{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T15:06:57Z","timestamp":1779116817695,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s11042-022-13493-9","type":"journal-article","created":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T08:06:47Z","timestamp":1659168407000},"page":"42309-42323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Optimized building extraction from high-resolution satellite imagery using deep learning"],"prefix":"10.1007","volume":"81","author":[{"given":"Ramesh","family":"Raghavan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinesh Chander","family":"Verma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0353-174X","authenticated-orcid":false,"given":"Digvijay","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit","family":"Anand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binay Kumar","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harinder","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"key":"13493_CR1","doi-asserted-by":"publisher","unstructured":"Azimi SM et al (2019) Towards multi-class object detection in unconstrained remote sensing imagery. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11363 LNCS, pp 150\u2013165. https:\/\/doi.org\/10.1007\/978-3-030-20893-6_10","DOI":"10.1007\/978-3-030-20893-6_10"},{"key":"13493_CR2","doi-asserted-by":"publisher","unstructured":"Ardila JP et al (2012)Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images. Int J Appl Earth Obs Geoinf. Elsevier B.V. 15(1):57\u201369. https:\/\/doi.org\/10.1016\/j.jag.2011.06.005","DOI":"10.1016\/j.jag.2011.06.005"},{"issue":"2","key":"13493_CR3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s12525-016-0219-0","volume":"26","author":"S Akter","year":"2016","unstructured":"Akter S, Wamba SF (2016) \u2018Big data analytics in E-commerce: a systematic review and agenda for future research. Electron Markets Electron Markets 26(2):173\u2013194. https:\/\/doi.org\/10.1007\/s12525-016-0219-0","journal-title":"Electron Markets Electron Markets"},{"key":"13493_CR4","doi-asserted-by":"crossref","unstructured":"Chen K, Fu K, Gao X, Yan M, Sun X, Zhang H (2017) Building extraction from remote sensing images with deep learning in a supervised manner. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp 1672\u20131675","DOI":"10.1109\/IGARSS.2017.8127295"},{"key":"13493_CR5","doi-asserted-by":"publisher","unstructured":"Cook K, Wright J (1975) Transmission Systems. 20(January 15, 1975), pp 219\u2013223. https:\/\/doi.org\/10.1049\/pbte071e_ch4","DOI":"10.1049\/pbte071e_ch4"},{"key":"13493_CR6","doi-asserted-by":"crossref","unstructured":"Duan Y, Sun L (2019) Buildings extraction from remote sensing data using deep learning method based on improved U-Net network. In: IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp 3959\u20133961","DOI":"10.1109\/IGARSS.2019.8899798"},{"issue":"4","key":"13493_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJDST.287859","volume":"12","author":"A Gupta","year":"2021","unstructured":"Gupta A, Anand R, Pandey D, Sindhwani N, Wairya S, Pandey BK, Sharma M (2021) Prediction of breast cancer using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of breast cancer. Int J Distrib Syst Technol (IJDST) 12(4):1\u201315","journal-title":"Int J Distrib Syst Technol (IJDST)"},{"key":"13493_CR8","doi-asserted-by":"publisher","unstructured":"Ghiasi G, Fowlkes CC (2016) Laplacian pyramid reconstruction and refinement for semantic segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9907 LNCS, pp 519\u2013534. https:\/\/doi.org\/10.1007\/978-3-319-46487-9_32","DOI":"10.1007\/978-3-319-46487-9_32"},{"key":"13493_CR9","doi-asserted-by":"publisher","unstructured":"G\u00fcler RA, Neverova N, Kokkinos I (2018) DensePose: Dense human pose estimation in the wild. