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We used streetview (SV)-based metrics of greenspace from an on-the-ground perspective to examine greenspace types that may be relevant for dementia risk. We followed 6761 participants from the Multi-Ethnic Study of Atherosclerosis, 2000-2018. Using deep learning algorithms applied to SV images, we measured %trees, %grass, %other green (plants\/flowers\/fields) in the 500\u00a0m area around participants\u2019 residences. We examined the controlled direct effect of greenspace with dementia accounting for death as a censoring event, using a weighted Kaplan\u2013Meier survival estimator, with the product of exposure weights and inverse-probability-of-censoring by death. We ran models separately per greenspace type. Mean baseline age was 62 years (SD, 10). Participants exposed to lowest levels of %trees were more likely to have a household income\u00a0&amp;lt;\u00a0$24\u2009999. We observed 585 (8.7%) dementia cases and 1437 (21.3%) deaths over follow-up. We observed a direct effect of high exposure to %grass on 19-year dementia risk had death been prevented of \u22124.57 (95% CI, -8.15, \u22120.40) percentage points. No associations were observed for %trees (\u22120.07; 95% CI, \u22122.29 to 2.11) and %other green (\u22120.03, 95% CI, \u22121.59 to 1.48). 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