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Mosquitoes are vector organisms that spread diseases to thousands of people worldwide, contributing to the considerable number of deaths due to vector-borne illness. This study serves to identify larvae of the malaria-spreading Anopheles genus of mosquitoes in North America while fueling methods to refine the NASA GLOBE Observer data set and promoting Citizen Science. Citizen Science data are considered highly inaccurate in the scientific community, with the reasoning that almost anyone, trained expert or not, can contribute to these data sets with varying levels of precision. To improve the accuracy and credibility of such Citizen Science data sets--in this case, the GLOBE Observer database--a sample of 155 unique mosquito observations were pulled from the database. Using this sample, trained classifiers and mosquito experts reclassified each reported observation to check Citizen Scientist’s accuracy when identifying Anopheles larvae. As a result of this reclassification the majority of Citizen Science data, in this aspect, is proven to be accurate, but perhaps not at the desired level of accuracy. For this reason, this refined sample of data can be used as a baseline for an AI recognition system to automatically classify images taken by Citizen Scientists as either Anopheles or non-Anopheles, in further development of this study.