Abstract
Cross-resolution land cover mapping aims to produce high-resolution semantic predictions from coarse or low-resolution supervision, yet the severe resolution mismatch makes effective learning highly challenging. Existing weakly supervised approaches often struggle to align fine-grained spatial structures with coarse labels, leading to noisy supervision and degraded mapping accuracy. To tackle this problem, we propose DDTM, a dual-branch weakly supervised framework that explicitly decouples local semantic refinement from global contextual reasoning. Specifically, DDTM introduces a diffusion-based branch to progressively refine fine-scale local semantics under coarse supervision, while a transformer-based branch enforces long-range contextual consistency across large spatial extents. In addition, we design a pseudo-label confidence evaluation module to mitigate noise induced by cross-resolution inconsistencies and to selectively exploit reliable supervisory signals. Extensive experiments demonstrate that DDTM establishes a new state-of-the-art on the Chesapeake Bay benchmark, achieving 66.52\% mIoU and substantially outperforming prior weakly supervised methods. The code is available at this https URL.
Abstract (translated)
跨分辨率地表覆盖制图旨在从粗略或低分辨率监督中生成高分辨率的语义预测，但由于严重的分辨率不匹配，有效的学习变得极具挑战性。现有的弱监督方法往往难以将细粒度的空间结构与粗糙标签对齐，导致嘈杂的监督信息和映射精度下降。为了解决这个问题，我们提出了DDTM，这是一种双分支弱监督框架，明确地解耦了局部语义细化和全局上下文推理。 具体而言，DDTM引入了一个基于扩散的方法分支，在粗略监督下逐步精炼细尺度的局部语义，而一个基于变压器的方法分支则强制在整个大空间范围内保持长期的上下文一致性。此外，我们设计了一种伪标签置信度评估模块来减轻由跨分辨率不一致引起的噪声，并选择性地利用可靠的监督信号。 广泛的实验表明，DDTM在切萨皮克湾基准测试上建立了新的最先进的性能，达到了66.52%的mIoU（平均交并比），远远优于以前的弱监督方法。代码可在提供的链接获取。
URL
https://arxiv.org/abs/2512.19990
