  Imagery collected from outdoor visual environments is often degraded due to
the presence of dense smoke or haze. A key challenge for research in scene
understanding in these degraded visual environments (DVE) is the lack of
representative benchmark datasets. These datasets are required to evaluate
state-of-the-art vision algorithms (e.g., detection and tracking) in degraded
settings. In this paper, we address some of these limitations by introducing
the first realistic hazy image benchmark, from both aerial and ground view,
with paired haze-free images, and in-situ haze density measurements. This
dataset was produced in a controlled environment with professional smoke
generating machines that covered the entire scene, and consists of images
captured from the perspective of both an unmanned aerial vehicle (UAV) and an
unmanned ground vehicle (UGV). We also evaluate a set of representative
state-of-the-art dehazing approaches as well as object detectors on the
dataset. The full dataset presented in this paper, including the ground truth
object classification bounding boxes and haze density measurements, is provided
for the community to evaluate their algorithms at:
https://a2i2-archangel.vision. A subset of this dataset has been used for the
``Object Detection in Haze'' Track of CVPR UG2 2022 challenge at
http://cvpr2022.ug2challenge.org/track1.html.
