GIS Colloquium – Talks (Summer Term 2023)
GeoAI Research and Technology Transfer for National Mapping
Dr. Samantha T. Arundel, Center of Excellence for Geospatial Information Science, US Geological Survey, USA
Di. 18.7.2023, 14:15 Uhr (Ort: INF 230, Centre for Organismal Studies (COS), EG, R00.005 „großer Seminarraum“
GeoAI Research and Technology Transfer for National Mapping" highlights the application of Artificial Intelligence (AI) and Geographic Information Systems (GIS) for national mapping. The presentation emphasizes the use of GeoAI technology for efficient and accurate data acquisition, processing, and analysis in the GIS, Cartography and Mapping fields. Dr. Arundel will discuss the potential benefits of AI in mapping, such as reduced costs, increased accuracy, and faster mapping processes. The presentation also discusses various applications of GeoAI technology, such as image recognition, object detection, and optical character recognition (OCR). Of particular emphasis is the importance of partnerships between research institutions and government agencies to promote the adoption of AI technology in mapping. Finally, the presentation showcases some examples of successful implementation of GeoAI technology in national mapping, including the use of AI for feature extraction from various types of imagery, noise reduction in point-clouds, and OCR for knowledge extraction from historical maps.
Spatial Optimization, Significance and Evolving GIScience
Prof. Dr. Alan T. Murray, Department of Geography, University of California at Santa Barbara, CA 93106, USA
Mo. 03.7.2023, 14:30 Uhr (Ort: COS, INF 230, EG R.005
Spatial optimization is introduced and reviewed in historical terms. The significance of spatial optimization is demonstrated through current analysis, management, planning and policy contexts focused on emergency response, food production, wildfire risk mitigation and public health monitoring.
Further, mathematical formalization in spatial optimization offers a theoretical framework to establish findings as significant. The ways in which GIScience perspectives can directly and indirectly support spatial optimization are highlighted.
The Science of GeoAI: Predictability, Explainability, and Reproducibility
Prof. Dr. WenWen Li, Arizona State University, USA
Mo. 12.06.2023, 10:15 Uhr (Ort: INF 348, Raum 004, EG)
AI, especially deep learning, has transformed many science and engineering disciplines because of its outstanding capabilities in learning and extracting patterns and knowledge in a data-driven manner. Geography is an ideal domain to extensively apply and further boost AI research because of its vast availability of diverse geospatial data, intriguing and complex human-environmental interactions, as well as its central role in enabling location-based analysis. In less than a decade, we have witnessed a rapidly growing interest and progress of GeoAI research – the transdisciplinary expansion of AI in geography and its sibling domains in urban, environmental, and social sciences. This talk will review the series of GeoAI research conducted at ASU, especially related to its applications in environmental analysis and natural feature detection. It involves the early attempts of improving the predictability of a GeoAI model and recent research in increasing its explainability and reproducibility to ensure trustworthy findings. The talk will also provide some reflections on the importance of sharpening the "science" of GeoAI in terms of its fundamental principles, theories, and methods to ensure its scientific rigor, liveliness, and long-lasting impacts.
Advancing urban modelling with emerging geospatial datasets and crowdsourcing
Dr. Filip Biljecki, National University of Singapore
Mon, April 24, 10 am (INF 252, Hörsaalzentrum Chemie, Hörsaal West)
The talk presents recent research efforts on urban and geospatial modelling at the Urban Analytics Lab at the National University of Singapore, and it focuses specifically on understanding the usability of emerging datasets and crowdsourcing.
The Lab spearheads a holistic and intertwined research agenda that covers the entire geospatial process in the urban realm: from advancing means to acquire data and standardising it to developing new use cases and unlocking value with AI & data analytics.
Much of the work is focused on OpenStreetMap and other VGI, and the talk overviews recent investigations on data quality, data gaps, and understanding contributors.
