{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:52:42Z","timestamp":1770886362879,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-1645952, IIP-1534080, CNS-1405826, CNS1505422"],"award-info":[{"award-number":["CNS-1645952, IIP-1534080, CNS-1405826, CNS1505422"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330741","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"2105-2113","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["DeepRoof"],"prefix":"10.1145","author":[{"given":"Stephen","family":"Lee","sequence":"first","affiliation":[{"name":"University of Massachusetts, Amherst, Amherst, MA, USA"}]},{"given":"Srinivasan","family":"Iyengar","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bangalore, Bangalore, India"}]},{"given":"Menghong","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, Amherst, MA, USA"}]},{"given":"Prashant","family":"Shenoy","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, Amherst, MA, USA"}]},{"given":"Subhransu","family":"Maji","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, Amherst, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561","author":"Badrinarayanan V","year":"2015"},{"key":"e_1_3_2_2_2_1","volume-title":"Evaluation of different models to estimate the global solar radiation on inclined surfaces. Renewable Energy","author":"Demain C","year":"2013"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.03.024"},{"key":"e_1_3_2_2_4_1","unstructured":"A. Dutta A. Gupta and A. Zissermann. 2016. VGG Image Annotator (VIA). http:\/\/www.robots.ox.ac.uk\/ vgg\/software\/via\/. (2016).  A. Dutta A. Gupta and A. Zissermann. 2016. VGG Image Annotator (VIA). http:\/\/www.robots.ox.ac.uk\/ vgg\/software\/via\/. (2016)."},{"key":"e_1_3_2_2_6_1","volume-title":"17th International Conference on Electrical Drives and Power Electronics .","author":"Gulin M","year":"2013"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"K He G Gkioxari P Doll\u00e1r and R Girshick. 2017. Mask R-CNN. In ICCV .  K He G Gkioxari P Doll\u00e1r and R Girshick. 2017. Mask R-CNN. In ICCV .","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_2_2_8_1","volume":"201","author":"He K","journal-title":"J Sun."},{"key":"e_1_3_2_2_9_1","volume-title":"International building code","author":"ICC IBC.","year":"2006"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219825"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209811.3209860"},{"key":"e_1_3_2_2_12_1","volume-title":"GIS data, and hourly daysim simulations. IBPSA-USA Journal","author":"Jakubiec J A","year":"2012"},{"key":"e_1_3_2_2_13_1","unstructured":"A Krizhevsky I Sutskever and G Hinton. 2012. Imagenet classification with deep convolutional neural networks. In NIPS .   A Krizhevsky I Sutskever and G Hinton. 2012. Imagenet classification with deep convolutional neural networks. In NIPS ."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/PVSC.2016.7750279"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077839.3077840"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"T Lin P Doll\u00e1r R Girshick K He B Hariharan and S Belongie. 2017. Feature pyramid networks for object detection. In CVPR .  T Lin P Doll\u00e1r R Girshick K He B Hariharan and S Belongie. 2017. Feature pyramid networks for object detection. In CVPR .","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_3_2_2_17_1","volume-title":"Daily insolation on surfaces tilted towards equator. ASHRAE Journal","author":"Liu B","year":"1961"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"J Long E Shelhamer and T Darrell. 2015. Fully convolutional networks for semantic segmentation. In CVPR .  J Long E Shelhamer and T Darrell. 2015. Fully convolutional networks for semantic segmentation. In CVPR .","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/37401.37422"},{"key":"e_1_3_2_2_20_1","volume-title":"Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities. Environmental Research Letters","author":"Margolis R","year":"2017"},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of the 29th International conference on machine learning (ICML) .","author":"Mnih V","year":"2012"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.647"},{"key":"e_1_3_2_2_24_1","volume-title":"IEEE International Geoscience and Remote Sensing Symposium .","author":"Wei Y","year":"2004"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330741","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330741","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330741","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:57:50Z","timestamp":1750208270000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330741"}},"subtitle":["A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery"],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":23,"alternative-id":["10.1145\/3292500.3330741","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330741","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}