FLAME2-DT

FLAME2-DT (Forest Fire Detection Dataset with Dual-modality Labels) is a comprehensive multi-modal dataset specifically designed for UAV-based forest fire detection research. The dataset consists of 1,280 paired RGB-thermal infrared images captured by a Mavic 2 Enterprise Advanced UAV system, with high-resolution (640×512) and precise pixel-level annotations for both fire and smoke regions. This dataset addresses critical challenges in forest fire detection by providing paired multi-modal data that captures the complementary characteristics of visible light and thermal imaging. The RGB images contain 2,496 fire bounding boxes and 4,404 smoke bounding boxes, while the thermal infrared images include 27,117 fire bounding boxes. Statistical analysis reveals distinct scale and distribution patterns: approximately 80% of fire regions occupy less than 5% of the image area with discrete distribution, while over 60% of smoke regions cover more than 12% with continuous patterns. FLAME2-DT is organized into five specialized packages to facilitate different research scenarios: original dataset, RGB-specific, thermal IR-specific, RGB with dual-modality labels, and complete fusion packages. The dataset is split into training (80%) and validation (20%) sets, providing a standardized benchmark for evaluating multi-modal forest fire detection algorithms. This dataset contributes to the advancement of forest fire detection research by: 1. Providing precisely registered multi-modal image pairs 2. Offering comprehensive pixel-level annotations verified through multi-expert cross-validation 3. Supporting the development of lightweight, real-time detection systems for UAV applications 4. Enabling comparative analysis of single-modal and multi-modal detection approaches