A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs)

“…Beyond object detection, the dataset is invaluable for considerable environmental monitoring activities. For instance, it facilitates wildlife monitoring by enabling the detection and tracking of animal movements, thereby supporting conservation efforts [ 2 ]. The UAVs-FFDB dataset also enhances land cover classification, aiding in the mapping and managing of natural resources [ 1 ].…”

“…Applying machine learning (ML), DL, and CV models to improve fire detection in forest environments holds promise for prospective research endeavors [ 1 ]. Access to comprehensive datasets is crucial for developing efficient AI approaches in this domain [ 2 ]. However, obtaining real-time fire datasets presents a significant challenge due to the complexities of creating suitable experimental conditions [ 3 ].…”

“…Existing literature indicates a scarcity of readily available real-time UAV datasets, primarily obtained from online repositories, still photos and processed using software tools such as Photoshop [ [5] , [6] , [7] ]. Early identification of forest fires is essential for pre-emptive measures and quick intervention [ 2 ]. Therefore, establishing a standardized real-time image dataset specifically designed for forest fire detection is imperative, enabling its integration into advanced AI architectures, including ML, DL, and CV methodologies.…”