Abstract. The expansion of data collection from remote sensing and other geographic data sources, as well as from other technology such as cloud, sensors, mobile, and social media, have made mapping and analysis more complex. Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation. In recent years, GPU processing has improved far more GIS applications than using CPU alone. As a result, numerous researchers have begun utilising GPUs for scientific, geometric, and database computations in addition to graphics hardware use. This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies. This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.
Abstract. Machine Learning used to refer as one Artificial Intelligence subsection that perform self-learning computational algorithms either supervised learning or unsupervised learning tasks. Machine Learning can compute a prediction onto hidden data patterns that hardly for human to detect. This valuable information and predictions able to help companies or researchers make a crucial decision making especially in natural disasters. In Geographic Information Systems (GIS), the advancement of Machine Learning used literally on satellite imagery analyses and fewer on LIDAR point clouds. In this paper presents an overview of Machine Learning definitions, big geospatial data, Machine Learning types and models, and advancement researches using Machine Learning in big LIDAR data.
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