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With the continuous advancement of remote sensing observation technology, wide-area observation, and high-resolution imaging make remote sensing images contain a large number of dense tiny objects. The detection of dense tiny objects is a very challenging task since these objects are with very low resolution and might stick together. Existing work lacks further exploration of the contextual scene information and inherent characteristics of dense tiny objects, which are crucial for performance improvement of dense tiny object detection (DTOD). In this work, we propose a novel scene contextualized detection network (SCDNet) by decoupling scene contextual information through a dedicated scene classification subnetwork (SCN), thereby enabling an enhanced exploration of the relationship between tiny objects and their surrounding environments. In particular, we design a lightweight scene context guided fusion module (SCGM) in SCDNet to incorporate scene context information around dense tiny objects more effectively. Moreover, we further develop the scene context guided foreground enhancement module (FEM) to suppress the background information while enhancing the foreground information based on the scene information. In addition, this research field still lacks a large-scale benchmark dataset with dense tiny objects, which is crucial for the training and comprehensive evaluation of detection methods. To this end, we construct a large-scale dataset for DTOD. It contains 11600 images with 1019800 instances, the average absolute size of objects is smaller than 13 pixels, and each image contains 88 objects on average. Extensive experiments are conducted on the proposed dataset, and the results demonstrate the superiority and effectiveness of SCDNet compared to existing methods. The dataset and evaluation code are available at https://github.com/mmic-lcl .