Search Results for author:
Found 15 papers, 3 papers with code
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language.
The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps
mapKurator empowers automated extraction, post-processing, and linkage of text labels from large numbers of large-dimension historical map scans.
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
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.
Quantile Extreme Gradient Boosting for Uncertainty Quantification
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.
Clustering Human Mobility with Multiple Spaces
Human mobility clustering is an important problem for understanding human mobility behaviors (e. g., work and school commutes).
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation
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.
Towards the automated large-scale reconstruction of past road networks from historical maps
Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization.
Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection
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.
Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images
Recent semi-supervised clustering approaches can reduce manual labeling but still require annotations for all object categories in the image.
Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction
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.
A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition
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.
An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images
This paper presents an end-to-end approach to address the real-world problem of finding and indexing historical map images.
Optical Character Recognition
Optical Character Recognition (OCR)
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data
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.
Kartta Labs: Collaborative Time Travel
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
Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence.
