Accurate understanding of the interactions between birds and solar energy infrastructure is important for continued deployment of utility-scale solar energy facilities. Current monitoring methods for avian-solar interactions that rely on periodic surveys for bird carcasses are costly, infrequent, spatially constrained, and subject to errors related to searcher efficiency and carcass predation. These methods account for collision but do not consider other avian-solar interactions, such as perching and fly-through, that could provide information on the occurrence and intensity of bird attraction to solar energy facilities.
In collaboration with Argonne’s Strategic Security Sciences and Mathematics and Computer Science divisions, EVS is developing a technology for automated detection of avian-solar interactions (e.g., perching, fly-through, and collisions). The system incorporates a machine/deep learning (ML/DL)-computer vision approach and high-definition edge-computing cameras. The automated avian monitoring technology will improve researchers’ ability to collect a large volume of avian-solar interaction data to better understand potential avian impacts associated with solar energy facilities. The use of an automated method is the most timely and cost-effective option for collecting a large volume of accurate data on avian-solar interactions across large areas.
We are developing ML/DL models by employing three stages of modeling objectives:
- Detecting moving objects in video (Stage 1),
- Recognizing birds among the moving objects (Stage 2), and
- Classifying bird interactions with solar energy infrastructure (Stage 3).
Iterative Model Development
We accomplish each stage of the modeling objectives by iterating over two phases:
- Training and evaluation of the ML/DL model using existing datasets to develop a deployable model and
- Deployment of the trained model and evaluation of predictions to validate model performance.
We developed the avian monitoring technology in partnership with Boulder AI and various industry partners and with expert guidance from the Cornell Lab of Ornithology; Northwestern University; University of Chicago; University of California, Los Angeles; and regulatory and conservation stakeholders.
Related Research Areas
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