Abstract
Updating the mapping of wind turbines farms—found in constant expansion—is important to predict energy production or to minimize the risk of these infrastructures during storms. This geoinformation is not usually provided by public mapping agencies, and the alternative sources are usually consortiums or individuals interested in mapping and study. However, they do not offer metadata or genealogy, and their quality is unknown. This article presents a methodology oriented to optimize the recognition and extraction of features (wind turbines) using hybrid architectures of semantic segmentation. The aim is to characterize the quality of these datasets and help to improve and update them automatically at a large-scale. To this end, we intend to evaluate the capacity of hybrid semantic segmentation networks trained to extract features representing wind turbines from high-resolution images and to characterize the positional accuracy and completeness of a dataset whose genealogy and quality are unknown. We built a training dataset composed of 5140 tiles of aerial images and their cartography to train six different neural network architectures. The networks were evaluated on five test areas (covering 520 km2 of the Spanish territory) to identify the best segmentation architecture (in our case, LinkNet as base architecture and EfficientNet-b3 as the backbone). This hybrid segmentation model allowed us to characterize the completeness—both by commission and by omission—of the available georeferenced wind turbine dataset, as well as its geometric quality.
Highlights
The representation of geographic elements in vector models is generally produced using various capturing and digitization technologies in which the human factor intervenes, that is, they are not free from errors
We propose a methodology for creating optimal Deep Learning-based solutions to extract the ground geometry of wind turbines by means of semantic segmentation using high-resolution aerial orthoimagery—for having them freely available, but we could have used high-resolution optical images obtained by remote sensing—and a methodology to evaluate the positional accuracy and the completeness of a dataset containing the geospatial objects
In [34], we studied the appropriateness of using hybrid segmentation networks for extracting complex geospatial elements and contrasted their performannccee iinnccoommppaarrisisoonntotostsatatete-o-of-ft-hthe-ea-ratrtsesgemgmenetnattaiotinonaracrhcihteitcetuctruerse, so,botabitnainginigmipmropvroemveemntesnitns pinerpfeorrfmoramnacencme emtreitcrsicosfo2f.72–.73–.35.%5%wwhehnencocmompparaerdedtotoththeeoorriiggiinnaallaarrcchhiitteeccttuurreess trained from ssccrraattcchh
Summary
The representation of geographic elements in vector models is generally produced using various capturing and digitization technologies in which the human factor intervenes, that is, they are not free from errors. One of the main drawbacks is related to the small study areas taken into account, featuring favorable scenarios (as pointed out in [29]) For this reason, in this article, we will explore the feasibility of training various segmentation models to extract and map wind turbines in optical high-resolution remote sensing orthorectified images. To address these challenges, we propose a methodology for creating optimal Deep Learning-based solutions to extract the ground geometry of wind turbines by means of semantic segmentation using high-resolution aerial orthoimagery—for having them freely available, but we could have used high-resolution optical images obtained by remote sensing—and a methodology to evaluate the positional accuracy and the completeness of a dataset containing the geospatial objects. The use of the best performing network, through the proposed methodology, allows to extract the features that represent wind turbines with few false-negatives and generate a set of data to use to compare and characterize the quality of another dataset of which the quality is unknown
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