Before this semester, I was familiar with crowdsourcing only in the context of consumer behaviour, using it to search for the best restaurants, hotels, etc. It was not until Dr. Stephen Ward spoke to our class that I realized the endless broad and diverse applications of crowdsourcing using available GIS and satellite imagery of the Earth. Dr. Ward discussed how DigitalGlobe launched their crowdsourcing platform Tomnod on March 11th in order to increase efforts to find the missing Malaysian plane. Using Tomnod, over 25,000 people have been able to scan satellite imagery and tag highly important areas, which are then run through algorithms to sift out all irrelevant information. Within a couple days, Tomnod uploaded over 1,235 square miles of high-resolution satellite imagery of the Gulf of Thailand, making me question how, even with crowdsourcing, we would be able to efficiently sort through the massive amounts of data to find the important details. Although computers use complex algorithms to determine what is noise and what is most likely relevant, I cannot help question the reliability and efficiency of this process.
According to The Stream Official Blog, some users, reported coordinates for interesting objects, such as an outline of what appeared to be a plane underwater, and oil slicks and metal/plastic debris. However, several people are skeptical about the practicality of using crowdsourcing to find the plane, as the plane probably will not resemble a plane any longer and the lack of visibility of debris due to the limited resolution of the satellite. What prevents people from tagging every rock or garbage they see? Also, how are we certain that the algorithms don’t discard any relevant information?
Over the past five years the developments in crowdsourcing has enabled it to be applied to several disciplines, such as science, international development, and security. It has been used to find missing people, determine future famines, highlight current conflict areas, and supply information that would otherwise go unknown. That being said, I fear we still lack the scientific capacity to rely as heavily as we have been on GIS and crowdsourcing. We cannot significantly reduce ground searches and ground operations until we successfully use GIS and crowdsourcing several more times. In the future, I think GIS and crowdsourcing will alter the development sector; however, we must continue to develop innovative ways to more efficiently and accurately deal with the influx of data before we rely on this method.