Controle gerencial na administração pública e transformação digital: sensoriamento remoto para fiscalizar

Orlandi, Alex Gois; Farias, Renata de Araujo Nobre; Júnior, Osmar Abílio Carvalho; Guimarães, Renato Fontes; Gomes, Roberto Arnaldo Trancoso

doi:10.12660/cgpc.v26n83.80456

Cited by 1 publication

(1 citation statement)

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“…Thus, inspecting solar plant constructions is important in order to carry out effective public policies. In Brazil, the Brazilian Electricity Regulatory Agency (ANEEL) is responsible for regulating the installed capacity expansion and monitoring the powerplant construction progress [38]. However, the inspection is manual, which will increase in complexity over time, requiring laborious work with skilled professionals and high costs for fieldworks and technical analysis.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

et al. 2021

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Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach.

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Section: Introductionmentioning

confidence: 99%