Search Results for author: Yao-Yi Chiang

Found 15 papers, 3 papers with code

GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding

1 code implementation23 Oct 2023 Zekun Li, Wenxuan Zhou, Yao-Yi Chiang, Muhao Chen

This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language.

Contrastive Learning Entity Typing +4

The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps

no code implementations29 Jun 2023 Jina Kim, Zekun Li, Yijun Lin, Min Namgung, Leeje Jang, Yao-Yi Chiang

mapKurator empowers automated extraction, post-processing, and linkage of text labels from large numbers of large-dimension historical map scans.

Zero-Shot Learning

Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

no code implementations28 Jun 2023 Arash Hajisafi, Haowen Lin, Sina Shaham, Haoji Hu, Maria Despoina Siampou, Yao-Yi Chiang, Cyrus Shahabi

Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies.

Decision Making Management +2

Quantile Extreme Gradient Boosting for Uncertainty Quantification

no code implementations23 Apr 2023 Xiaozhe Yin, Masoud Fallah-Shorshani, Rob McConnell, Scott Fruin, Yao-Yi Chiang, Meredith Franklin

For both the simulated and traffic noise datasets, the overall performance of the prediction intervals from QXGBoost were better than other models based on coverage width-based criterion.

Decision Making Prediction Intervals +2

Clustering Human Mobility with Multiple Spaces

no code implementations20 Jan 2023 Haoji Hu, Haowen Lin, Yao-Yi Chiang

Human mobility clustering is an important problem for understanding human mobility behaviors (e. g., work and school commutes).

Clustering Trajectory Clustering

SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation

no code implementations21 Oct 2022 Zekun Li, Jina Kim, Yao-Yi Chiang, Muhao Chen

Characterizing geo-entities is integral to various application domains, such as geo-intelligence and map comprehension, while a key challenge is to capture the spatial-varying context of an entity.

Entity Linking Entity Typing +2

Towards the automated large-scale reconstruction of past road networks from historical maps

1 code implementation10 Feb 2022 Johannes H. Uhl, Stefan Leyk, Yao-Yi Chiang, Craig A. Knoblock

Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization.

Decision Making

Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection

no code implementations12 Dec 2021 Zekun Li, Runyu Guan, Qianmu Yu, Yao-Yi Chiang, Craig A. Knoblock

We show that the state-of-the-art text detection models (e. g., PSENet) can benefit from the synthetic historical maps and achieve significant improvement for historical map text detection.

Style Transfer Text Detection

Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images

no code implementations10 Dec 2021 Weiwei Duan, Yao-Yi Chiang, Stefan Leyk, Johannes H. Uhl, Craig A. Knoblock

Recent semi-supervised clustering approaches can reduce manual labeling but still require annotations for all object categories in the image.

object-detection Object Detection

Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction

no code implementations10 Dec 2021 Yijun Lin, Yao-Yi Chiang, Meredith Franklin, Sandrah P. Eckel, José Luis Ambite

In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations.

feature selection Time Series +1

A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition

no code implementations10 Dec 2021 Weiwei Duan, Yao-Yi Chiang, Stefan Leyk, Johannes H. Uhl, Craig A. Knoblock

Thousands of scanned historical topographic maps contain valuable information covering long periods of time, such as how the hydrography of a region has changed over time.

Object Recognition

A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data

no code implementations14 Oct 2021 Yijun Lin, Yao-Yi Chiang

Large network logs, recording multivariate time series generated from heterogeneous devices and sensors in a network, can often reveal important information about abnormal activities, such as network intrusions and device malfunctions.

Anomaly Detection Event Detection +2

Kartta Labs: Collaborative Time Travel

no code implementations7 Oct 2020 Sasan Tavakkol, Feng Han, Brandon Mayer, Mark Phillips, Cyrus Shahabi, Yao-Yi Chiang, Raimondas Kiveris

We introduce the modular and scalable design of Kartta Labs, an open source, open data, and scalable system for virtually reconstructing cities from historical maps and photos.

DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis

no code implementations3 Mar 2020 Mingxuan Yue, Yaguang Li, Haoze Yang, Ritesh Ahuja, Yao-Yi Chiang, Cyrus Shahabi

Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence.

Clustering Marketing +1

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