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Large wood is an integral part of many rivers, often defining river‐corridor morphology and habitat, but its occurrence, magnitude, and evolution in a river system are much less well understood than the sedimentary and hydraulic components, and due to methodological limitations, have seldom previously been mapped in substantial detail. We present a new method for this, representing a substantial advance in automated deep‐learning‐based image segmentation. From these maps, we measured large wood and sediment deposits from high‐resolution orthoimages to explore the dynamics of large wood in two reaches of the Elwha River, Washington, USA, between 2012 and 2017 as it adjusted to upstream dam removals. The data set consists of a time series of orthoimages (12.5‐cm resolution) constructed using Structure‐from‐Motion photogrammetry on imagery from 14 aerial surveys. Model training was optimized to yield maximum accuracy for estimated wood areas, compared to manually digitized wood, therefore model development and intended application were coupled. These fully reproducible methods and model resulted in a maximum of 15% error between observed and estimated total wood areas and wood deposit size‐distributions over the full spatio‐temporal extent of the data. Areal extent of wood in the channel margin approximately doubled in the years following dam removal, with greatest increases in large wood in wider, lower‐gradient sections. Large‐wood deposition increased between the start of dam removal (2011) and winter 2013, then plateaued. Sediment bars continued to grow up until 2016/17, assisted by a partially static wood framework deposited predominantly during the period up to winter 2013. Plain Language Summary: We measure the large wood in the Elwha River, Washington, USA, during and after dam removal. The presence of two dams had previously limited the movement of sediment and wood through the system. The removal of those dams liberated large amounts of sediment and wood from the former reservoir bottoms, which traveled downstream and deposited in the river channel. We develop an Artificial Intelligence (AI) model to measure all wood and sediment in the Elwha River corridor downstream of the two former dams, from a time‐series of high‐resolution imagery collected from aircraft. These measurements, accurate to within 15% of true values, provide a unique opportunity to understand how large wood occurs and behaves over multiple years and tens of kilometers. We found that the deposition of large wood on bars was coincident with and promoted the growth of sediment bars. The AI model we made could be powerful enough to find large wood in other places and images for similar purposes. Our data sets and models are made available to stimulate further studies of changes in river form resulting from interactions between water flow, wood, sediment, and vegetation. Key Points: We develop automated methods for detection and mapping of large wood, sediment, vegetation, and water from a time‐series of orthoimageryHigh‐resolution, reach‐wide measurements reveal wood dynamics in two Elwha River reaches adjusting to upstream dam removals over 5.5 yearsLarge wood deposition increased after dam removal, then plateaued, and bars continued to grow assisted by a partially static wood framework [ABSTRACT FROM AUTHOR]