The Massachusetts dataset, created using vector data from the OpenStreetMap (OSM) platform, was observed to contain various types of labeling errors. Since the OSM data are continuously updated by volunteer contributors, manual data entry may bring the risk of inconsistency and inaccuracy [20]. Also, the resolution of the images exacerbates labeling errors by contributing to problems such as blurred building boundaries [21].
FLAME 3 is the third dataset in the FLAME series of aerial UAV-collected side-by-side multi-spectral wildlands fire imagery (see FLAME 1 and FLAME 2). This set contains a single-burn subset of the larger FLAME 3 dataset focusing specifically on Computer Vision tasks such as fire detection and segmentation.
An understanding of local walking context plays an important role in the analysis of gait in humans and in the high level control systems of robotic prostheses. Laboratory analysis on its own can constrain the ability of researchers to properly assess clinical gait in patients and robotic prostheses to function well in many contexts, therefore study in diverse walking environments is warranted. A ground-truth understanding of the walking terrain is traditionally identified from simple visual data.
