Geoinformatics-Guided Machine Learning for Power Plant Classification
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915134315692032 |
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| author | Austin-Gabriel, Blessing Varde, Aparna S. Liu, Hao |
| author_facet | Austin-Gabriel, Blessing Varde, Aparna S. Liu, Hao |
| contents | This paper proposes an approach in the area of Knowledge-Guided Machine Learning (KGML) via a novel integrated framework comprising CNN (Convolutional Neural Networks) and ViT (Vision Transformers) along with GIS (Geographic Information Systems) to enhance power plant classification in the context of energy management. Knowledge from geoinformatics derived through Spatial Masks (SM) in GIS is infused into an architecture of CNN and ViT, in this proposed KGML approach. It is found to provide much better performance compared to the baseline of CNN and ViT only in the classification of multiple types of power plants from real satellite imagery, hence emphasizing the vital role of the geoinformatics-guided approach. This work makes a contribution to the main theme of KGML that can be beneficial in many AI systems today. It makes broader impacts on AI in Smart Cities, and Environmental Computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geoinformatics-Guided Machine Learning for Power Plant Classification Austin-Gabriel, Blessing Varde, Aparna S. Liu, Hao Machine Learning I.2.6; I.2.m; J.2 This paper proposes an approach in the area of Knowledge-Guided Machine Learning (KGML) via a novel integrated framework comprising CNN (Convolutional Neural Networks) and ViT (Vision Transformers) along with GIS (Geographic Information Systems) to enhance power plant classification in the context of energy management. Knowledge from geoinformatics derived through Spatial Masks (SM) in GIS is infused into an architecture of CNN and ViT, in this proposed KGML approach. It is found to provide much better performance compared to the baseline of CNN and ViT only in the classification of multiple types of power plants from real satellite imagery, hence emphasizing the vital role of the geoinformatics-guided approach. This work makes a contribution to the main theme of KGML that can be beneficial in many AI systems today. It makes broader impacts on AI in Smart Cities, and Environmental Computing. |
| title | Geoinformatics-Guided Machine Learning for Power Plant Classification |
| topic | Machine Learning I.2.6; I.2.m; J.2 |
| url | https://arxiv.org/abs/2502.01039 |