Intelligent agents stand out as a potential path toward artificial general intelligence (AGI).
Thus, researchers have dedicated significant effort to diverse implementations for them …

Over the past decade, big data incorporating a spatial component “GEOSPATIAL BIG DATA”
has become a global focus, increasingly attracting the attention of academia, industry …

Pre-processing spatial data for machine learning applications often includes combining
different datasets into a form usable by the machine learning algorithms. Spatial data is …

The explosive growth of massive high-dimensional data requires capabilities for data
processing, storing, and analyzing. This brings significant challenges to traditional …

Abstract< jats: p> While a significant number of databases are deployed in cloud
environments, pushing part or all data storage and querying planes closer to their sources …

Spatial join processing techniques that identify intersections between complex geometries
(eg, polygons) commonly follow a two-step filter-and-refine pipeline. The filter step evaluates …

Vector databases have emerged as the computation engine that enables us to successfully
interact with vector embeddings in our applications as a result of the exponential rise of …

There has been an increase in the amount of spatial data in the recent years due to the
advancements in remote sensing technology and the widespread use of smart phones and …

Abstract Buku" Sistem Informasi Geografis untuk Tata Ruang" menyajikan panduan
komprehensif tentang penggunaan SIG dalam perencanaan tata ruang. Bab pertama …

Geospatial finance is an emerging field that integrates spatial data and analysis to provide
insights into financial and environmental, social, and governance (ESG) aspects of various …