Radroachhd.

The dataset is split into:

RadroachHD: A High-Fidelity Computer Vision Dataset and Benchmark for Post-Apocalyptic Environmental Navigation radroachhd.

The deployment of autonomous agents in disaster zones—such as nuclear power plant melt-downs, chemical spill sites, or war-torn urban centers—requires computer vision models capable of interpreting high-noise, low-contrast, and chaotic scenes. Current perception stacks, trained on pristine driving datasets, suffer from catastrophic domain shift when encountering the visual chaos of a post-apocalyptic environment. The dataset is split into: RadroachHD: A High-Fidelity

Foggy Cityscapes and RainCOCO introduced weather corruptions to test robustness. Synthetic datasets generated via simulators like CARLA or AirSim allow for data generation. RadroachHD builds upon this by utilizing a custom pipeline in Unreal Engine 5 to simulate not just weather, but the specific visual artifacts of decay: rust layers, volumetric dust, and "glowing" radiological artifacts. Synthetic datasets generated via simulators like CARLA or

The field of autonomous robotic navigation has seen tremendous progress in structured, urban environments. However, navigation in unstructured, hazardous, or "post-apocalyptic" scenarios remains a significant challenge due to the scarcity of relevant training data. Standard datasets (e.g., KITTI, Cityscapes) focus on clean, well-lit, and structured geometries, failing to generalize to environments characterized by decay, rubble, and biological contamination. To address this gap, we introduce RadroachHD , a high-definition dataset designed for robust perception in degraded environments. RadroachHD comprises over 50,000 high-resolution frames featuring synthetic assets of radiological hazards, biological contaminants, and structural decay. We provide pixel-perfect semantic segmentation annotations and depth maps. We evaluate state-of-the-art (SOTA) object detection and segmentation models on RadroachHD, demonstrating the dataset's difficulty and its necessity for training resilient AI systems intended for nuclear decommissioning, disaster relief, and search-and-rescue operations.

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