EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues

TL;DR

EarthDial is a specialized conversational AI for Earth Observation data that supports multi-sensory, multi-temporal, and multi-resolution analysis, enabling diverse remote sensing tasks through extensive instruction tuning.

Contribution

The paper introduces EarthDial, a novel Earth Observation-specific Vision-Language Model with a large instruction tuning dataset covering multiple modalities and resolutions, surpassing existing models in EO tasks.

Findings

Outperforms existing models on 44 EO datasets

Supports multi-spectral, multi-temporal, and multi-resolution data

Enables diverse remote sensing applications

Abstract

Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and {resource management}. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor modalities. In this paper, we introduce EarthDial, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues. EarthDial supports multi-spectral, multi-temporal, and multi-resolution imagery, enabling a wide range of remote sensing tasks, including classification, detection, captioning, question answering, visual reasoning, and visual grounding. To achieve this, we introduce an extensive instruction tuning dataset comprising over 11.11M instruction pairs covering RGB, Synthetic Aperture Radar (SAR), and multispectral modalities such as Near-Infrared (NIR) and infrared. Furthermore, EarthDial handles bi-temporal and multi-temporal sequence analysis for applications like change detection. Our extensive experimental results on 44 downstream datasets demonstrate that EarthDial outperforms existing generic and domain-specific models, achieving better generalization across various EO tasks. Our source codes and pre-trained models are at https://github.com/hiyamdebary/EarthDial.