乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:14:26Z","timestamp":1740154466585,"version":"3.37.3"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T00:00:00Z","timestamp":1589241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2015\/50484-0","2016\/17652-9","2015\/22987-7"],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council","doi-asserted-by":"publisher","award":["NE\/N012542\/1"],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-17-CE23-0009"],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Brazilian Development Bank","award":["17.2.0536.1"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Currently, there exists a growing demand for individual building mapping in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that performs building instance segmentation. The proposed network is trained with WorldView-3 satellite RGB images (0.3 m) and three different labeled masks. The first is the building mask; the second is the border mask, which is the border of the building segment with 4 pixels added outside and 3 pixels inside; and the third is the inner segment mask, which is the segment of the building diminished by 2 pixels. The architecture consists of three parallel paths, one for each mask, all starting with a U-net model. To accurately capture the overlap between the masks, all activation layers of the U-nets are copied and concatenated on each path and sent to two additional convolutional layers before the output activation layers. The method was tested with a dataset of 7563 manually delineated individual buildings of the city of Joan\u00f3polis-SP, Brazil. On this dataset, the semantic segmentation showed an overall accuracy of 97.67% and an F1-Score of 0.937 and the building individual instance segmentation showed good performance with a mean intersection over union (IoU) of 0.582 (median IoU = 0.694).<\/jats:p>","DOI":"10.3390\/rs12101544","type":"journal-article","created":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T14:53:55Z","timestamp":1589295235000},"page":"1544","source":"Crossref","is-referenced-by-count":43,"title":["U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images\u2014Case Study in the Joan\u00f3polis City, Brazil"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9623-1182","authenticated-orcid":false,"given":"Fabien","family":"Wagner","sequence":"first","affiliation":[{"name":"GeoProcessing Division, Foundation for Science, Technology and Space Applications\u2014FUNCATE, S\u00e3o Jos\u00e9 dos Campos, SP 12210-131, Brazil"},{"name":"Remote Sensing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos, SP 12227-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-8697","authenticated-orcid":false,"given":"Ricardo","family":"Dalagnol","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos, SP 12227-010, Brazil"}]},{"given":"Yuliya","family":"Tarabalka","sequence":"additional","affiliation":[{"name":"Luxcarta Technology, Parc d\u2019Activit\u00e9 l\u2019Argile, Lot 119b, 06370 Mouans Sartoux, France"},{"name":"Inria Sophia Antipolis, CEDEX, 06902 Sophia Antipolis, France"}]},{"given":"Tassiana","family":"Segantine","sequence":"additional","affiliation":[{"name":"GeoProcessing Division, Foundation for Science, Technology and Space Applications\u2014FUNCATE, S\u00e3o Jos\u00e9 dos Campos, SP 12210-131, Brazil"}]},{"given":"Rog\u00e9rio","family":"Thom\u00e9","sequence":"additional","affiliation":[{"name":"GeoProcessing Division, Foundation for Science, Technology and Space Applications\u2014FUNCATE, S\u00e3o Jos\u00e9 dos Campos, SP 12210-131, Brazil"}]},{"given":"Mayumi","family":"Hirye","sequence":"additional","affiliation":[{"name":"Quap\u00e1 Lab, Faculty of Architecture and Urbanism, University of S\u00e3o Paulo\u2014USP, S\u00e3o Paulo, SP 05508-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. 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