Model assessment & interpretation in Geospatial ML: spatial dependence & high dimensionality
0As the interpretability and explainability of artificial intelligence decisions has been gaining attention, novel approaches are needed to develop diagnostic tools that account for the unique challenges of environmental data, especially the spatial dependence of measurements and the high dimensionality of feature spaces. These are addressed by novel methods presented in this contribution with examples such as the regionalization of pollutants in the environment, and remotely-sensed mapping of mountain permafrost features as an essential climate variable and potential hazard. \n\nBuilding upon the geostatistical tradition of distance-based measures, spatial prediction error profiles (SPEPs) and spatial variable importance proles (SVIPs) are introduced as novel model-agnostic assessment and interpretation tools that explore the behavior of models at different prediction horizons.\n\nIn the case study, SPEPs and SVIPs successfully highlight differences and surprising similarities among geostatistical and machine-learning algorithm. Moreover, to address the challenges of interpreting the joint effects of strongly correlated or high-dimensional features, often found in remote sensing, a model-agnostic approach is developed that distills aggregated relationships from complex models into a lower-dimensional interpretation space. \n\nThe novel diagnostic tools enrich the toolkit of geospatial data science, and may improve machine-learning model interpretation, selection, and design in a variety of geospatial application domains. \n\nThe AI for Good Global Summit is the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure, and other global development priorities. AI for Good is organized by the International Telecommunication Union (ITU) – the UN specialized agency for information and communication technology – in partnership with 40 UN sister agencies and co-convened with the government of Switzerland.\n\nSpeakers:\nAlexander Brenning\nProfessor\nFriedrich Schiller University Jena\n\nModerators:\nMarkus Reichstein\nDirector & Professor\nMax Planck Institute for Biogeochemistry\n\nJoin the Neural Network!\nhttps://aiforgood.itu.int/neural-network/\nThe AI for Good networking community platform powered by AI. \nDesigned to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\n\n Watch the latest #AIforGood videos!\n\n\n Stay updated and join our weekly AI for Good newsletter:\nhttp://eepurl.com/gI2kJ5\n\nCheck out the latest AI for Good news:\nhttps://aiforgood.itu.int/newsroom/\n\nExplore the AI for Good blog:\nhttps://aiforgood.itu.int/ai-for-good-blog/\n\n Connect on our social media:\nWebsite: https://aiforgood.itu.int/\nTwitter: https://twitter.com/AIforGood\nLinkedIn Page: https://www.linkedin.com/company/26511907 \nLinkedIn Group: https://www.linkedin.com/groups/8567748 \nInstagram: https://www.instagram.com/aiforgood \nFacebook: https://www.facebook.com/AIforGood\n\nDisclaimer:\nThe views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.