Datasets

Drone-based wildfire detection methods enable high-precision, real-time fire monitoring that is not offered by traditional wildfire mitigation techniques that can help significantly mitigate the overall damages caused by wildfires due to early detection and response. However, the development of these systems has been limited by the lack of high quality, well-annotated datasets necessary for the development of detection, monitoring, and modelling models. To combat this, we have collected and published the FLAME datasets, FLAME 1 and FLAME 2, to help enhance the development of robust wildfire monitoring systems. Both datasets include high-quality, well-annotated drone aerial imagery of prescribed burns in Northern Arizona with additional supplementary data. FLAME 3 is the third dataset in the FLAME series of aerial UAV-collected side-by-side multi-spectral wildlands fire imagery.

These datasets are publicly available via IEEE DataPort or Kaggle and are linked below.

In our ever-expanding world of advanced satellite and communications systems, there's a growing challenge for passive radiometer sensors used in the Earth observation like 5G. These passive sensors are challenged by risks from radio frequency interference (RFI) caused by anthropogenic signals. To address this, we urgently need effective methods to quantify the impacts of 5G on Earth observing radiometers. Unfortunately, the lack of substantial datasets in the radio frequency (RF) domain, especially for active/passive coexistence, hinders progress. Our study introduces a controlled testbed featuring a calibrated L-band radiometer and a 5G wireless communication system. In a controlled chamber, this unique setup allows us to observe and quantify transmission effects across different frequency bands. By creating a comprehensive dataset, we aim to standardize and benchmark both wireless communication and passive sensing. With the ability to analyze raw measurements, our testbed facilitates RFI detection and mitigation, fostering the coexistence of wireless communication and passive sensing technologies while establishing crucial standards.

The dataset is publicly available via IEEE DataPort, GitHub, and OneDrive and are linked below. Further details are included in the dataset descriptions and article.