The manuscript “Automating tephra fall building damage assessment using deep learning” by Eleanor Tennant et al. presents a pipeline based on UAV imagery and deep learning to automatically localize buildings and make a classification of the damage caused by tephra fall.

The manuscript is generally well written, logically organized, and adequately illustrated. I recommend it for publication after few minor points are addressed in revision, as described in the following.

• The manuscript is too focused on methodology. I understand that it represents the heart of the work, but the text seems too unbalanced in relation to the results and the chosen volcanic application (the 2021 eruption of the La Soufrière volcano, St Vincent and the Grenadines). I suggest lightening Sections 2 and 3, moving even more details into the supplementary material and simplifying the main text. This would definitely make reading faster and more fluent.
• The location of Table 1 cannot be the Introduction. It provides a performance comparison of several models, including the one described in this manuscript, and should therefore be included in the Discussions. It is not logically correct to introduce F1, mean average precision and accuracy scores before even introducing the model. It is also not immediately clear what "P", "P & P", "C1" and "C2" mean.
• In Figure 1, please include the location of Georgetown.
• The text is full of diagrams and tables. Perhaps it would be more attractive to a wider audience if the authors included some figures on the case study (for example, some images used for the model development, currently in the supplementary material).