ChinaPV-10m-2024: A Vector Dataset of Utility-Scale and Rooftop PV Plants
ChinaPV-10m-2024: 中国における大規模・屋上太陽光発電所のベクターデータセット (AI 翻訳)
Zhang, Bin
🤖 gxceed AI 要約
日本語
中国全域の大規模・屋上太陽光発電所を10m解像度でベクター化したデータセット。ハイブリッド遺伝的アルゴリズムとLightGBMを用いてSentinel-1/2画像から抽出し、精度95.16%を達成。各ポリゴンに容量、発電量、CO2削減量を付与。再生可能エネルギー計画や脱炭素化評価に活用可能。
English
This dataset provides a 10-m resolution vector map of utility-scale and rooftop PV plants across China in 2024, extracted from Sentinel-1/2 imagery using a hybrid genetic algorithm and LightGBM classifier with 95.16% accuracy. It includes polygon geometries with estimated capacity, annual generation, and avoided CO2 emissions. The dataset supports spatial energy planning and decarbonization assessment.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本データセットは中国に特化しているが、同様の手法は日本の太陽光発電マッピングにも応用可能。特に日本の再エネ導入計画やSSBJ関連の報告における実データ活用の参考になる。
In the global GX context
ChinaPV-10m-2024 provides a high-resolution, open dataset for the world's largest solar market, enabling cross-country comparisons and supporting global energy transition and carbon accounting efforts. The methodology is scalable to other regions.
👥 読者別の含意
🔬研究者:This dataset offers a high-quality, validated source for PV deployment analysis and can be used for energy system modeling, carbon accounting, and climate resilience studies.
🏢実務担当者:Corporate sustainability teams can use this data to benchmark PV expansion in China or apply similar methodology for their own supply chain mapping.
🏛政策担当者:Policymakers can leverage this dataset for spatial energy planning and to assess decarbonization potential of solar PV at regional scale.
📄 Abstract（原文）
This dataset provides a comprehensive 10-m resolution vector map of utility-scale and rooftop photovoltaic (PV) power plants across China for the year 2024. It was generated using a novel framework that integrates a Customized Hybrid Genetic Algorithm (CHGA) for feature optimization with a scalable LightGBM classifier, applied to multi-temporal Sentinel-1 SAR and Sentinel-2 optical imagery. The extraction model achieved a high overall accuracy of 95.16%. The dataset is released in ESRI Shapefile format (WGS-84 coordinate system) and includes polygon geometries representing individual PV installations. Each polygon is attributed with estimated installed capacity, annual electricity generation, and avoided CO₂ emissions, derived using consistent provincial grid emission factors. To ensure full transparency and reproducibility, the complete model framework, Sentinel-1/2 preprocessing pipelines, and post-processing code are released alongside the dataset. This resource is intended to support research in spatial energy planning, decarbonization potential assessment, and climate resilience analysis for PV infrastructure.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20179001first seen 2026-05-22 04:13:14 · last seen 2026-05-27 04:13:42
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。