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About Remotesensing 12 02026
This document discusses using deep learning to count albatrosses from satellite imagery. It conducted both manual counts by 6 observers and automated counts using a convolutional neural network (CNN) on images of 4 islands. It found that (1) counts varied significantly between observers on different islands, (2) the small dataset limited the CNN's ability to generalize, and (3) the choice of ground truth labels impacted the CNN's performance evaluation. The results showed the CNN detected albatrosses as accurately as humans on 2 islands, but misclassified some due to noise, cloud cover and habitat not in its training data. It stresses the importance of considering these factors when data is limited and observer confidence varies.
Detailed Information
| Author: | ['RNG'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 18 |
| Language: | English |
| Format: | |
| Price: | FREE |
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