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 7297\u20137306. https:\/\/doi.org\/10.1109\/CVPR.2018.00762","DOI":"10.1109\/CVPR.2018.00762"},{"key":"13493_CR10","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter, pp 1440\u20131448. https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"13493_CR11","doi-asserted-by":"crossref","unstructured":"Huang Z, Cheng G, Wang H, Li H, Shi L, Pan C (2016) Building extraction from multi-source remote sensing images via deep deconvolution neural networks. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp 1835\u20131838","DOI":"10.1109\/IGARSS.2016.7729471"},{"issue":"1","key":"13493_CR12","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1109\/JSTARS.2011.2168195","volume":"5","author":"X Huang","year":"2012","unstructured":"Huang X, Zhang L (2012) Morphological building\/shadow index for building extraction from high-resolution imagery over urban areas. IEEE J Sel Top Appl Earth Obs Remote Sens 5(1):161\u2013172. https:\/\/doi.org\/10.1109\/JSTARS.2011.2168195","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"13493_CR13","doi-asserted-by":"publisher","unstructured":"He K et al (2017) Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2017-Octob, pp 2980\u20132988. https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"issue":"5","key":"13493_CR14","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/LGRS.2018.2880986","volume":"16","author":"J Hui","year":"2018","unstructured":"Hui J, Du M, Ye X, Qin Q, Sui J (2018) Effective building extraction from high-resolution remote sensing images with multitask driven deep neural network. IEEE Geosci Remote Sens Lett 16(5):786\u2013790","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"14","key":"13493_CR15","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1155\/ASP.2005.2196","volume":"2005","author":"X Jin","year":"2005","unstructured":"Jin X, Davis CH (2005) Automated building extraction from high-resolution satellite imagery in Urban areas using structural, contextual, and spectral information. EURASIP J Appl Sig Process 2005(14):2196\u20132206. https:\/\/doi.org\/10.1155\/ASP.2005.2196","journal-title":"EURASIP J Appl Sig Process"},{"key":"13493_CR16","doi-asserted-by":"publisher","unstructured":"Liu Z et al (2015) \u2018Deep learning face attributes in the wild. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, pp 3730\u20133738. https:\/\/doi.org\/10.1109\/ICCV.2015.425","DOI":"10.1109\/ICCV.2015.425"},{"key":"13493_CR17","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhou J, Qi W, Li X, Gross L, Shao Q, ... Li Z (2020) ARC-Net: an efficient network for building extraction from high-resolution aerial images. IEEE Access 8:154997\u2013155010","DOI":"10.1109\/ACCESS.2020.3015701"},{"key":"13493_CR18","doi-asserted-by":"publisher","unstructured":"Li W et al (2006) A novel framework for urban change detection using VHR satellite images. Proceedings - International Conference on Pattern Recognition 2, pp 312\u2013315. https:\/\/doi.org\/10.1109\/ICPR.2006.138","DOI":"10.1109\/ICPR.2006.138"},{"issue":"10","key":"13493_CR19","doi-asserted-by":"publisher","first-page":"3680","DOI":"10.1109\/JSTARS.2018.2865187","volume":"11","author":"X Li","year":"2018","unstructured":"Li X, Yao X, Fang Y (2018) Building-a-nets: Robust building extraction from high-resolution remote sensing images with adversarial networks. IEEE J Sel Top Appl Earth Obs Remote Sens 11(10):3680\u20133687","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"13493_CR20","doi-asserted-by":"publisher","first-page":"128774","DOI":"10.1109\/ACCESS.2019.2940527","volume":"7","author":"Y Liu","year":"2019","unstructured":"Liu Y, Gross L, Li Z, Li X, Fan X, Qi W (2019) Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling. IEEE Access 7:128774\u2013128786","journal-title":"IEEE Access"},{"issue":"3","key":"13493_CR21","doi-asserted-by":"publisher","first-page":"103","DOI":"10.5194\/isprsannals-II-3-W4-103-2015","volume":"2","author":"D Marmanis","year":"2015","unstructured":"Marmanis D, Adam F, Datcu M, Esch T, Stilla U (2015) Deep neural networks for above-ground detection in very high spatial resolution digital elevation models. