Enhancing Individual Tree Detection and Species Classification in an Urban Forest with Semi-Supervised Deep Learning Models

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dc.contributor.authormorgenroth, justin
dc.contributor.authorZhao H
dc.contributor.authorPearse G
dc.contributor.authorSchindler J
dc.date.accessioned2025-01-30T21:01:11Z
dc.date.available2025-01-30T21:01:11Z
dc.date.issued2024
dc.identifier.citationMorgenroth J, Zhao H, Pearse G, Schindler J (2024). Enhancing Individual Tree Detection and Species classification in an Urban Forest with Semi-Supervised Deep learning models. Rotorua, New Zealand: ForestSAT. 09/09/2024-13/09/2024.
dc.identifier.urihttps://hdl.handle.net/10092/107657
dc.rightsAll rights reserved unless otherwise stated
dc.rights.urihttp://hdl.handle.net/10092/17651
dc.subject.anzsrc30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300707 - Forestry management and environment
dc.subject.anzsrc41 - Environmental sciences::4104 - Environmental management::410402 - Environmental assessment and monitoring
dc.subject.anzsrc46 - Information and computing sciences::4611 - Machine learning::461103 - Deep learning
dc.subject.anzsrc33 - Built environment and design::3304 - Urban and regional planning::330412 - Urban informatics
dc.subject.anzsrc46 - Information and computing sciences::4605 - Data management and data science::460599 - Data management and data science not elsewhere classified
dc.titleEnhancing Individual Tree Detection and Species Classification in an Urban Forest with Semi-Supervised Deep Learning Models
dc.typeConference Contributions - Other
uc.collegeFaculty of Engineering
uc.departmentSchool of Forestry
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