GeoDiT：面向地理空间理解的基于扩散的视觉语言模型
GeoDiT: A Diffusion-based Vision-Language Model for Geospatial Understanding
把地理空间视觉语言生成从自回归序列解码换成扩散式并行精修，对做遥感 captioning、visual grounding 和多目标检测的人提供了一个可直接替换解码范式的思路，尤其适合需要结构化、对象级输出的场景。
原文依据 we introduce GeoDiT, the first diffusion-based vision-language model tailored for the geospatial domain
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自回归模型与地理空间理解固有的并行特性在结构上并不匹配，它们将一种僵化的序列式叙述强加于场景之上，从根本上阻碍了结构化且连贯的输出的生成。我们通过将地理空间生成重新表述为并行精修过程来挑战这一范式，从而实现整体性的、由粗到细的合成，同时解析所有语义元素。为将这一思路付诸实践，我们提出了 GeoDiT，这是首个面向地理空间领域定制的基于扩散的视觉语言模型。大量实验表明，GeoDiT 在需要结构化、以对象为中心输出的基准上确立了新的最优性能。它在图像描述、视觉定位和多目标检测上取得了显著提升，而这些正是自回归模型表现欠佳的任务。我们的工作验证了：使生成过程与数据的内在结构相对齐，是在复杂地理空间分析中释放更优性能的关键。
英文原文摘要 1024 字符
Autoregressive models are structurally misaligned with the inherently parallel nature of geospatial understanding, forcing a rigid sequential narrative onto scenes and fundamentally hindering the generation of structured and coherent outputs. We challenge this paradigm by reframing geospatial generation as a parallel refinement process, enabling a holistic, coarse-to-fine synthesis that resolves all semantic elements simultaneously. To operationalize this, we introduce GeoDiT, the first diffusion-based vision-language model tailored for the geospatial domain. Extensive experiments demonstrate that GeoDiT establishes a new state-of-the-art on benchmarks requiring structured, object-centric outputs. It achieves significant gains in image captioning, visual grounding, and multi-object detection, precisely the tasks where autoregressive models falter. Our work validates that aligning the generative process with the data's intrinsic structure is key to unlocking superior performance in complex geospatial analysis.