Exploring Different Levels of Supervision for Detecting and Localizing
  Solar Panels on Remote Sensing Imagery
Maarten Burger Rob Wijnhoven Shaodi You
This study investigates object presence detection and localization in remote
sensing imagery, focusing on solar panel recognition. We explore different
levels of supervision, evaluating three models: a fully supervised object
detector, a weakly supervised image classifier with CAM-based localization, and
a minimally supervised anomaly detector. The classifier excels in binary
presence detection (0.79 F1-score), while the object detector (0.72) offers
precise localization. The anomaly detector requires more data for viable
performance. Fusion of model results shows potential accuracy gains. CAM
impacts localization modestly, with GradCAM, GradCAM++, and HiResCAM yielding
superior results. Notably, the classifier remains robust with less data, in
contrast to the object detector.