Machine Learning for Remote Sensing (ML4RS)
Workshop Type: Hybrid
Keywords: machine learning, remote sensing, self-supervised learning, multi-fidelity data fusion, federated learning, data-centric AI, human-in-the-loop and active learning, machine learning for time series, agriculture and food security, forestry, biodiversity, species distribution modeling, natural hazards and disasters
Abstract: Promoting diverse viewpoints and trans-disciplinary research is the basis for addressing the pressing questions of our times, such as climate change, social inequalities, biodiversity, and food security. Developing modern machine learning approaches tailored towards remote sensing data is key to investigating these problems efficiently. This second Machine Learning for Remote Sensing (ML4RS) workshop promotes this exchange by allowing researchers to present their a) research on environmentally and societally important applications and/or b) innovative methods that can have an impact in such application domains. This workshop is the continuation of the ICLR 2023 ML for Remote Sensing workshop that enabled local stakeholders, researchers, and students, for instance, from the Rwanda Space Agency and local CMU Africa, to discuss and debate the key problems to be addressed with machine learning. In this workshop in Vienna, we continue in this spirit by giving locally-based experts a voice to articulate important regional challenges in ML4RS, for instance, by inviting domain scientists from international organizations, such as the IAEA or the Red Cross (invited panelists). The keynote speakers are leading researchers in the intersection of machine learning and remote sensing. Our workshop is financially sponsored by industry and governmental organizations, such as the European Space Agency (pledged 3k EUR). With this proposal, we hope to establish a continual exchange in this environmentally and socially highly relevant research field of machine learning for remote sensing for the upcoming years.
Submission Number: 34
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