We introduce GeoDiT, a diffusion transformer designed for text-to-satellite image generation with point-based control. Existing controlled satellite image generative models often require pixel-level maps that are time-consuming to acquire, yet semantically limited. To address this limitation, we introduce a novel point-based conditioning framework that controls the generation process through the spatial location of the points and the textual description associated with each point, providing semantically rich control signals. This approach enables flexible, annotation-friendly, and computationally simple inference for satellite image generation. To this end, we introduce an adaptive local attention mechanism that effectively regularizes the attention scores based on the input point queries. We systematically evaluate various domain-specific design choices for training GeoDiT, including the selection of satellite image representation for alignment and geolocation representation for conditioning. Our experiments demonstrate that GeoDiT achieves impressive generation performance, surpassing the state-of-the-art remote sensing generative models.

翻译：我们提出GeoDiT，一种专为基于点控制的文本到卫星图像生成而设计的扩散Transformer模型。现有的可控卫星图像生成模型通常需要像素级标注图，这类标注获取耗时且语义信息有限。为克服这一限制，我们引入了一种新颖的基于点的条件控制框架，通过点的空间位置及每个点关联的文本描述来控制生成过程，从而提供语义丰富的控制信号。该方法为卫星图像生成实现了灵活、标注友好且计算简单的推理过程。为此，我们提出了一种自适应局部注意力机制，该机制能基于输入的点查询有效规范化注意力分数。我们系统评估了训练GeoDiT时涉及的多领域特定设计选择，包括用于对齐的卫星图像表示方法及用于条件控制的地理位置表示方法。实验表明，GeoDiT在生成性能上表现卓越，超越了当前最先进的遥感生成模型。