GOMAA-Geo: GOal Modality Agnostic Active Geo-localization
Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen, Chongjie Zhang,, Nathan Jacobs, Yevgeniy Vorobeychik

TL;DR
GOMAA-Geo is a versatile active geo-localization system that effectively uses cross-modal learning and reinforcement to locate targets across different modalities and datasets, even in unseen scenarios.
Contribution
It introduces a goal modality agnostic approach that generalizes across modalities and datasets using contrastive learning, foundation model pretraining, and reinforcement learning.
Findings
Outperforms alternative methods in localization accuracy
Generalizes to unseen datasets like disaster-hit areas
Successfully handles multiple goal modalities such as language and imagery
Abstract
We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple possible modalities. This could emulate a UAV involved in a search-and-rescue operation navigating through an area, observing a stream of aerial images as it goes. The AGL task is associated with two important challenges. Firstly, an agent must deal with a goal specification in one of multiple modalities (e.g., through a natural language description) while the search cues are provided in other modalities (aerial imagery). The second challenge is limited localization time (e.g., limited battery life, urgency) so that the goal must be localized as efficiently as possible, i.e. the agent must effectively leverage its sequentially observed aerial views when searching for the goal. To address these challenges, we…
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Taxonomy
TopicsGeological Modeling and Analysis · Seismology and Earthquake Studies · Seismic Imaging and Inversion Techniques
MethodsALIGN · Contrastive Learning
