Not in Isolation: Learning from Scene Dependencies for Open-Vocabulary Remote Sensing Segmentation

Although existing open-vocabulary semantic segmentation (OVSS) methods have made significant progress, segmenting complex remote sensing images remains challenging. Regions of the same semantic class often varies widely in scale and shape, while different classes may exhibit similar local appearances. These ambiguities can be reduced by using spatial relationships between regions and their surroundings as complementary cues. However, such scene dependencies remain insufficiently modeled in OVSS. Therefore, we propose SCENE, a scene dependency learning framework that incorporates scene dependencies into open-vocabulary visual representations through content-adaptive multi-granularity perception at the location level and spatial relation modeling at the region level. Specifically, Content-Adaptive Spatial Perception adaptively aggregates information across multiple spatial granularities for each position based on image content; Scene-Relational Structure Learning treats category-agnostic regions as entities, propagates region information through multiple types of spatial relations, and maps the resulting relational representations into pixel space to generate dense relational context; Structure-Guided Fusion Decoder further uses relational context to guide the refinement of spatial perception representations and dense visual representations and performs progressive decoding with multi-level features to produce segmentation predictions. Extensive experiments on four remote sensing benchmarks demonstrate that SCENE effectively improves OVSS performance and generalization across datasets. Code will be available at https://anonymous.4open.science/r/Anonymous-DD21.