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2(3):103","journal-title":"ISPRS Ann Photogramm Remote Sens Spat Inf Sci"},{"issue":"8","key":"13493_CR22","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1109\/JSTARS.2019.2924582","volume":"12","author":"RD Majd","year":"2019","unstructured":"Majd RD, Momeni M, Moallem P (2019) Transferable object-based framework based on deep convolutional neural networks for building extraction. IEEE J Sel Top Appl Earth Observations Remote Sens 12(8):2627\u20132635","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"key":"13493_CR23","doi-asserted-by":"publisher","unstructured":"Meivel S, Sindhwani N, Anand R, Pandey D, Alnuaim AA, Altheneyan AS, ... Lelisho ME (2022) Mask detection and social distance identification using internet of things and faster R-CNN algorithm. Comput Intell Neurosci 2022:2103975. https:\/\/doi.org\/10.1155\/2022\/2103975","DOI":"10.1155\/2022\/2103975"},{"key":"13493_CR24","doi-asserted-by":"publisher","unstructured":"Madhumathy P, Pandey D (2022) Deep learning based photo acoustic imaging for non-invasive imaging. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-022-11903-6","DOI":"10.1007\/s11042-022-11903-6"},{"key":"13493_CR25","doi-asserted-by":"crossref","unstructured":"Pandey D, Pandey BK (2022) An efficient deep neural network with adaptive galactic swarm optimization for complex image text extraction. Process Mining Techniques for Pattern Recognition. CRC Press, pp 121\u2013137","DOI":"10.1201\/9781003169550-10"},{"issue":"1","key":"13493_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41133-021-00051-5","volume":"6","author":"BK Pandey","year":"2021","unstructured":"Pandey BK, Pandey D, Wariya S, Aggarwal G, Rastogi R (2021) Deep learning and particle swarm optimisation-based techniques for visually impaired humans\u2019 text recognition and identification. Augmented Hum Res 6(1):1\u201314","journal-title":"Augmented Hum Res"},{"issue":"5","key":"13493_CR27","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/72.159058","volume":"3","author":"SK Pal","year":"1992","unstructured":"Pal SK, Mitra S (1992) Multilayer perceptron, fuzzy sets, and classification. IEEE Trans Neural Netw 3(5):683\u2013697. https:\/\/doi.org\/10.1109\/72.159058","journal-title":"IEEE Trans Neural Netw"},{"key":"13493_CR28","doi-asserted-by":"crossref","unstructured":"Pandey BK, Mane D, Nassa VKK, Pandey D, Dutta S, Ventayen RJM, ... Rastogi R (2021) Secure text extraction from complex degraded images by applying steganography and deep learning. In: Multidisciplinary Approach to Modern Digital Steganography. IGI Global, pp 146-163","DOI":"10.4018\/978-1-7998-7160-6.ch007"},{"key":"13493_CR29","doi-asserted-by":"crossref","unstructured":"Pandey BK, Pandey D, Wariya S, Agarwal G (2021) A deep neural network-based approach for extracting textual images from deteriorate images. EAI Endorsed Trans Ind Netw Intell Syst 8(28):e3","DOI":"10.4108\/eai.17-9-2021.170961"},{"key":"13493_CR30","doi-asserted-by":"publisher","unstructured":"Papadomanolaki M et al (2016) Benchmarking deep learning frameworks for the classification of very high resolution satellite multispectral data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci III\u20137(July):83\u201388. https:\/\/doi.org\/10.5194\/isprsannals-iii-7-83-2016","DOI":"10.5194\/isprsannals-iii-7-83-2016"},{"key":"13493_CR31","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1007\/s00500-020-05245-4","volume":"25","author":"D Pandey","year":"2021","unstructured":"Pandey D, Pandey B, Wairya S (2021) Hybrid deep neural network with adaptive galactic swarm optimization for text extraction from scene images. Soft Comput 25:1563\u20131580. https:\/\/doi.org\/10.1007\/s00500-020-05245-4","journal-title":"Soft Comput"},{"issue":"1","key":"13493_CR32","doi-asserted-by":"publisher","first-page":"131","DOI":"10.4018\/IJISMD.2021010107","volume":"12","author":"N Sindhwani","year":"2021","unstructured":"Sindhwani N, Verma S, Bajaj