A Yolo-Based Semantic Segmentation Model for Solar Photovoltaic Panel Identification.

Jiandong Zhang, Daqing Chen, Bo Li, Zhanfang Zhao, Huibo Bi, Perry Xiao

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jiandong ZhangSchool of Engineering and Design, London South Bank University, 103 Borough Road, London SE1 0AA, UK.

Daqing ChenSchool of Engineering and Design, London South Bank University, 103 Borough Road, London SE1 0AA, UK.ORCID 0000-0003-0030-1199

Bo LiDepartment of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong 999077, China.

Zhanfang ZhaoSchool of Engineering and Design, London South Bank University, 103 Borough Road, London SE1 0AA, UK.

Huibo BiCollege of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0001-7923-4341

Perry XiaoSchool of Engineering and Design, London South Bank University, 103 Borough Road, London SE1 0AA, UK.ORCID 0000-0002-9036-3061

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global shift towards renewable energy is increasingly driven by the need to reduce carbon emissions and address urban energy demands sustainably. Solar power, as an accessible and efficient energy source, offers substantial potential for integration within urban environments. However, there remains a lack of a comprehensive evaluation framework for accurately predicting the energy generation of urban solar panel installations. Therefore, in this study, we develop a YOLO-based semantic segmentation framework to estimate the energy generation potential of existing solar panels in a city-scale fashion and use the Elephant andCastle area of London city as a case study. The results demonstrate that the proposed model can detect and segment solar panels in complex urban environments with an accuracy of 98.32%, and the total area of solar panels in the designated area is 127.75 m

Indexed as

attention mechanismdeep learningenergy generation evaluationsolar panel detectionYOLO-based model

Identifiers

PMID41516510

DOI10.3390/s26010075

PMCPMC12787452

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.