Symposium of the International Future Lab AI4EO 2023
After our successful AI4EO Symposium 2022 we invite you to the Symposium of the International Future Lab AI4EO 2023 October 9th-10th at the TUM Campus in Ottobrunn. Stay tuned to get information about how to sign up for two days full of Research highlights, Poster Sessions, Scientific talks and panel discussions … and of course social events.
Date:
Monday, 9 Oct 2023 to 10 Oct 2023 (All day)
Location:
onsite, Campus Ottobrunn, Lise-Meitner-Str.9, 85521 Ottobrunn
What to expect:
Organized by the International Future Lab AI4EO and sponsored by the BMBF, the AI4EO Symposium 2023 aims to promote Artificial Intelligence for Earth Observation by addressing cutting-edge AI4EO methods and societally relevant applications.
This symposium will feature keynote talk, as well as oral and poster sessions and ample opportunities for networking and discussion. This symposium will offer an excellent opportunity for participants to share their latest research, collaborate, and learn about recent developments in Data Science in Earth Observation.
Topics (not limited to):
· Reasoning
· Uncertainty quantification
· Ethics in AI4EO
· Green AI
· Generalizability and Transferrability
· Physics-Aware Machine Learning
· Societal Relevant Applications of AI4EO (e.g. urbanization, UN’s SDG, climate science, food security etc)
Important Dates:
Registration deadline: September 30, 2023
Have a sneak peek into our program here:
Abstract submission deadline: September 15, 2023
Acceptance decision: September 30, 2023
P R O G R A M O C T 9 T H
09:00 Welcome and Introduction to the Future Lab AI4EO – Prof. Xiaoxiang Zhu
09:30 Oral session I: AI4EO – Reasoning, Uncertainty and Ethics – Chair: Prof. Richard Bamler
4 presentations 12+3 min each
- Prof. Dr. Jonathan Bamber (TUM AI4EO Future Lab/Uni. Bristol), A statistical framework for spatio-temporal inference: application to global sea level trends
- Konrad Heidler (TUM), Let’s think it through: Reasoning Strategies in AI for Earth Observation
- Prof. Dr. Yuanyuan Wang (TUM AI4EO Future Lab), How certain is uncertainty?
- Prof. Dr. Muhammad Shahzad (TUM AI4EO Future Lab), Towards a Benchmark EO Semantic Segmentation Dataset for Uncertainty Quantification
10.30 coffee break
11:00 Oral session II: AI4EO for Environment –Chair: Prof. Xiaoxiang Zhu
5 presentations 12+3 min each
- Dr. Ronny Hänsch (DLR), Towards Cross-Modal Classification of Remote Sensing Imagery
- Dr. Cornelius Senf (TUM), Mapping forest change across Europe with AI
- Prof. Dr. Jian Peng (UFZ), Unleashing Machine Learning and Remote Sensing for Land-Atmosphere Interaction
- Prof. Dr. Pedram Ghamisi (HZDR/IARAI), Managing Limited Labels: Exploring Point-level Labels and Text-to-Image Synthesis
- Prof. Dr. Guy Schumann (Uni. Bristol), Innovative EO-based flood mapping: the intersection between cloud computing, trustworthy AI and high technology readiness level
12:15 Poster Session I – lightening presentations
12.45 lunch (Group photo after lunch)
14:30 Keynote talk I: Prof. Veronika Eyring, DLR/TUM
Title: Machine learning for improved understanding and projections of climate change
15.30 coffee break/poster viewing
16:00 Oral Session III: AI4EO for Social Good – Chair: Prof. Jonathan Bamber
5 presentations 12+3 min each
- Dr. Martin Waehlisch (UN), Earth Observation for Sustainable Development and Peacebuilding
- Dr. Fahad Shah (Nankai University), Exploring land use land cover changes and their effect on urban heat island and land surface temperature: The case of Lahore, Pakistan
- Yannis Rupp (TU Darmstadt), Towards equation-based modeling of population dynamics in two South Asian Cities
- Getachew Workineh Gella (Paris Lodron Uni), Dwellings as anomalies: integrating VAEs for unsupervised object counting in humanitarian emergency response
- Angela Abascal (University of Twente), EO Modelling Environmental Deprivation and Climate Change Impacts in Urban Slums
19:00 Social Event & Dinner
P R O G R A M O C T 1 0 T H
09:00 Welcome
09:15 Keynote talk II: Prof. Dr. Hannes Taubenböck, DLR/Uni Würzburg
Title: Understanding urbanization using EO and AI: Capabilities and Challenges
10:15 coffee break
10:40 Oral session IV: AI4EO – Ethics and Beyond – Chair: Prof. Yuanyuan Wang
5 presentations 12+3 min each
- Prof. Dr. Mrinalini Kochupillai (TUM AI4EO Future Lab), Ethics in AI4EO
- Dr. Jiaojiao Tian (DLR), Domain adaptation-from synthetic to real
- Prof. Dr. Anna Kruspe (TH Nürnberg/TUM), Obtaining geoinformation from social media
- Prof. Dr. Martin Werner (TUM), Efficient AI for Earth Observation – Towards onboard processing and downlink optimization
- Dr. Perich Gregor (ETH), EOdal – Open Source Earth Observation for Everyone
12:00 Poster Session II – lightening presentations
12:30 lunch & poster
14:30 Keynote talk III: Dr. Bertrand Le Saux, ESA
Title: Next Generation AI4EO
15:30 Oral Session V: Physics- and Uncertainty-aware AI4EO – Chair: Prof. Muhammad Shahzad
5 presentations 12+3 min each
