Enhancing Individual Tree Detection and Species Classification in an Urban Forest with Semi-Supervised Deep Learning Models
Type of content
Conference Contributions - Other
UC permalink
Publisher's DOI/URI
Thesis discipline
Degree name
Publisher
Journal Title
Journal ISSN
Volume Title
Language
Date
2024
Authors
morgenroth, justin
Zhao H
Pearse G
Schindler J
Abstract
Description
Citation
Morgenroth 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.
Keywords
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300707 - Forestry management and environment
41 - Environmental sciences::4104 - Environmental management::410402 - Environmental assessment and monitoring
46 - Information and computing sciences::4611 - Machine learning::461103 - Deep learning
33 - Built environment and design::3304 - Urban and regional planning::330412 - Urban informatics
46 - Information and computing sciences::4605 - Data management and data science::460599 - Data management and data science not elsewhere classified
41 - Environmental sciences::4104 - Environmental management::410402 - Environmental assessment and monitoring
46 - Information and computing sciences::4611 - Machine learning::461103 - Deep learning
33 - Built environment and design::3304 - Urban and regional planning::330412 - Urban informatics
46 - Information and computing sciences::4605 - Data management and data science::460599 - Data management and data science not elsewhere classified
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