{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:49:47Z","timestamp":1772758187990,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing University of Technology \u201cUrban Carbon Neutral\u201d Science and Technology Innovation Fund","award":["YS22-1013392-170"],"award-info":[{"award-number":["YS22-1013392-170"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.<\/jats:p>","DOI":"10.3390\/rs14133144","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"3144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3653-6531","authenticated-orcid":false,"given":"Shaofu","family":"Lin","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100124, China"}]},{"given":"Chang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100124, China"}]},{"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Big Data Analysis, PLA Strategic Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5882-9389","authenticated-orcid":false,"given":"Xiliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100124, China"}]},{"given":"Guihong","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Big Data Centre, Chaoyang District, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8651-9505","authenticated-orcid":false,"given":"Shaohua","family":"Wang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0714-0172","authenticated-orcid":false,"given":"Jinchuan","family":"Chai","sequence":"additional","affiliation":[{"name":"National Railway Track Test Center, China Academy of Railway Sciences Corporation Limited (CARS), Engineer, Jiuxiaoqiao North Road, Chaoyang District, Beijing 100015, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.enpol.2013.12.068","article-title":"Development of a new energy efficiency rating system for existing residential buildings","volume":"68","author":"Koo","year":"2014","journal-title":"Energy Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.energy.2008.04.003","article-title":"Energy system analysis of 100% renewable energy systems\u2014The case of Denmark in years 2030 and 2050","volume":"34","author":"Lund","year":"2009","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.rser.2015.04.138","article-title":"Solar energy potential assessment: An overview and a fast modeling approach with application to Italy","volume":"49","author":"Bocca","year":"2015","journal-title":"Renew. 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