Exploring Different Levels of Supervision for Detecting and Localizing
Solar Panels on Remote Sensing Imagery
- URL: http://arxiv.org/abs/2309.10421v1
- Date: Tue, 19 Sep 2023 08:33:29 GMT
- Title: Exploring Different Levels of Supervision for Detecting and Localizing
Solar Panels on Remote Sensing Imagery
- Authors: Maarten Burger (1 and 2) and Rob Wijnhoven (1) and Shaodi You (2) ((1)
University of Amsterdam (UvA), (2) Spotr.ai)
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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