Segmenting dunes from remote sensing landforms images with deep vision models is promising by freeing geographers from manual visual interpretation tasks, making them more concentrated on the essential tasks in solving desertification challenges. However, geographers have reported that automated segmentation results may be not satisfactory though achieving high accuracy, implying there are potential gaps between pixel-level metrics and the utility in downstream geographic tasks. Therefore, pixel-wise metrics may be not proper in evaluating the deep dune segmentation in the geography domain, arising the necessity to develop domain-specific, human-centered measurements for deep dune segmentation. This paper first proposes a novel measurement based on geographers’ subjective judgments, which allows the evaluation of the alignment between deep dune segmentation models and geographical utility. We design an interactive framework integrating multiagent reinforcement learning (MARL) with geographers’ domain knowledge to improve models’ utility in the domain of geography. Our extensive experiments show that (1) our framework enables the interactive domain knowledge integration in the model-building process, and thus (2) the dune segmentation model better aligns with geographical utility, which ultimately improves the effectiveness of dune segmentation. We have deployed the framework with a number of geographers to support their various tasks including dune segmentation as a component. The results demonstrate our framework’s capabilities.