1st ACM SIGSPATIAL Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery (GeoAI 2017)

Deep Learning and Artificial Intelligence (AI) techniques are transforming a range of sectors from computer vision and natural language processing to autonomous driving and healthcare. In particular, deep learning methods achieve great success in many computer vision problems, such as image classification and object detection. Deep neural networks are very powerful to capture the hierarchical representation of features in massive and complex data by adopting multiple layers of non-linear information processing. Due to the availability of vast and high-resolution geospatial data and efficient high-performance computing architectures, deep learning techniques empower the geospatial system to provide fast and near-human level perception. For example, recent studies have shown deep learning techniques coupled with volunteered geographic information (such as OpenStreetMap data) can accurately extract buildings from satellite imagery for humanitarian mapping in rural African areas. Also, deep learning helps assimilate autonomous vehicles and intelligent transport system by incorporating a great amount of information gathered by traffic cameras and sensors.

Moreover, deep learning technology facilitates the discovery of geographic information within unstructured text data across different languages. There are also many other applications of deep learning in the domain of GIS, such as the prediction for spatial diffusion patterns in epidemiology, urban expansion prediction, and hyperspectral image analysis. Given much success achieved and huge interests in this field, deep learning and AI have become an important topic discussed in many international conferences, such as KDD (Knowledge Discovery and Data Mining), CVPR (Computer Vision and Pattern Recognition), ICML (International Conference on Machine Learning), and ACL (Association of Computational Linguistics).