Workshop AI for Data Science 2023, PoF IV Subtopic 2.4

The Workshop is part of a yearly series within the AI/ML-Cluster in Subtopic 2.4 of the research program PoF IV: Changing Earth - Sustaining our Future

AI for Earth System Science - Ocean & Cryosphere in Climate, Workshop

The Workshop within the Research Program "Changing Earth - Sustain our Future" focuses on Artificial Intelligence topics and projects within the Data Science sub-cluster in Subtopic 2.4. Common directions and posible collaborations are discussed in four thematic sessions: AI Classification, Explainable AI, AI for Parameterization, and AI Enablement

A visual analytics tool to validate simulation models against collected data. V. 1.0.0

The validation of a simulation model is a crucial task in model development. It involves the comparison of simulation data to observation data and the identification of suitable model parameters. SLIVISU is a Visual Analytics framework that enables geoscientists to perform these tasks for observation data that is sparse and uncertain. Primarily, SLIVISU was designed to evaluate sea level indicators, which are geological or archaeological samples supporting the reconstruction of former sea level over the last ten thousands of years and are compiled in a postgreSQL database system. At the same time, the software aims at supporting the validation of numerical sea-level reconstructions against this data by means of visual analytics

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

The analysis of natural disasters in a timely manner often suffers from limited sensor data. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called “Volunteered Geographic Information (VGI)”. To save the analyst from manual inspection of all images posted online, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 results from 55% to 87% after 5 rounds of feedback

The Pilot Lab Exascale Earth System Modelling

The Pilot Lab Exascale Earth System Modelling (PL-ExaESM) is a “Helmholtz-Inkubator Information & Data Science” project and explores specific concepts to enable exascale readiness of Earth System models and associated work flows in Earth System science. PL-ExaESM provides a new platform for scientists of the Helmholtz Association to develop scientific and technological concepts for future generation Earth System models and data analysis systems

Integrating Data Science and Earth Science

This open access book presents the results of three years collaboration between earth scientists and data scientists, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows