VERTICAL ACCURACY ASSESSMENT OF THE PROCESSED SRTM DATA FOR THE BRAZILIAN TERRITORY

This research aims to determine the vertical accuracy of the Interferometric Digital Elevation Model (DEM) obtained from the processed Shuttle Radar Topographic Mission (SRTM) data. The research compared the SRTM-GL1 (Shuttle Radar Topographic Mission-Global 1) with 30-meter resolution and the following 90-meter resolution models: (a) EMBRAPA; (b) Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS) (HydroSHEDS), provided by the United States Geological Survey (USGS); (c) Consultative Group for International Agricultural Research-Consortium for Spatial Information (CGIAR-CSI); and (d) Jonathan de Ferranti. The accuracy analysis considered the diverse Brazilian regions, adopting 1,087 field points from the Global Navigation Satellite System (GNSS) trackers or topography methods. The Jonathan de Ferranti model achieved the best accuracy with RMSE of 9.61m among the 90-meter resolution models. Most SRTM models at 1:100,000 scale reached Grade A of the Cartographic Accuracy Standard. However, the accuracy at the 1: 50,000 scale did not achieve the same performance. SRTM errors are linearly related to slope and the most significant errors always occur in forest areas. The 30-meter resolution SRTM showed an accuracy of around 10% better (RMSE of 8.52m) than the model of Jonathan de Ferranti with 90-meter resolution (RMSE of 9.61m)

Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation

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

Avaliação da acurácia vertical do modelo SRTM para o Brasil

Esse trabalho tem como objetivo determinar a acurácia vertical do Modelo Digital de Elevação (MDE) interferométrico obtido a partir das várias versões provenientes do Shuttle Radar Topographic Mission (SRTM) em todo o Brasil, comparando-os com 1.087 pontos medidos diretamente em campo, obtidos com rastreadores Global Navigation satellite System (GNSS) ou métodos de topografia. Esses pontos foram utilizados para o apoio básico às restituições aerofotogramétricas utilizadas nos estudos de inventários hidrelétricos ou projetos básicos de usinas hidrelétricas em território brasileiro. Neste trabalho, são comparados os seguintes MDEs: a) modelo disponibilizado pela EMBRAPA, b) modelo Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS), fornecido pelo United States Geological Survey (USGS) c) Consultative Group for International Agricultural Research- Consortium for Spatial Information (CGIAR_CSI), e d) Jhonatan Ferranti. Neste estudo, o Brasil foi divido em faixas com 4º de latitude, visando à distribuição mais homogênea possível dos dados de campo e minimizando a possibilidade de ocorrência de grandes áreas sem medições. Os resultados demonstraram que a acurácia vertical do Modelo fornecido pelo SRTM varia ao longo do território brasileiro, apresentando para os 1.087 pontos distribuídos no Brasil: erro médio de 8,96m e desvio-padrão de 11,20m para os modelos da EMBRAPA; erro médio de 0,24m e desviopadrão de 12,70m para o HydroSHEDS; erro médio de 9,78m e desvio-padrão de 8,16m para CGIAR-CSI; erro médio de 6,33m e desvio-padrão de 7,22m para Jhonatan Ferranti. Além disso, no conjunto dos 1.087 pontos, todos os Modelos podem ser classificados no Padrão de Exatidão Cartográfica como “A” para a escala de 1:100.000, mas podem apresentar resultados melhores em termos locais. Também foram evidenciadas as influências direta da declividade e da vegetação na acurácia altimétrica dos pontos. Tais conclusões reforçam a ideia de que o usuário deve ter a exata noção da acurácia esperada do Modelo e que sua acurácia altimétrica também varia em razão da fonte em que o modelo é obtido, apesar de possuir a mesma resolução espacial. Assim, tais fatores são importantes, mas não diminuem o valor agregado por esse Modelo gratuito para muitos estudos que sejam compatíveis com a escala indicada para sua utilização

Transformação digital e o controle externo exercido pela Administração Pública : o caso do sensoriamento remoto para fiscalização de obras no setor elétrico brasileiro por meio de insumos gratuitos

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022Este trabalho tem como objetivo analisar a viabilidade e a aplicação de imagens gratuitas de satélite no processo de monitoramento e fiscalização das obras de usinas de geração de energia em implantação, tendo por base um projeto piloto em desenvolvimento na Agência Nacional de Energia Elétrica. O estudo foi feito em todo o território nacional nos locais onde havia usinas em implantação com o objetivo de verificar essa aplicabilidade por meio de imagens dos satélites CBERS4, CBERS4A e Sentinel 2. Os resultados obtidos sinalizam o potencial de enriquecimento que o uso de imagens traz aos processos de controle na Administração Pública, ao fortalecimento da accountability no setor público e ao efeito positivo da transformação digital como ferramenta simples e moderna de fiscalização, propiciando economicidade e eficiência ao controle externo na administração pública.The objective of this work is to analyze the feasibility and application of free satellite images in the process of monitoring and inspecting the works of power generation plants under implementation, based on a pilot project under development at the National Electric Energy Agency. The study was carried out throughout the national territory in places where there were plants being implemented in order to verify this applicability through images from the CBERS4, CBERS4A and Sentinel 2 satellites. The results obtained indicate the potential for enrichment that the use of images brings to the control processes in the Public Administration, to the strengthening of accountability in the public sector and to the positive effect of digital transformation as a simple and modern inspection tool, providing economic and efficient external control in public administration.Instituto de Ciências Humanas (ICH)Departamento de Geografia (ICH GEA)Programa de Pós-Graduação em Geografi

A data-centric approach for wind plant instance-level segmentation using semantic segmentation and GIS

Wind energy is one of Brazil’s most promising energy sources, and the rapid growth of wind plants has increased the need for accurate and efficient inspection methods. The current onsite visits, which are laborious and costly, have become unsustainable due to the sheer scale of wind plants across the country. This study proposes a novel data-centric approach integrating semantic segmentation and GIS to obtain instance-level predictions of wind plants by using free orbital satellite images. Additionally, we introduce a new annotation pattern, which includes wind turbines and their shadows, leading to a larger object size. The elaboration of data collection used the panchromatic band of the China–Brazil Earth Resources Satellite (CBERS) 4A, with a 2-m spatial resolution, comprising 21 CBERS 4A scenes and more than 5000 wind plants annotated manually. This database has 5021 patches, each with 128 × 128 spatial dimensions. The deep learning model comparison involved evaluating six architectures and three backbones, totaling 15 models. The sliding windows approach allowed us to classify large areas, considering different pass values to obtain a balance between performance and computational time. The main results from this study include: (1) the LinkNet architecture with the Efficient-Net-B7 backbone was the best model, achieving an intersection over union score of 71%; (2) the use of smaller stride values improves the recognition process of large areas but increases computational power, and (3) the conversion of raster to polygon in GIS platforms leads to highly accurate instance-level predictions. This entire pipeline can be easily applied for mapping wind plants in Brazil and be expanded to other regions worldwide. With this approach, we aim to provide a cost-effective and efficient solution for inspecting and monitoring wind plants, contributing to the sustainability of the wind energy sector in Brazil and beyond.Faculdade de Tecnologia (FT)Departamento de Engenharia Elétrica (FT ENE)Instituto de Ciências Humanas (ICH)Departamento de Geografia (ICH GEA)Instituto de Ciências Exatas (IE)Departamento de Ciência da Computação (IE CIC