T, Anand R (2021) Comparative analysis of intelligent driving and safety assistance systems using YOLO and SSD model of deep learning. Int J Inform Syst Model Des (IJISMD) 12(1):131\u2013146","journal-title":"Int J Inform Syst Model Des (IJISMD)"},{"key":"13493_CR33","doi-asserted-by":"publisher","unstructured":"Vakalopoulou M et al (2015) Building detection in very high resolution multispectral data with deep learning features. International Geoscience and Remote Sensing Symposium (IGARSS), 2015-Novem, pp 1873\u20131876. https:\/\/doi.org\/10.1109\/IGARSS.2015.7326158","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"13493_CR34","doi-asserted-by":"publisher","unstructured":"Wang Y, Gu L, Li X, Ren R (2020) Building extraction in multitemporal high-resolution remote sensing imagery using a multifeature LSTM network.IEEE Geosci Remote Sensing Lett. https:\/\/doi.org\/10.1109\/LGRS.2020.3005018","DOI":"10.1109\/LGRS.2020.3005018"},{"key":"13493_CR35","doi-asserted-by":"publisher","unstructured":"Wang M, Yuan S, Pan J (2013) Building detection in high resolution satellite urban image using segmentation, corner detection combined with adaptive windowed Hough Transform. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote. Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, pp 508\u2013511. https:\/\/doi.org\/10.1109\/IGARSS.2013.6721204","DOI":"10.1109\/IGARSS.2013.6721204"},{"key":"13493_CR36","doi-asserted-by":"publisher","unstructured":"Wang X, Shrivastava A, Gupta A (2017) A-Fast-RCNN: Hard positive generation via adversary for object detection. Proceedings \u2013 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, pp 3039\u20133048. https:\/\/doi.org\/10.1109\/CVPR.2017.324","DOI":"10.1109\/CVPR.2017.324"},{"key":"13493_CR37","doi-asserted-by":"publisher","unstructured":"Xu Y et al (2018) Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens 10(1). https:\/\/doi.org\/10.3390\/rs10010144","DOI":"10.3390\/rs10010144"},{"key":"13493_CR38","unstructured":"Yuan J (2016) Automatic building extraction in aerial scenes using convolutional networks. arXiv preprint arXiv:1602.06564"},{"key":"13493_CR39","doi-asserted-by":"crossref","unstructured":"Singh SK, Thakur RK, Kumar S, Anand R (2022, March) Deep Learning and Machine Learning based Facial Emotion Detection using CNN. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (pp 530\u2013535). IEEE","DOI":"10.23919\/INDIACom54597.2022.9763165"},{"key":"13493_CR40","doi-asserted-by":"crossref","unstructured":"Saini P, Anand MR (2014) Identification of Defects in Plastic Gears Using Image Processing and Computer Vision: A Review. Int J Eng Res 3(2):94\u201399","DOI":"10.17950\/ijer\/v3s2\/212"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13493-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13493-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13493-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,26]],"date-time":"2022-11-26T22:49:07Z","timestamp":1669502947000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13493-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,30]]},"references-count":40,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["13493"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13493-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,30]]},"assertion":[{"value":"17 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable (as the results of studies does not involve any human or animal).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not Applicable (as the results of studies does not involve any human or animal).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not Applicable (as the results of studies does not involve any human or animal).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have \u2018no known conflict of interests or personal relationships\u2019 that could have appeared to influence the work reported in this paper.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\u00a0\/ Competing interests"}}]}}