Automated Ground Truth Annotation for Forest and Non-Forest Classification in Satellite Remote Sensing Images

Accurate ground truth annotation plays a vital role in training and evaluating deep learning models for forest and non-forest classification tasks. This paper introduces a robust algorithm designed to automate the annotation process, specifically targeting the identification of regions of interest;...

詳細記述

書誌詳細
出版年:2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings
第一著者: 2-s2.0-85176549266
フォーマット: Conference paper
言語:English
出版事項: Institute of Electrical and Electronics Engineers Inc. 2023
オンライン・アクセス:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176549266&doi=10.1109%2fAiDAS60501.2023.10284683&partnerID=40&md5=5e0e1a03f16304dec14a8f8c9fa9cf46