This scarcity of labeled data often leads existing learning-based methods to overfit, resulting in models that struggle to recognize diverse land-use categories. To bypass these limitations, this paper for the first time advocates knowledge graph to leverage indirect supervision from related tasks for zero-shot land-use mapping. Toward this goal, this paper introduces a multi-granularity knowledge…

With the increasing resolution of remote sensing imagery (RSI), large-size RSI has emerged as a vital data source for high-precision vector mapping of geographic objects. Existing methods are typically constrained to processing small image patches, which often leads to the loss of contextual information and produces fragmented vector outputs. To address these, this paper introduces…