{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:36:25Z","timestamp":1760402185157,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672064"],"award-info":[{"award-number":["61672064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Laboratory of Advanced Information Networks","award":["0040000546319003 and PXM2019_014204_500029"],"award-info":[{"award-number":["0040000546319003 and PXM2019_014204_500029"]}]},{"name":"Basic Research Program of Qinghai Province","award":["2020-ZJ-709"],"award-info":[{"award-number":["2020-ZJ-709"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.<\/jats:p>","DOI":"10.3390\/rs14020388","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:45:21Z","timestamp":1642365921000},"page":"388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1607-2725","authenticated-orcid":false,"given":"Zhihao","family":"Wei","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100021, China"},{"name":"School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7620-2221","authenticated-orcid":false,"given":"Kebin","family":"Jia","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100021, China"},{"name":"Beijing Laboratory of Advanced Information Network, Beijing 100021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100021, China"},{"name":"Beijing Laboratory of Advanced Information Network, Beijing 100021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Physics and Electronic Information Engineering, Qinghai Nationalities University, Xining 810007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"Twenty First Century Aerospace Technology Co., Ltd., Beijing 100096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilian","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Physics and Electronic Information Engineering, Qinghai Nationalities University, Xining 810007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"220","DOI":"10.3390\/ijgi2010220","article-title":"Mapping urban tree species using very high resolution satellite imagery: Comparing pixel-based and objectbased approaches","volume":"2","author":"Agarwal","year":"2013","journal-title":"ISPRS Int. 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