Automated vs. manual mapping - consequences for crowdsourcing
Digital cartography has made map making a lot easier. But If a map contains a lot of data or specific data, it can become a complex or costly adventure. Despite the efforts around open data, still the majority of data is not publicly available, and if so only for high costs. Crowdsourcing is one alternative to collect data for maptivism, but maybe some of these approaches will not soon be needed if automated mapping is further progressing.
It is quite impressive and a bit scary to see the pace of innovation around digital recognition. Its aim is to make more information available from the offline world. Google is on the frontrun of digital recognition with another example: 3D trees in Google Earth. Google has chosen parks in 50 cities around the world to identify in an automated process trees out of satellite images.
“With 3D trees in Google Earth, we’ve brought characteristic trees to life, from the palm trees that dot San Francisco's bayfront Embarcadero Street, to the olive trees that cling to the Acropolis in Athens, to the flowering dogwoods found in Tokyo’s parks. All told, there are around 50 different tree species to explore in Google Earth and counting!”
Consequences for mapping
A while ago I blogged about the crowdsourcing Urban Forest Map in San Francisco. Its goal is to map all trees in the city. Now at least the work for the park is not needed anymore if Google is giving out the data. Thousands of people from the Openstreetmap community use satellite imagery from Yahoo to draw shapes of buildings into maps. Is that becoming obsolete soon? What needs to be manually mapped? Of course a lot, because most of such data will not necessarily be publicly available. One example is real-time data. Check this post on maptivism: live tactical mapping for protest swarming.
Here, we are also getting in a dilemma. Such a virtualization of trees can contribute to the protection of forests. Imagine the mapping happens within days and deforestation in the rain forest can be act on quickly. However, what else can be mapped? If trees can be classified, all kind of objects can be classified if digital recognition software becomes increasingly powerful. Check for example the bird’s eye view from Bing, where you can see detailed aerial imagery (see image), not to mention Google street view.