Never in history have we known so much about our planet. Never in history have we had such access to Earth Science data. Never in history, has our society been so much at risk.

Environmental Hazards pose a constant threat to the development and sustainment of our civilization. A single catastrophic event can claim thousands of lives, cause damages for billions of dollars, trigger an economic depression that might affect, directly or indirectly, the entire world, destroy natural landmarks, cause tsunamis, floods, landslides, render a large territory uninhabitable and destabilize the military and political balance in a region. Such potential catastrophic consequences are due to the emergence of megacities, the proliferation of nuclear power plants and nuclear waste storage facilities, high dams, and other facilities whose destruction poses an unacceptable risk of global reach. Thus the study of environmental hazards and the processes that govern their occurrence has become a fundamental challenge for the survival of our civilization.

In recent years, advances in our ability to observe the Earth and its environment through the use of air, space and ground based sensors has led to the generation of large dynamic, and geographically distributed spatiotemporal data. The rate at which geospatial data are being generated exceeds our ability to organize and analyze them to extract patterns critical for an understanding our dynamically changing world. New challenges arise from our unprecedented access to massive amounts of Earth science data that can be used to study the complementary nature of different parameters. These developments are quickly leading towards a data-rich but knowledge-poor environment.

Data Mining algorithms are needed to address these scientific and computational challenges and provide innovative and effective solutions to analyze these large, often multi-modal, spatiotemporal datasets. Traditional data mining techniques are ineffective as they do not incorporate the idiosyncrasies of the spatial domain, which include spatial autocorrelation, spatial context, and spatial constraints.

The scope of this workshop is to provide a forum for the exchange of ideas and the establishment of synergistic activities among scientists working in fields such as geographic information science (GIS), data mining, machine learning, natural hazards, geoinformatics, remote sensing, as well as earth and atmospheric sciences. During this one-day event we aim to bring together these scientific communities, which are overlapping but not always interacting.



List of Topics

Geoinformatics

Environmental Hazards

Data Mining and Machine Learning

Social Media Analysis

Atmospheric Data Analysis

Remote Sensing

Hyperspectral Image Analysis

Geo-spatial Analysis

Big Data Analytics

Geographic Information Systems

Natural Hazards

Risk Assessment

Climate Change

Numerical Simulations