From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
It addresses the problem of integrating visual and textual data for remote sensing tasks, such as environmental monitoring and disaster response, but is incremental as it reviews existing methods without presenting new results.
This review examines the development and application of multi-modal language models (MLLMs) in remote sensing to interpret and describe satellite imagery using natural language, covering technical aspects, challenges, and applications like scene description and object detection.
Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and application of multi-modal language models (MLLMs) in remote sensing, focusing on their ability to interpret and describe satellite imagery using natural language. We cover the technical underpinnings of MLLMs, including dual-encoder architectures, Transformer models, self-supervised and contrastive learning, and cross-modal integration. The unique challenges of remote sensing data--varying spatial resolutions, spectral richness, and temporal changes--are analyzed for their impact on MLLM performance. Key applications such as scene description, object detection, change detection, text-to-image retrieval, image-to-text generation, and visual question answering are discussed to demonstrate their relevance in environmental monitoring, urban planning, and disaster response. We review significant datasets and resources supporting the training and evaluation of these models. Challenges related to computational demands, scalability, data quality, and domain adaptation are highlighted. We conclude by proposing future research directions and technological advancements to further enhance MLLM utility in remote sensing.