I am a computer science Ph.D. student at the University of Minnesota (UMN), working with Professor Yao-Yi Chiang. My research interest is on the geospatial data analysis with computer vision and natural language processing techniques. I have worked on text detection of historical map labels, connecting separated text labels, linking recognized place names to existing knowledge bases (entity linking) and label type inference (entity typing). Previously at the University of Southern California (USC), I also worked on object detection projects in the Information Sciences Institute (ISI) and Spatial Sciences Institute (SSI).
Dec 2019 - Aug 2020, Los Angeles
Aug 2018 - Dec 2019, Los Angeles
May 2019 - Aug 2019, Beijing, China
The paper proposes a novel spatial language model called SpaBERT that captures the spatial context of named geographic entities (geo-entities) in geospatial data. The model is based on the hypothesis that the characteristics of a geo-entity can be inferred by its surrounding entities, similar to word meanings in linguistic context.
The paper proposes a novel model for detecting arbitrarily-oriented objects, such as texts/hands or objects in aerial images. The model evolves the axis-aligned bounding box to an oriented quadrilateral box using contour information.
The paper proposes an unified method, called ChartOCR, to extract data from various types of chart images, including bar charts, line charts, and pie charts. We combine deep learning and rule-based methods to achieve generalization ability and obtain accurate and semantic-rich intermediate results.
We present an end-to-end approach that automatically processes historical map images to extract their text content and generate a set of metadata linked to large geographic databases. The approach combines OCR with geocoding to accurately identify location phrases and assign geospatial coordinates.
Many text detection algorithms have been proposed to locate text regions in map images automatically, but most of the algorithms are trained on out-of-domain datasets. This paper introduces a method to automatically generate an unlimited amount of annotated historical map images for training text detection models.
The paper proposes a framework that uses remote sensing data (the Global Human Settlement Layer, GHSL) and georeferenced historical maps to generate historical urban extents for the early 20th century.
The paper proposes a template-based learning approach for computer vision tasks, where multiple instances of a concept are available. The method dynamically predicts weights that consider noise and redundancy to aggregate image-level features into a single template-level representation.
A deep learning tool to process scanned historical maps. Performs text detection & recoginition, postOCR correction and entity linking.
SpaBERT extends BERT to capture linearized spatial context, while incorporating a spatial coordinate embedding mechanism to preserve spatial relations of entities in the 2-dimensional space.
Utilized cycle-GAN to convert open street map (OSM) images into historical map style.
Mishmash: A Mix of Old and New won the Ordnance Survey Award 2022. I adopted cycleGAN to generate synthetic historical maps from Open Street Map (OSM) vector data.
The Map Feature Extraction Challenge required us to identify and label map features–lines, polygons, and points– that appear in the legend of historical maps. Our team isi-umn won the first place.
