{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:24:28Z","timestamp":1771698268880,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"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":["2017YFC1500902"],"award-info":[{"award-number":["2017YFC1500902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Science and Technology Program of Hainan province","award":["ZDKJ2019006"],"award-info":[{"award-number":["ZDKJ2019006"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research (STEP)","award":["2019QZKK0806"],"award-info":[{"award-number":["2019QZKK0806"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.<\/jats:p>","DOI":"10.3390\/ijgi10030168","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T11:38:58Z","timestamp":1615808338000},"page":"168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN"],"prefix":"10.3390","volume":"10","author":[{"given":"Peng","family":"Liu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China"}]},{"given":"Yongming","family":"Wei","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"}]},{"given":"Qinjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Jingjing","family":"Xie","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9095-243X","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"}]},{"given":"Zhichao","family":"Li","sequence":"additional","affiliation":[{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Hongying","family":"Zhou","sequence":"additional","affiliation":[{"name":"Research Institute of Petroleum Exploration &amp; Development, PetroChina, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Alizadeh, M., Chen, W., Mohammadi, A., Ahmad, B., Panahi, M., and Hong, H. 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