Luísa Treptow Ribeiro
Research Subjects
Luísa Treptow Ribeiro has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Andressa Ullmann Duarte Heberle
Pix Force Tecnologia S.A, Porto Alegre 90240-200, RS, Brazil
1 shared publication - André Vinicius Rodrigues Moreira
Light Serviços de Eletricidade S.A, Rio de Janeiro 20211-050, RJ, Brazil
1 shared publication - Antonio Oseas de Carvalho Filho
Federal University of Piauí (UFPI)
1 shared publication - Antônio Manoel Matta dos Santos Lameirão
LIGHT Energia S.A., 168 Marechal Floriano Avenue, Rio de Janeiro 20080-002, RJ, Brazil
1 shared publication - Cid Marcos Gonçalves Andrade
Department of Chemical Engineering, State University of Maringá (UEM), Maringá 87020-900, PR, Brazil
1 shared publication - Flávio Alves dos Santos
Pix Force Tecnologia S.A, Porto Alegre 90240-200, RS, Brazil
1 shared publication
Latest Publications
- Open Access
NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning
This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company’s coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.
Open AccessNTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning
This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company’s coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.