  The revolutionary capabilities of large language models (LLMs) have paved the
way for multimodal large language models (MLLMs) and fostered diverse
applications across various specialized domains. In the remote sensing (RS)
field, however, the diverse geographical landscapes and varied objects in RS
imagery are not adequately considered in recent MLLM endeavors. To bridge this
gap, we construct a large-scale RS image-text dataset, LHRS-Align, and an
informative RS-specific instruction dataset, LHRS-Instruct, leveraging the
extensive volunteered geographic information (VGI) and globally available RS
images. Building on this foundation, we introduce LHRS-Bot, an MLLM tailored
for RS image understanding through a novel multi-level vision-language
alignment strategy and a curriculum learning method. Additionally, we introduce
LHRS-Bench, a benchmark for thoroughly evaluating MLLMs' abilities in RS image
understanding. Comprehensive experiments demonstrate that LHRS-Bot exhibits a
profound understanding of RS images and the ability to perform nuanced
reasoning within the RS domain.
