Geoserver | INES Ingenieros

IngestaGeo

Cloud-based system for the ingestion, management and service of georeferenced maps.

The system enables the centralization of georeferenced data in a single location. It utilices a map server, such as Geoserver, through its REST API via a Python backend. Georeferenced layers are ingested serverlessly using GDAL and OGR tools. Vector layers are stored in RDS, while raster layers are stored in Amazon S3. The system is cloud-native, designed and implemented to scale on demand.

Main Features

Support for Numerous Formats

Thanks to the use of native geospatial image processing tools like OGR and GDAL, we support virtually all geospatial data formats.

User Transparency

Users interact through a web interface using a request-based system where they upload the layers they consider necessary. Once approved by an administrator, the layers are processed and made accessible to others.

Integration with Other Tools

Compatible with desktop tools like QGIS and ArcGIS. Also supports web visualization tools such as OpenLayers, Leaflet, MapStore, GeoServer, and more.

Group Based Layer Visualizacion Rescriction

Layer visibility can be restricted to certain user groups, allowing for a single system to host all the data while providing access granularly based on specific needs.

Load, Transformation and Automatic Georeferenced Layers Publishing

The tool allows the creation and connection of a centralized source of geospatial data with multiple tools, automating the ingestion process and making its use accessible to the average user.

Vector Layer Ingestion in OGR Containers

For the ingestion of vector layers, a containerized service is launched, adjusting resource capacity as needed. In the case of vector layers, OGR is used to ingest them into a PostgreSQL database running PostGIS, enabling geospatial operations on the stored data. Both the execution container and the database are hosted in the cloud.

Optimezed Storage of Rasters in the Cloud

Uploaded rasters are stored in AWS S3, after having been converted to COG format, optimizing the cloud and operational costs. A containerized service in the cloud using GDAL tools is responsible of the transformations.