The 2nd International Workshop on AI for Geographic Knowledge Discovery GeoAI’18

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活动简介

Based on academia, industry Successes, challenges, and opportunities in the field of artificial intelligence for data mining approaches grounding scientific findings. Whether it's the latest techniques in computer vision for satellite image analysis, scalable workflows, limitations of traditional learning methods, new geo-computational and related geo -spatial research, we invite you to join us at GeoAI2018.

We are inviting paper submission for the following categories:

Vision and position papers: 2 pages
On-going academic and industry papers: 4 pages
Research papers and production ready papers: 8 -10 pages

The workshop will be interactive to engage in discussions, shape the research directions, and disseminate state-of-the-art solutions.

组委会

Yingjie Hu
Assistant Professor of the Department of Geography at the University of Tennessee, Knoxville, USA

Song Gao
Assistant Professor in GIScience, at the University of Wisconsin, Madison, USA

Shawn Newsam
Associate Professor of Electrical Engineering & Computer Science and a Founding Faculty member at the University of California, Merced, USA

Dalton Lunga
A Geospatial image analyis and machine learning scientist at Oak Ridge National Laboratory, USA

Budhendra Bhaduri
A corporate Research Fellow and leader of the Geographic Information Science and Technology group at Oak Ridge National Laboratory, USA

Program Committee

Grant McKenzie, University of Maryland, College Park
Bandana Kar, Oak Ridge National Lab
Jonathan Gerrand, Council for Scientific and Industrial Research, South Africa
Huina Mao, Oak Ridge National Lab
Gautum Thakur, Oak Ridge National Laboratory
Xiaojiang Li, MIT Senseable City Lab
Krzysztof Janowicz, University of California-Santa Barbara
Wenwen Li, Arizona State University
Yao-Yi Chiang, University of Southern California
Raffay Hamid, DigitalGlobe
William Wang, University of California-Santa Barbara
Benjamin Adams, University of Canterbury
Bruno Martins, University of Lisbon
Hsiuhan (Lexie) Yang, Oak Ridge National Laboratory
Dengfeng Chai, Zhejiang University, China
Kuldeep Kurte, Oak Ridge National Laboratory

征稿信息

重要日期

2018-08-24

初稿截稿日期

2018-09-24

初稿录用日期

2018-10-05

终稿截稿日期

Topics include but not limited to:

GIScience with artificial intelligence for earth sciences and sustainability;
Artificial intelligence for public health and agricultural applications;
Novel deep neural network architectures and algorithms for geographic information analysis;
Artificial intelligence methods for object extraction (such as roads and buildings) from remote sensing images;
Deep learning for geographic information extraction from text (eg social media, web documents, and news);
Urban growth prediction and planning with machine learning methods;
Artificial intelligence methods for autonomous transportation and high-precision maps;
Unsupervised learning methods for large geographical scientific discoveries;
Deep learning for disaster response and humanitarian applications;
Human in the loop methods for enhancing deep learning applications;
Distributed computing methods for large scale geocomputing;
Novel training methods for large scale machine learning with geographical data;
Fusion of geographic attributed datasets to improve model estimation.

作者指南

Paper Format And Submission Guidelines

Short research articles or industry demonstrations of existing or developing scalable methods, toolkits, and best practices for AI applications in the geospatial domain are also invited . Vision or position papers noting future directions or an overview of grand challenges for AI technology in geospatial applications are also Welcome. All submitted papers will be peer reviewed to ensure the quality, clarity and relevance of the solicited work.

Manuscripts should be formatted using the ACM camera-ready templates available at http://www.acm.org/publications/proceedings-template .

Accepted papers will be considered for "Best Paper Award."