recognizing a large number of classes to master a subset of classes at hand is shown to drastically degrade the model's accuracy in the other classes it had previously learned.
Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories
This research focuses on assessing the ability of AI foundation models in representing the trajectories of movements.
Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts.
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
In this work, we exploit the structured context linked to camera trap images to boost out-of-distribution generalization for species classification tasks in camera traps.
Ranked #1 on
Image Classification
on iWildCam2020-WILDS
(using extra training data)
Artificial Intelligence and Human Geography
This paper examines the recent advances and applications of AI in human geography especially the use of machine (deep) learning, including place representation and modeling, spatial analysis and predictive mapping, and urban planning and design.
Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics
The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning.
DTA: Distribution Transform-based Attack for Query-Limited Scenario
In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack.
Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond
In this work, we present a different view on trajectory similarity by introducing a measure that utilizes logical entailment.
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows
This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-Enhanced Graph Neural Network, designed to overcome these challenges and enhance the analysis of geospatial resilience of multicommodity food flow network, which is one type of spatial networks.
Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models
Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.
CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches
The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users.
Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations
This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations.
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
In this paper, a generalized packet drop model is proposed to describe the packet loss phenomenon caused by DoS attacks and other factors.
A Model Fusion Distributed Kalman Filter For Non-Gaussian Observation Noise
The convergence of MFDKF and its derivative algorithms is analyzed.
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
The widely used practice is to build task-specific or even dataset-specific solutions, which are hard to generalize and disable the opportunities of knowledge sharing that can be learned from different datasets and multiple tasks.
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI.
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise
Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Gaussian impulsive noise.
GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research
Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption.
Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions
Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components.
Measuring Network Resilience via Geospatial Knowledge Graph: a Case Study of the US Multi-Commodity Flow Network
Quantifying the resilience in the food system is important for food security issues.
STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity
In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering.
DeepSSN: a deep convolutional neural network to assess spatial scene similarity
Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries.
A Review of Location Encoding for GeoAI: Methods and Applications
A common need for artificial intelligence models in the broader geoscience is to represent and encode various types of spatial data, such as points (e. g., points of interest), polylines (e. g., trajectories), polygons (e. g., administrative regions), graphs (e. g., transportation networks), or rasters (e. g., remote sensing images), in a hidden embedding space so that they can be readily incorporated into deep learning models.
Detecting Adversarial Examples by Additional Evidence from Noise Domain
In this paper, we utilize the steganalysis rich model (SRM) to generate noise feature maps, and combine them with RGB images to discover the difference between adversarial examples and clean examples.
Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic
Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic.
Social and Information Networks Physics and Society
LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection
The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues.
Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic
The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic.
Social and Information Networks Physics and Society Populations and Evolution 65D10 H.4; G.3; J.2
Mapping county-level mobility pattern changes in the United States in response to COVID-19
To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing.
Physics and Society Social and Information Networks Populations and Evolution H.4; H.5
Calibrating the dynamic Huff model for business analysis using location big data
In this research, we present a time-aware dynamic Huff model (T-Huff) for location-based market share analysis and calibrate this model using large-scale store visit patterns based on mobile phone location data across ten most populated U. S. cities.
Social and Information Networks H.1
Shape Deformation and Drag Variation of a Coupled Rigid-flexible System in a Flowing Soap Film
We find the shape of the afterbody is determined by the filament length and flow velocity.
Fluid Dynamics
Extracting human emotions at different places based on facial expressions and spatial clustering analysis
In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed.
Transferring Multiscale Map Styles Using Generative Adversarial Networks
The advancement of the Artificial Intelligence (AI) technologies makes it possible to learn stylistic design criteria from existing maps or other visual art and transfer these styles to make new digital maps.
A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks
Given different types of constraints on human life, people must make decisions that satisfy social activity needs.
Scene Text Recognition Using Part-Based Tree-Structured Character Detection
Scene text recognition has inspired great interests from the computer vision community in recent years.
