{"id":2972,"date":"2024-09-09T14:13:27","date_gmt":"2024-09-09T12:13:27","guid":{"rendered":"https:\/\/test.it-u.at\/?post_type=research&#038;p=1796"},"modified":"2026-03-04T16:38:18","modified_gmt":"2026-03-04T15:38:18","slug":"geosocial-ai","status":"publish","type":"research","link":"https:\/\/it-u.at\/en\/research\/research-groups\/geosocial-ai\/","title":{"rendered":"Geosocial Artificial Intelligence"},"content":{"rendered":"\n<p>The growing influence of digital data and tools, along with recent advancements in AI, is transforming how we approach research. With the increasing availability of large, open datasets\u2014ranging from social media posts and news articles to physiological measurements and mobile phone records\u2014entirely new fields of study are emerging. Many of these datasets include geospatial references, offering unique insights into spatial structures across a variety of real-world contexts, such as disaster management, urban well-being, mobility planning, and democratic-political developments.\u00a0<\/p>\n\n\n\n<!--more-->\n\n\n\n<p>Our research group is pioneering a dual approach that integrates both \u201csensing\u201d and \u201cactuating\u201d processes. This methodology not only focuses on analysing social environments but also actively shaping them through data-driven insights. We aim to create novel AI methodologies that provide deeper understanding of geospatial and social processes by analysing human-generated data. This holistic approach to research transcends traditional disciplinary boundaries and integrates concepts from various fields like geoinformatics, computer science, sociology, psychology, education research, and others.&nbsp;<\/p>\n\n\n\n<p><strong>Key research areas will include:<\/strong>&nbsp;<\/p>\n\n\n\n<p><strong>Multimodal Machine Learning<\/strong>: Investigating methods for analysing and integrating heterogeneous data sources simultaneously considering multiple modalities.&nbsp;<\/p>\n\n\n\n<p><strong>Information Fusion<\/strong>: Creating novel algorithms for combining various data streams into a single information layer.&nbsp;<\/p>\n\n\n\n<p><strong>Generative AI and Active Learning<\/strong>: Exploring generative models to create synthetic data, involving a human-in-the-loop approach.&nbsp;<\/p>\n\n\n\n<p><strong>Contextual Enrichment<\/strong>: Designing methods to contextualise information layers within relevant spatial, temporal, semantic or emotional contexts, thus increasing their interpretability.\u00a0<\/p>\n","protected":false},"featured_media":0,"template":"","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Geosocial Artificial Intelligence | IT:U<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/it-u.at\/en\/research\/research-groups\/geosocial-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Geosocial Artificial Intelligence | IT:U\" \/>\n<meta property=\"og:description\" content=\"The growing influence of digital data and tools, along with recent advancements in AI, is transforming how we approach research. With the increasing availability of large, open datasets\u2014ranging from social media posts and news articles to physiological measurements and mobile phone records\u2014entirely new fields of study are emerging. 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