A Visual Active Search Framework for Geospatial Exploration

TL;DR

This paper introduces a reinforcement learning-based visual active search framework for geospatial exploration, optimizing object detection in satellite imagery within limited search budgets, and demonstrates significant improvements over baselines.

Contribution

The paper presents a novel RL-based meta-search policy for geospatial search tasks, with domain adaptation techniques to handle domain gaps, advancing the state-of-the-art in satellite imagery analysis.

Findings

Significant performance improvements over baselines in satellite imagery search tasks.

Effective domain adaptation techniques enhance policy performance with domain gaps.

The approach generalizes well across multiple large-scale datasets.

Abstract

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.