  Natural scene analysis and remote sensing imagery offer immense potential for
advancements in large-scale language-guided context-aware data utilization.
This potential is particularly significant for enhancing performance in
downstream tasks such as object detection and segmentation with designed
language prompting. In light of this, we introduce the CPSeg, Chain-of-Thought
Language Prompting for Finer-grained Semantic Segmentation), an innovative
framework designed to augment image segmentation performance by integrating a
novel "Chain-of-Thought" process that harnesses textual information associated
with images. This groundbreaking approach has been applied to a flood disaster
scenario. CPSeg encodes prompt texts derived from various sentences to
formulate a coherent chain-of-thought. We propose a new vision-language
dataset, FloodPrompt, which includes images, semantic masks, and corresponding
text information. This not only strengthens the semantic understanding of the
scenario but also aids in the key task of semantic segmentation through an
interplay of pixel and text matching maps. Our qualitative and quantitative
analyses validate the effectiveness of CPSeg.
