  This work introduces composed image retrieval to remote sensing. It allows to
query a large image archive by image examples alternated by a textual
description, enriching the descriptive power over unimodal queries, either
visual or textual. Various attributes can be modified by the textual part, such
as shape, color, or context. A novel method fusing image-to-image and
text-to-image similarity is introduced. We demonstrate that a vision-language
model possesses sufficient descriptive power and no further learning step or
training data are necessary. We present a new evaluation benchmark focused on
color, context, density, existence, quantity, and shape modifications. Our work
not only sets the state-of-the-art for this task, but also serves as a
foundational step in addressing a gap in the field of remote sensing image
retrieval. Code at: https://github.com/billpsomas/rscir
