{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T04:53:00Z","timestamp":1772686380219,"version":"3.50.1"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":33,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Given current global energy demand, a substantial expansion of solar panel deployment in urban areas, across both developing and developed nations, is essential to meet the net-zero emissions targets outlined in the Paris Agreement. To maximize energy output, it is critical to identify photovoltaic (PV) installation sites that offer optimal conditions for high solar efficiency. This paper introduces the OVEN architecture (dO eVErything oNce)\u00a0 which simultaneously identifies potential PV installation sites and extracts parameterized representations of roof topologies. Unlike previous methods that decompose this problem in multiple stages, our YOLO-based approach requires a single pass to estimate roof characteristics. The model enables wider accessibility and scalability while significantly reducing operational costs and computational resources.<\/jats:p>","DOI":"10.2139\/ssrn.6346947","type":"posted-content","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T02:38:04Z","timestamp":1772678284000},"source":"Crossref","is-referenced-by-count":0,"title":["OVEN: A Deep Learning Framework for Photovoltaic Site Identification"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5641-5322","authenticated-orcid":true,"given":"Jo\u00e3o","family":"Oliveira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6285-8737","authenticated-orcid":true,"given":"Miguel  S. 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