  The field of building detection from remote sensing images has made
significant progress, but faces challenges in achieving high-accuracy detection
due to the diversity in building appearances and the complexity of vast scenes.
To address these challenges, we propose a novel approach called
Context-Enhanced Detector (CEDet). Our approach utilizes a three-stage cascade
structure to enhance the extraction of contextual information and improve
building detection accuracy. Specifically, we introduce two modules: the
Semantic Guided Contextual Mining (SGCM) module, which aggregates multi-scale
contexts and incorporates an attention mechanism to capture long-range
interactions, and the Instance Context Mining Module (ICMM), which captures
instance-level relationship context by constructing a spatial relationship
graph and aggregating instance features. Additionally, we introduce a semantic
segmentation loss based on pseudo-masks to guide contextual information
extraction. Our method achieves state-of-the-art performance on three building
detection benchmarks, including CNBuilding-9P, CNBuilding-23P, and SpaceNet.
