{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:38:12Z","timestamp":1773898692426,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T00:00:00Z","timestamp":1679616000000},"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":["42071343"],"award-info":[{"award-number":["42071343"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42204031"],"award-info":[{"award-number":["42204031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020-KYYWF-0690"],"award-info":[{"award-number":["2020-KYYWF-0690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Scientific Research Expenses of Heilongjiang Provincial Universities, China","award":["42071343"],"award-info":[{"award-number":["42071343"]}]},{"name":"Basic Scientific Research Expenses of Heilongjiang Provincial Universities, China","award":["42204031"],"award-info":[{"award-number":["42204031"]}]},{"name":"Basic Scientific Research Expenses of Heilongjiang Provincial Universities, China","award":["2020-KYYWF-0690"],"award-info":[{"award-number":["2020-KYYWF-0690"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The lack of large-scale, multi-scene, and multi-type pavement distress training data reduces the generalization ability of deep learning models in complex scenes, and limits the development of pavement distress extraction algorithms. Thus, we built the first large-scale dichotomous image segmentation (DIS) dataset for multi-type pavement distress segmentation, called ISTD-PDS7, aimed to segment highly accurate pavement distress types from natural charge-coupled device (CCD) images. The new dataset covers seven types of pavement distress in nine types of scenarios, along with negative samples with texture similarity noise. The final dataset contains 18,527 images, which is many more than the previously released benchmarks. All the images are annotated with fine-grained labels. In addition, we conducted a large benchmark test, evaluating seven state-of-the-art segmentation models, providing a detailed discussion of the factors that influence segmentation performance, and making cross-dataset evaluations for the best-performing model. Finally, we investigated the effectiveness of negative samples in reducing false positive prediction in complex scenes and developed two potential data augmentation methods for improving the segmentation accuracy. We hope that these efforts will create promising developments for both academics and the industry.<\/jats:p>","DOI":"10.3390\/rs15071750","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T06:34:07Z","timestamp":1679639647000},"page":"1750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["ISTD-PDS7: A Benchmark Dataset for Multi-Type Pavement Distress Segmentation from CCD Images in Complex Scenarios"],"prefix":"10.3390","volume":"15","author":[{"given":"Weidong","family":"Song","sequence":"first","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}]},{"given":"Zaiyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"College of Mining Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China"}]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}]},{"given":"Guohui","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Resources and Civil Engineering, Liaoning Institute of Science and Technology, Benxi 117004, China"}]},{"given":"Hongbo","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}]},{"given":"Jinhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24510","DOI":"10.1109\/TITS.2022.3204334","article-title":"A new method for automated monitoring of road pavement aging conditions based on recurrent neural network","volume":"23","author":"Xiao","year":"2022","journal-title":"IEEE Trans. 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