In this paper, we investigated the factors affecting human accuracy in geolocalizing wildfires based on visual imagery. Through an online survey of 71 participants of diverse demographics, we evaluated three key hypotheses regarding wildfire location estimation. Participants were presented with images captured by the FireDetectAI system during the summer of 2022 and asked to mark estimated fire locations on a map using a custom Leaflet-based interface. Our analysis revealed that incorrectly marked fire alarms significantly reduced location accuracy (mean distance error of 7.86 km versus 9.89 km for other scenes). Camera misalignment also negatively impacted geolocalization precision, with misaligned scenes showing higher error values when outliers were excluded. The presence of landmarks improved estimation accuracy (mean distance error of 7.84 km versus 10.37 km for scenes without landmarks). Statistical analysis of the results reveals inherent challenges in relying solely on human visual assessment for wildfire location estimation, particularly in featureless terrain, and suggests that improved annotation accuracy, proper camera alignment, and landmark presence could enhance human geolocalization performance. These insights have important implications for the design of wildfire monitoring systems and emphasize the potential benefits of supplementing human assessment with automated detection and geolocalization technologies for more effective early warning and response.