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Current contextual aggregation methods only aggregate contextual information within specific regions to improve feature representation, which may yield poorly robust contextual information. To address this problem, we propose a novel multi-level context refinement network (MLCRNet) that aggregates three levels of contextual information effectively and efficiently in an adaptive manner. First, we designed a local-level context aggregation module to capture local information around each pixel. Second, we integrate multiple levels of context, namely, local-level, image-level, and semantic-level, to aggregate contextual information from a comprehensive perspective dynamically. Third, we propose an efficient multi-level context transform (EMCT) module to address feature redundancy and to improve the efficiency of our multi-level contexts. Finally, based on the EMCT module and feature pyramid network (FPN) framework, we propose a multi-level context feature refinement (MLCR) module to enhance feature representation by leveraging multi-level contextual information. Extensive empirical evidence demonstrates that our MLCRNet achieves state-of-the-art performance on the ISPRS Potsdam and Vaihingen datasets.<\/jats:p>","DOI":"10.3390\/rs14061498","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1498","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MLCRNet: Multi-Level Context Refinement for Semantic Segmentation in Aerial Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9949-4367","authenticated-orcid":false,"given":"Zhifeng","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai 200062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3041-643X","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai 200062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai 200062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112400","DOI":"10.1016\/j.rse.2021.112400","article-title":"InSAR monitoring of creeping landslides in mountainous regions: A case study in Eldorado National Forest, California","volume":"258","author":"Kang","year":"2021","journal-title":"Remote Sens. 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