UC Santa Cruz

In order to harness the potential of deep learning and computer object recogni-tion in practical environments, a substantial collection of interesting features is essential. However, the creation of labeled image datasets is a significant challenge, hindering the broader adoption and autonomy by farmers, land managers, and ecologists. To address this challenge, we present a data collection, annotation, and pro- cessing pipeline utilizing Unmanned Aerial Vehicle (UAV) based optical sensing. Bird- sEye empowers non-deep learning experts to train, maintain, and deploy sophisticated computer vision methods on their own local land environments by substituting the need for aerial image feature identification with terrain based observation by subject matter experts. These annotated ground observations are then used to identify relevant image sections within UAV captured imagery. By facilitating the rapid generation of labeled datasets, our approach can iden- tify and characterize a diverse array of land and plant conditions. This has significant applications in areas such as disease monitoring, vegetation and pest identification, and precision treatments.