{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T09:57:23Z","timestamp":1730195843331,"version":"3.28.0"},"reference-count":28,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1109\/aipr.2017.8457973","type":"proceedings-article","created":{"date-parts":[[2018,9,21]],"date-time":"2018-09-21T20:57:51Z","timestamp":1537563471000},"page":"1-7","source":"Crossref","is-referenced-by-count":17,"title":["A Benchmark for Building Footprint Classification Using Orthorectified RGB Imagery and Digital Surface Models from Commercial Satellites"],"prefix":"10.1109","author":[{"given":"Hirsh","family":"Goldberg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myron","family":"Brown","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"journal-title":"Building Detection from Satellite Images on a Global Scale","year":"2017","author":"zhang","key":"ref10"},{"journal-title":"SpaceNet Challenge","year":"0","key":"ref11"},{"journal-title":"DSTL Satellite Imagery Feature Detection Challenge","year":"0","key":"ref12"},{"journal-title":"ISPRS Test Project on Urban Classification \\3D Building Reconstruction and Semantic Labeling","year":"0","key":"ref13"},{"key":"ref14","article-title":"Can semantic labeling methods generalize to any city? The INRIA aerial image labeling benchmark","author":"maggiori","year":"2017","journal-title":"IEEE Symp Geoscience and Remote Sensing (IGARSS)"},{"journal-title":"TorontoCity Seeing the world with a million eyes","year":"2016","author":"wang","key":"ref15"},{"key":"ref16","article-title":"Aerial imagery object identification dataset for building and road detection, and building height estimation","author":"bradbury","year":"2016","journal-title":"Figshare"},{"journal-title":"OpenStreetMap homepage","year":"0","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/AIPR.2016.8010543"},{"journal-title":"Multi-view Stereo \\3D Challenge Homepage","year":"0","key":"ref19"},{"journal-title":"Urban \\3D Challenge Website","year":"0","key":"ref28"},{"key":"ref4","article-title":"Semi-Supervised Learning with Visual Pixel-Level Similaries for Object Detection","author":"binh","year":"2016","journal-title":"roceedings of the International Conference on Image Processing Computer Vision and Pattern Recognition (IPCV)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2013.2256882"},{"key":"ref3","article-title":"Semantic Classification in Aerial Imagery by Integrating Appearance and Height Information","author":"kluckner","year":"2009","journal-title":"Proc Asian Conf Comput Vision"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1999.0750"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.3390\/rs3061188","article-title":"Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data","volume":"3","author":"hermosilla","year":"2011","journal-title":"Remote Sensing"},{"key":"ref8","article-title":"Extraction of building footprints from satellite imagery","author":"chartock","year":"2017","journal-title":"Stanford University"},{"key":"ref7","article-title":"A SVM-based approach to extract building footprints","author":"bachofer","year":"2015","journal-title":"Proceedings of International Conference on “Geospatial Technologies for Sustainable Urban and Rural Development"},{"key":"ref2","article-title":"Automatic building detection in aerial and satellite images","author":"saeedi","year":"2008","journal-title":"International Conference on Control Automation Robotics and Vision"},{"journal-title":"Multi-task learning for segmentation of building footprints with deep neural networks","year":"2017","author":"bischke","key":"ref9"},{"journal-title":"Machine Learning for Aerial Image Labeling","year":"2013","author":"mnih","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.5194\/isprs-archives-XLII-1-W1-239-2017"},{"journal-title":"Digital terrain model generation using structure from motion Influence of canopy closure and interpolation method on accuracy","year":"2017","author":"washburn","key":"ref22"},{"journal-title":"Vricon Homepage","year":"0","key":"ref21"},{"key":"ref24","article-title":"Automated feature extraction for 3-dimensional point clouds","author":"magruder","year":"2016","journal-title":"Proceedings Volume 9832 Laser Radar Technology and Application XXI SPIE Defense and Security"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.5194\/isprsarchives-XLI-B3-201-2016"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/S0034-4257(01)00295-4"},{"journal-title":"Public Geospatial Data and Software","year":"0","key":"ref25"}],"event":{"name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","start":{"date-parts":[[2017,10,10]]},"location":"Washington, DC","end":{"date-parts":[[2017,10,12]]}},"container-title":["2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8424301\/8457934\/08457973.pdf?arnumber=8457973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:24:20Z","timestamp":1598228660000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8457973\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/aipr.2017.8457973","relation":{},"subject":[],"published":{"date-parts":[[2017,10]]}}}