Integrating camera imagery, crowdsourcing, and deep learning to improve high-frequency automated monitoring of snow at continental-to-global scales - Fig 2

Examples of false positive (A-C) and false negative (D-I) images. Beneath each image are the labels provided by three participants (“Crowd”), convolutional neural networks (“CNN”), and a MODIS snow product. S = Snow, N = No snow, X = not available. False positives are due to (A) ice on lake, (B) fog, (C) precipitation on the lens. False negatives are due to (D) snow on distant mountains and (E-I) patchy snow.