Taming Rich GML With stETL, A Lightweight Python Framework For Geospatial ETL
Formal Metadata
| Title | Taming Rich GML With stETL, A Lightweight Python Framework For Geospatial ETL |
|
| Title of Series | |
| Number of Parts | 95 |
| Author | |
| License | CC Attribution - NonCommercial - ShareAlike 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this license. |
| Identifiers | |
| Publisher | |
| Release Date | |
| Language | |
| Production Place | Nottingham |
Content Metadata
| Subject Area | |
| Genre | |
| Abstract | Data conversion combined with model and coordinate transformation from a source to a target datastore (files, databases) is a recurring task in almost every geospatial project. This proces is often refered to as ETL (Extract Transform Load). Source and/or target geo-data formats are increasingly encoded as GML (Geography Markup Language), either as flat records, so called Simple Features, but more and more using domain-specific, object oriented OGC/ISO GML Application Schema's. GML Application Schema's are for example heavily used within the INSPIRE Data Harmonization effort in Europe. Many National Mapping and Cadastral Agencies (NMCAs) use GML-encoded datasets as their bulk format for download and exchange and via Web Feature Services (WFSs). |
|
