Finding Swimming Pools in Australia using Deep Learning · Tomnod

In a recent project, we found which of 700000 property parcels in Adelaide, Australia, contain swimming pools. We used a combination of crowdsourcing and supervised machine learning in order to harness the inherent ability of humans to identify objects in imagery and the speed of machines, which can perform this task much faster than humans, once trained sufficiently. Our initial approach consisted of training a random forest classifier with a set of crowdsourced labels, then using the machine classifications to present to the crowd only the parcels that were likely to contain swimming pools. Since only a small percentage of the parcels actually contain pools, the efficiency gain of this approach is huge compared to a pure crowdsourcing campaign. At first glance, identifying a pool in a high-resolution satellite image might appear to be a simple task for a human and a machine alike.