For most geospatial data processing pipelines, mapping is the last stage — the final product shown to the user, summarizing the regions of interest and visualizing hotspots for further intervention. However, many projects are not able to easily present these final products to general audiences, because the problem of rendering large digital raster and vector maps in a scalable fashion across the internet is too challenging for most researchers, and particularly when compounded against restrictive digital infrastructure in the developing world.
After evaluating many mapping tools, including both Geoserver and Geonode, we opted to design a new solution that fulfills our mapping needs. Some of the features of our platform include:
- Render both raster and vector maps at multiple lower levels of resolution before investing in the download of large files.
- Store large files in a scalable way.
- Easily colorize maps for clear presentation of gradients and differences, without the fuss that Geoserver presents.
- Crop imagery to download only the region of interest required.
- Index and search through many layers of imagery based on their type and full metadata descriptions.
- Provide APIs that are interoperable with other applications in the SPEED ecosystem, such as Geosurvey.
Maps provides users with a high level of functionality, satisfying all of the above use cases through the construction of many back-end and front-end innovations. We use it both to host existing gridded satellite imagery such as that generated by AfSIS through CIESIN, as well as predictive maps generated by QED’s machine learning crew.