Full text geoparsing as a Python library

Full text geoparsing/toponym resolution with event geolocation

Geographic Place, Date/time, and Pattern entity extraction toolkit along with text extraction from unstructured data and GIS outputters.

Full resources supporting the publication "A Pragmatic Guide to Geoparsing Evaluation."

Resources for the ACL 2018 publication "Which Melbourne? Augmenting Geocoding with Maps", published in July 2018.

The accompanying code and data for the Springer 2017 publication "What's missing in geographical parsing?" in Language Resources and Evaluation.

IDRISI is the largest-scale publicly-available Twitter Location Mention Prediction (LMP) datasets, in both English and Arabic languages. It contains 41 disaster events of different types (e.g., floods, fires). Annotations include tagged LMs in posts, location types (e.g., cities, streets), links to OSM toponyms, & usefulness of features for LMD.

Improve Earth Data Discovery through Deep Query Understanding (ESIP Incubator project)

Dockerized version of geoparser service mordecai3 with API

This repository contains descriptions about how to reproduce our geoparsing evaluation study. More information can found in our paper "Geoparsing: Solved or Biased? An Evaluation of Geographic Biases in Geoparsing" accepted by AGILE 2022.

Repository for the development of a library with features of: Geoparsing and Topic Modeling

Evaluating State-of-the-Art Deep Learning Geoparsers

Wildfire Heat Map Generation with Twitter and BERT

Basic, Integrated, and Reliably Distributed/Dockerized Coding, Actors, and Geolocation for Events

MARGE: Model Artificially Resolving Geographic Entities

Geoparse articles then geocode locations and visualize them