International Journal of Geographical Information Science
GeoAI: Artificial Intelligence Techniques for Geographic Knowledge Discovery
摘要截稿: 2017-11-30
全文截稿: 2018-04-30
影响因子: 3.733
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 地球科学 - 2区
• 小类 : 计算机:信息系统 - 2区
• 小类 : 地理学 - 2区
• 小类 : 自然地理 - 2区
• 小类 : 图书情报与档案管理 - 2区
Overview
Novel Artificial Intelligence (AI) techniques are transforming a range of fields from computer vision and natural language processing to autonomous driving and healthcare. With the availability of heterogenous, high-resolution geographic data and high-performance computing, techniques such as deep learning are enabling the discovery of new geographic knowledge, providing fast and accurate object detection and inferences, and supporting the development of more intelligent geographic information systems (GIS). For example, recent studies have shown that AI techniques coupled with volunteered geographic information can accurately extract buildings from satellite images for humanitarian mapping. Machine learning and natural language processing are facilitating the extraction of geographic information from unstructured text data, such as news articles and Wikipedia pages. Semantic Web technologies, ontologies, and Linked Data are being employed to improve geographic information retrieval and to construct advanced geographic knowledge graphs for geo-enrichment. A combination of multiple techniques, also helps integrate autonomous vehicles with intelligent transport systems by incorporating real-time information gathered by traffic cameras and other sensors. There are many other potential applications of AI in geospatial research. This special issue aims to bring together the latest research on this exciting topic focusing on the interface on deductive and inductive techniques.
The special issue complements the 2017 ACM SIGSPATIAL Workshop on AI and Deep Learning for Geographic Knowledge Discovery (https://udi.ornl.gov/geoai) and the relevant special sessions in 2018 AAG Annual Meeting. However, submissions to the special issue are open to all interested authors regardless of their participations in the workshops.
Relevant Topics Include
- General AI techniques for geographic knowledge discovery
- Deep learning/machine learning for object recognition and geographic information extraction
- Geo-ontologies and knowledge graphs for geographic information retrieval and enrichment
- Geographic Linked Data and Geospatial Semantic Web for AI development and geographic knowledge discovery
- AI techniques for gazetteer construction and enrichment
- Deductive and Inductive methods for geographic knowledge discovery
- AI in autonomous transportation and high-precision maps
- High-Performance Computing (HPC) architecture for deep learning in geospatial domains
- Applications of machine learning and general AI in disaster response
- Social sensing and semantic signatures
- Generation of geospatial datasets and novel techniques to study data quality