The rapid increase in multimodal data, advanced deep learning algorithms, and the availability of fast hardware have largely contributed to a renewed interest in Artificial Intelligence (AI). Despite many successful stories in computer vision and natural language processing, there are challenges that remain to be solved, such as large scale neural-symbolic reasoning and automatic knowledge graph construction. Unsurprisingly, one of the most prominent topics in AI, nowadays, is the combination of representation learning (connectionist AI) with symbolic representation and reasoning (symbolic AI). By combining these two aspects, we are capable of developing scalable and explainable machine learning models. Such a trend becomes even more pressing with the foundation models (e.g., GPT-4) have been used everywhere just within the past few months. While embracing their surprisingly fascinating performance, the society also calls for a more explainable and responsible use of these foundation models.
From a geospatial point-of-view, GeoAI, as an interdisciplinary field of GIScience and AI, advocating the development of spatially explicit machine learning approaches and the use of novel AI techniques in geography and earth science. Graphs are at the core of GeoAI, as they have been shown to allow effective representations of semantics as well as spatial and temporal relationships. Geospatial knowledge graphs, particularly as symbolic representations of geospatial knowledge, facilitate many intelligent applications such as geospatial data integration and knowledge discovery. Nevertheless, many deep learning models treat geographic entities as ordinary entities in which spatial characteristics, such as spatial footprint or distance decay, are often ignored. This results in suboptimal performance in many geospatial related tasks including geospatial knowledge graph completion, geographic question answering, geographic entity alignment, as well as geographic knowledge graph summarization.
Following its former success in GIScience 2021, this workshop at GIScience 2023 will continue highlighting the importance of geospatial information and principles in designing, developing, and utilizing geospatial knowledge graphs and other GeoAI techniques to discover knowledge in geosciences. We invite researchers from disparate disciplines (e.g., environmental studies, GIScience, AI, cognition, supply chain, humanities, etc.) to submit papers in the following three formats. All submitted papers will be peer-reviewed by our Program Committee.
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