Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier

Malof, Jordan M.; Bradbury, Kyle; Collins, Leslie M.; Newell, Richard G.; Serrano, Alexander; Wu, Hetian; Keene, Sam

doi:10.1109/icrera.2016.7884446

Cited by 25 publications

(11 citation statements)

References 17 publications

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“…Such a result is significantly higher than previous reports. [6][7][8]13 Furthermore, our performance evaluation guarantees far more robustness since their test sets were only obtained from one or two cities but ours are sampled from nationwide imagery. Mean relative error (MRE), the area-weighted relative error, is used to measure size estimation performance.…”

Section: Scalable Deep Learning Model For Solar Panel Identificationmentioning

confidence: 99%

DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

Wang

Majumdar

et al. 2018

Joule

247

130

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We developed an accurate deep learning framework to automatically localize solar photovoltaic panels from satellite imagery and estimate their sizes. We used it to construct a comprehensive and publicly available solar installation database of the contiguous US. We demonstrated its value by identifying key environmental and socioeconomic factors correlating with solar deployment, such as income and education. We also found that the solar deployment density can be accurately estimated at the microscopic level with these factors using a novel predictive model.

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Section: Scalable Deep Learning Model For Solar Panel Identificationmentioning

confidence: 99%

DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

Wang

Majumdar

et al. 2018

Joule

247

130

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“…Satellite observations can offer detailed spectral and geospatial information for PV power plants identification. Researchers have recently mapped PV power plants on a regional and global scale using remote sensing images [24][25][26][27][28][29][30][31][32][33][34]. Some researchers build PV power plant datasets based on manual annotation and visual interpretation methods with remote sensing imagery [26,34].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning is widely used in the remote sensing community as a practical empirical approach for regression and classification [35,36]. Machine learning methods, such as random forest (RF), convolutional neural networks (CNN), and deep learning, have been applied to map PV panels or PV power plants with various remote sensing images from the regional to continental scale [24,25,[28][29][30]32,33]. Machine learning algorithms can model complex class signatures with high accuracy by accepting various input variables and not making assumptions about the data distribution [37].…”

Section: Introductionmentioning

confidence: 99%

Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China

et al. 2021

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Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.

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“…Under this category of data, large-scale aerial image processed as orthophotography is a useful source of information for many domains. To give some examples of the broad array of applications we can find, there have been systems developed for the detection of coastline changes [2], snow avalanches [3], fires [4], bodies in disaster sites [5], trees [6], seedlings [7], roofs [8], transmission towers [9,10], vehicles [11,12], photovoltaic arrays [13,14], vegetation and buildings [15]. We focus on road detection on aerial images being it an important subject, among other things, due to the need to constantly update road maps.…”

Section: Introductionmentioning

confidence: 99%

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

Castillo¹,

Díaz-Álvarez²,

Callejo³

et al. 2020

Preprint

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Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

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Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier

Cited by 25 publications

References 17 publications

DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

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