{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:04:13Z","timestamp":1760234653766,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC1501501"],"award-info":[{"award-number":["2017YFC1501501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41775009","41775007","41905004"],"award-info":[{"award-number":["41775009","41775007","41905004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lightning location provides an important means for the study of lightning discharge process and thunderstorms activity. The fine positioning capability of total lightning based on low-frequency signals has been improved in many aspects, but most of them are based on post waveform processing, and the positioning speed is slow. In this study, artificial intelligence technology is introduced for the first time to lightning positioning, based on low-frequency electric-field detection array (LFEDA). A new method based on deep-learning encoding features matching is also proposed, which provides a means for fast and fine location of total lightning. Compared to other LFEDA positioning methods, the new method greatly improves the matching efficiency, up to more than 50%, thereby considerably improving the positioning speed. Moreover, the new algorithm has greater fine-positioning and anti-interference abilities, and maintains high-quality positioning under low signal-to-noise ratio conditions. The positioning efficiency for return strokes of triggered lightning was 99.17%, and the standard deviation of the positioning accuracy in the X and Y directions was approximately 70 m.<\/jats:p>","DOI":"10.3390\/rs13112212","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"2212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Fast and Fine Location of Total Lightning from Low Frequency Signals Based on Deep-Learning Encoding Features"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5878-6902","authenticated-orcid":false,"given":"Jingxuan","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100089, China"},{"name":"College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5973-8964","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yadan","family":"Tan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100089, China"},{"name":"College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zefang","family":"Chen","sequence":"additional","affiliation":[{"name":"Meteorological Observation Center of CMA, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7573-424X","authenticated-orcid":false,"given":"Yijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1007\/s00376-019-8195-x","article-title":"A review of atmospheric electricity research in China from 2011 to 2018","volume":"36","author":"Qie","year":"2019","journal-title":"Adv. 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