GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Kartik Kuckreja Muhammad Sohail Danish Muzammal Naseer Abhijit Das Salman Khan Fahad Shahbaz Khan
Recent advancements in Large Vision-Language Models (VLMs) have shown great
promise in natural image domains, allowing users to hold a dialogue about given
visual content. However, such general-domain VLMs perform poorly for Remote
Sensing (RS) scenarios, leading to inaccurate or fabricated information when
presented with RS domain-specific queries. Such a behavior emerges due to the
unique challenges introduced by RS imagery. For example, to handle
high-resolution RS imagery with diverse scale changes across categories and
many small objects, region-level reasoning is necessary alongside holistic
scene interpretation. Furthermore, the lack of domain-specific multimodal
instruction following data as well as strong backbone models for RS make it
hard for the models to align their behavior with user queries. To address these
limitations, we propose GeoChat - the first versatile remote sensing VLM that
offers multitask conversational capabilities with high-resolution RS images.
Specifically, GeoChat can not only answer image-level queries but also accepts
region inputs to hold region-specific dialogue. Furthermore, it can visually
ground objects in its responses by referring to their spatial coordinates. To
address the lack of domain-specific datasets, we generate a novel RS multimodal
instruction-following dataset by extending image-text pairs from existing
diverse RS datasets. We establish a comprehensive benchmark for RS multitask
conversations and compare with a number of baseline methods. GeoChat
demonstrates robust zero-shot performance on various RS tasks, e.g., image and
region captioning, visual question answering, scene classification, visually
grounded conversations and referring detection. Our code is available at
https://github.com/mbzuai-oryx/geochat.