  Many problems can be viewed as forms of geospatial search aided by aerial
imagery, with examples ranging from detecting poaching activity to human
trafficking. We model this class of problems in a visual active search (VAS)
framework, which has three key inputs: (1) an image of the entire search area,
which is subdivided into regions, (2) a local search function, which determines
whether a previously unseen object class is present in a given region, and (3)
a fixed search budget, which limits the number of times the local search
function can be evaluated. The goal is to maximize the number of objects found
within the search budget. We propose a reinforcement learning approach for VAS
that learns a meta-search policy from a collection of fully annotated search
tasks. This meta-search policy is then used to dynamically search for a novel
target-object class, leveraging the outcome of any previous queries to
determine where to query next. Through extensive experiments on several
large-scale satellite imagery datasets, we show that the proposed approach
significantly outperforms several strong baselines. We also propose novel
domain adaptation techniques that improve the policy at decision time when
there is a significant domain gap with the training data. Code is publicly
available.
