Between Sensors and GIS Content

One of the things that has always struck me as unusual is that there does not seem to be a lot of insight into the role of photogrammetry in the broader geospatial community. For example, google “GIS data” and you’ll get a lot of hits on various GIS data sources, including street maps, census data, building footprints, cadastral data, and so forth. However, it is important to keep in mind that this data typically is not first generation, but rather a derived product. Often the data lineage is not tracked (or at least not tracked back directly to the sensor), so users making business decisions based on the data do not have insight into how the data was developed, potential problems with the data, the true accuracy, and other issues…

At any rate, the point of this post is that photogrammetric processing provides the link between satellite/airborne sensors and GIS data. How and why? This is because geospatial data frequently derived from some sort of sensor, be it airborne imagery, satellite, LIDAR, GPS, or any other of the measurement sensor technology. This is how we get the “Geographic” part of GIS. All that great orthorectified imagery you see as a layer in a commercial GIS or in Google Earth, Microsoft Virtual Earth, and ERDAS TITAN typically goes through some sort of photogrammetric processing. The Virtual Earth 3D blog has a good post on how UltraCamX sensor data is processed for delivery in Virtual Earth. The post doesn't contain the technical details of exactly what sort of processing is applied, as the exact nuts and bolts of the workflow is likely a Microsoft/Vexcel trade secret at the moment. The general workflow is fairly well-known though, as photogrammetric processes can produce all sorts of primary base map and other data (mainly orthos, terrain, and 3D feature data) as input into a spinning globe app or a GIS...

Stay tuned for the next post and I will walk through an "photogrammetry to GIS" technical example...