{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:44:47Z","timestamp":1762508687747,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"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":["31727901"],"award-info":[{"award-number":["31727901"]}],"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>Weather radar data can capture large-scale bird migration information, helping solve a series of migratory ecological problems. However, extracting and identifying bird information from weather radar data remains one of the challenges of radar aeroecology. In recent years, deep learning was applied to the field of radar data processing and proved to be an effective strategy. This paper describes a deep learning method for extracting biological target echoes from weather radar images. This model uses a two-stream CNN (Atrous-Gated CNN) architecture to generate fine-scale predictions by combining the key modules such as squeeze-and-excitation (SE), and atrous spatial pyramid pooling (ASPP). The SE block can enhance the attention on the feature map, while ASPP block can expand the receptive field, helping the network understand the global shape information. The experiments show that in the typical historical data of China next generation weather radar (CINRAD), the precision of the network in identifying biological targets reaches up to 99.6%. Our network can cope with complex weather conditions, realizing long-term and automated monitoring of weather radar data to extract biological target information and provide feasible technical support for bird migration research.<\/jats:p>","DOI":"10.3390\/rs13244998","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T21:46:58Z","timestamp":1639086418000},"page":"4998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Animal Migration Patterns Extraction Based on Atrous-Gated CNN Deep Learning Model"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2617-6904","authenticated-orcid":false,"given":"Shuaihang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Cheng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China"}]},{"given":"Kai","family":"Cui","sequence":"additional","affiliation":[{"name":"Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China"},{"name":"School of Computer Sciences, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3510-7356","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0190-8534","authenticated-orcid":false,"given":"Huafeng","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Key Laboratory of Electronic and Information Technology in Satellite Navigation, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China"}]},{"given":"Dongli","family":"Wu","sequence":"additional","affiliation":[{"name":"The Meteorological Observation Center of China Meteorological Administration, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"ref_1","first-page":"624","article-title":"The natural link between Europe and Africa\u20132.1 billion birds on migration","volume":"118","author":"Hahn","year":"2009","journal-title":"Oikos"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1038\/s41559-018-0666-4","article-title":"Seasonal abundance and survival of North America\u2019s migratory avifauna determined by weather radar","volume":"2","author":"Dokter","year":"2018","journal-title":"Nat. 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