- Dr. Piyush Garg (Argonne National Laboratory), Physics-Informed Domain-Aware Convolutional Neural Network Based Atmospheric Radiative Transfer Emulator
- Qingsong Xu (TUM), Physics-aware Machine Learning: A New Paradigm for Machine Learning and Process-based Hydrology
- Dr. Alen Turnwald (E:FS TechHub GmbH), Advanced methods for uncertainty quantification and causality in AI applications
- Björn Tings (DLR), Uncertainty Estimation for Detectability Modelling without Ground Truth
- Ivica Obadic (TUM), MEDSAT: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
16:45 Wrap- up and goodbyes
Detailed list of posters:
Poster Session I: AI4EO applications
Detecting War Destruction in Ukraine using Sentinel-1 Time-Series
Olivier Dietrich, ETH Zurich
A machine-learning-based framework for retrieving water quality parameters in
highly urbanized areas using UAV hyperspectral images
Dr. Bing Liu, Peking University
Comparative Performance Analysis of simple U-Net, Residual Attention U-Net,
and VGG16-U-Net for Inventory InlandWater Bodies
Dr. Mohammadmehdi Saberioon., GFZ
High-resolution satellite images reveal tree growth promoted under urbanization
in South America
Dr. Jianhua Guo, TUM
A Multitask Earth Observation Dataset for Crop Prediction and Climate Applications
Adrian Höhl, TUM
Spatial-temporal dynamics of urban heat island hotspots
Keli Wang, TUM
Towards Large-scale Building Attribute Mapping using Crowdsourced Images:
Scene Text Recognition on Flickr and Problems to be Solved
Dr. Yao Sun, TUM
Geographic Transferability of Building Type Models
Nikolai Skuppin, DLR
Application of deep neural networks to retrieve cloud properties for Sentinel-4
(S4) and TROPOMI / Sentinel-5 Precursor (S5P)
Fabian Romahn, DLR
Estimating Soil Parameters from DESIS Images using Deep Learning
Xiangyu Zhao, TUM
Leveraging High-Resolution Satellite Imagery for Enhanced Organic Farm Inspection
in Compliance with EU Regulations
Tess Posch, Universität für Bodenkultur Wien
Quantifying Tree-species Specific Responses to the Extreme 2022 Drought in
Germany
Yixuan Wang; TUM
From Pixels to Species: Empowering Forest Tree Species Mapping with Sentinel-2 Time Series Data Using Deep Learning
Yang Mu, TUM
The spatial-temporal role of urban green space in mitigating land surface temperature
in Chinese mega-cities
Yue Zeng, TUM
Uncovering 22 Years of Deforestation in the Amazon with Artificial Intelligence
Bruno Matosak, Instituto Nacional de Pesquisas Espaciais (INPE)
Cocoa for Gold: mapping the landuse transition between two economically
substantial commodities
Stella Ofori-Ampofo, TUM
Predicting 5-year urban growth probability using satellite data, open data and machine learning
Anna-Lena Erdmann, Cellgrid
Deep-learning-based large-scale forest height estimation with the synergy of ICESAT-2, Sentinel-1 and Sentinel-2 imagery
Dr. Qi Zhang, TUM
A Glance at theWorld Forest by Machine Learning
Haiyin Ye, TUM
Mapping inland aquaculture ponds using deep learning methods
Dr. Qingyu Li, TUM
Poster Session II: AI4EO Methods
Self-Supervised Transfer Learning for Historical Aerial Images
Marvin Burges, TU Wien, Vienna University of Technology
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
Zhaiyu Chen, TUM
Generative Adversarial Networks for Long-term Subtle Volcano Deformation Detection
Teo Beker, TUM / DLR
Adaptive Morphology Filter: A Lightweight Module for Deep Hyperspectral Image Classification
Dr. Xin Sun PhD, TUM
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Yi Wang, TUM / DLR
An inconsistency-aware network for precipitation downscaling
Shan Zhao, TUM
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
Fahong Zhang, TUM
Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation
Chenying Liu, TUM / DLR
HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images
Sining Chen, TUM
Hybrid Quantum Deep Learning with Superpixel Encoding for Earth Observation Data Classification
Fan Fan, DLR
Cross-resolution image segmentation for mapping smallest ponds in the Arctic
Tabea Rettelbach, AlfredWegener Institute Helmholtz Centre for Polar and Marine Research
A Comparison of Uncertainty Quantification Methods for Earth Observation Image Regression Data
Nils Lehmann, TUM
Research Project Methane detection and quantification using a multimodal deep learning approach
Enno Tiemann, TUM / OHB Digital Connect GmbH
Glacier Ice Thickness Estimation from Physics-aware Machine Learning
Viola Steidl, TUM
Rapid building damage assessment pipeline near urban flash flood events using unsupervised change detection
Jeremy Eudaric, DLR
Learning high resolution encoding for coarse weather forecasting: Going beyond subgrid eddy forcing
Constantin Le Clei, TUM
Towards AI-based EO Services Using Hyperspectral and Thermal-Infrared Data
Marco Spagnolli, OHB System AG
Simulate LiDAR point cloud for biomass estimation
Dr. Qian Song, TUM
AdaptMatch: Adaptive Matching for Semi-supervised Binary Segmentation of Remote Sensing Images
Wei Huang, TUM
RRSIS: Referring Remote Sensing Image Segmentation
Zhenghang Yuan, TUM