{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:27:17Z","timestamp":1772756837879,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Aeronautics and Space Administration (NASA)","award":["80NSSC20K1485, 80NSSC22K1164"],"award-info":[{"award-number":["80NSSC20K1485, 80NSSC22K1164"]}]},{"name":"NSF","award":["1838159, 1739191, 2147195"],"award-info":[{"award-number":["1838159, 1739191, 2147195"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1145\/3557915.3560937","type":"proceedings-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:11:25Z","timestamp":1669162285000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Clustering augmented self-supervised learning"],"prefix":"10.1145","author":[{"given":"Rahul","family":"Ghosh","sequence":"first","affiliation":[{"name":"University of Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Jia","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leikun","family":"Yin","sequence":"additional","affiliation":[{"name":"University of Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenxi","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenong","family":"Jin","sequence":"additional","affiliation":[{"name":"University of Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vipin","family":"Kumar","sequence":"additional","affiliation":[{"name":"University of Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","author":"Badrinarayanan Vijay","year":"2017","unstructured":"Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39, 12 (2017), 2481--2495."},{"key":"e_1_3_2_1_2_1","unstructured":"CDL 2021. USDA Cropland Data Layer. https:\/\/www.nass.usda.gov\/Research_and_Science\/Cropland\/SARS1a.php."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_4_1","volume-title":"Land cover mapping in limited labels scenario: A survey. arXiv preprint arXiv:2103.02429","author":"Ghosh Rahul","year":"2021","unstructured":"Rahul Ghosh, Xiaowei Jia, and Vipin Kumar. 2021. Land cover mapping in limited labels scenario: A survey. arXiv preprint arXiv:2103.02429 (2021)."},{"key":"e_1_3_2_1_5_1","volume-title":"Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 1399--1408","author":"Ghosh Rahul","year":"2021","unstructured":"Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Chenxi Lin, Zhenong Jin, and Vipin Kumar. 2021. Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 1399--1408."},{"key":"e_1_3_2_1_6_1","volume-title":"Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728","author":"Gidaris Spyros","year":"2018","unstructured":"Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018)."},{"key":"e_1_3_2_1_7_1","volume-title":"Deep learning to map concentrated animal feeding operations. Nature Sustainability","author":"Handan-Nader Cassandra","year":"2019","unstructured":"Cassandra Handan-Nader and Daniel E Ho. 2019. Deep learning to map concentrated animal feeding operations. Nature Sustainability (2019)."},{"key":"e_1_3_2_1_8_1","unstructured":"Yunfeng Hu et al. 2018. A deep convolution neural network method for land cover mapping: a case study of qinhuangdao China. Remote Sensing (2018)."},{"key":"e_1_3_2_1_9_1","volume-title":"What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614","author":"Huh Minyoung","year":"2016","unstructured":"Minyoung Huh, Pulkit Agrawal, and Alexei A Efros. 2016. What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614 (2016)."},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence.","author":"Neal","unstructured":"Neal Jean et al. 2019. Tile2vec: Unsupervised representation learning for spatially distributed data. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","author":"Xiaowei","unstructured":"Xiaowei Jia et al. 2017. Incremental dual-memory lstm in land cover prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Anuj Karpatne et al. 2016. Monitoring land-cover changes: A machine-learning perspective. IEEE Geoscience and Remote Sensing Magazine (2016).","DOI":"10.1109\/MGRS.2016.2528038"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_35"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.96"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639343"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553453"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_19_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.","journal-title":"Journal of machine learning research 9"},{"key":"e_1_3_2_1_20_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.29"},{"key":"e_1_3_2_1_22_1","volume-title":"Xiaohua Zhai, and Neil Houlsby.","author":"Neumann Maxim","year":"2019","unstructured":"Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, and Neil Houlsby. 2019. In-domain representation learning for remote sensing. arXiv preprint arXiv:1911.06721 (2019)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.628"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383157"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_30_1","volume-title":"Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network. Geocarto International","author":"Saralioglu Ekrem","year":"2020","unstructured":"Ekrem Saralioglu and Oguz Gungor. 2020. Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network. Geocarto International (2020)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Andrei Stoian et al. 2019. Land cover maps production with high resolution satellite image time series and convolutional neural networks: Adaptations and limits for operational systems. Remote Sensing (2019).","DOI":"10.20944\/preprints201906.0270.v2"},{"key":"e_1_3_2_1_32_1","volume-title":"Land Cover Change Detection via Semantic Segmentation. arXiv preprint arXiv:1911.12903","author":"Su Renee","year":"2019","unstructured":"Renee Su and Rong Chen. 2019. Land Cover Change Detection via Semantic Segmentation. arXiv preprint arXiv:1911.12903 (2019)."},{"key":"e_1_3_2_1_33_1","volume-title":"Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification. arXiv preprint arXiv:2003.02899","author":"Ulmas Priit","year":"2020","unstructured":"Priit Ulmas and Innar Liiv. 2020. Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification. arXiv preprint arXiv:2003.02899 (2020)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"e_1_3_2_1_35_1","volume-title":"The color out of space: learning self-supervised representations for Earth Observation imagery. arXiv preprint arXiv:2006.12119","author":"Vincenzi Stefano","year":"2020","unstructured":"Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Marco Cipriano, Pietro Fronte, Roberto Cuccu, Carla Ippoliti, Annamaria Conte, and Simone Calderara. 2020. The color out of space: learning self-supervised representations for Earth Observation imagery. arXiv preprint arXiv:2006.12119 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"International conference on machine learning. 478--487","author":"Xie Junyuan","year":"2016","unstructured":"Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In International conference on machine learning. 478--487."},{"key":"e_1_3_2_1_37_1","unstructured":"Michael Xie et al. 2015. Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. arXiv:1510.00098 [cs.CV]"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.556"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.76"},{"key":"e_1_3_2_1_40_1","unstructured":"L Zhou X Yang et al. 2008. Use of neural networks for land cover classification from remotely sensed imagery. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences (2008)."}],"event":{"name":"SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems","location":"Seattle Washington","acronym":"SIGSPATIAL '22","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"]},"container-title":["Proceedings of the 30th International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3557915.3560937","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3557915.3560937","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3557915.3560937","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:26Z","timestamp":1750182566000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3557915.3560937"}},"subtitle":["an application to land cover mapping"],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":40,"alternative-id":["10.1145\/3557915.3560937","10.1145\/3557915"],"URL":"https:\/\/doi.org\/10.1145\/3557915.3560937","relation":{},"subject":[],"published":{"date-parts":[[2022,11]]},"assertion":[{"value":"2022-11-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}