Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping.

Area of Science:

• Avian ecology
• Computer vision
• Artificial intelligence

Background:

• Accurate bird population data is crucial for understanding movement trends but manual counting is challenging.
• Developing consistent and efficient methods for bird population assessment is a significant ecological problem.

Purpose of the Study:

• To leverage deep learning for automated bird counting from images.
• To compare the accuracy and efficiency of AI-based bird counting against manual methods.
• To create spatial distribution and diversity maps of bird species using GIS technology.

Main Methods:

• Utilized a dataset of on-ground bird photographs.
• Employed state-of-the-art generic object-detection algorithms for bird identification.
• Applied Geographic Information System (GIS) technology for spatial mapping.

Main Results:

• Computer-aided bird counting demonstrated superior accuracy and speed compared to manual counting.
• Successfully generated spatial bird order distribution and species diversity maps for Turkey.
• Validated the effectiveness of deep learning in bird monitoring.

Conclusions:

• Deep learning offers a powerful tool to enhance bird monitoring and ecological studies.
• Automated bird counting can increase the participation and efficiency of citizen scientists in large-scale surveys.
• Image-based AI methods provide a scalable solution for tracking bird population dynamics.