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A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis

  • Deliang Chen , Taotao Cheng , Yanyan Lu , Jianbo Xiao , Chen Ji EMAIL logo , Siyu Hong , Qizhi Zhuang and Liang Cheng
Published/Copyright: June 14, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

The wind power industry is increasing worldwide every year. Thus, obtaining timely and detailed information on wind farms’ number and spatial distribution is critical for quantitatively estimating wind energy utilization and for planning the construction of new wind farms. Therefore, this study proposes a method for quickly identifying wind farms in a large-scale area. Given that wind farms mainly comprise individual objects such as wind turbines and substations, we labeled sample images of wind turbines and substations on a global scale. Then, these sample images are used to train a target recognition model and an object classification model and detect the specific locations of wind turbines and substations in the study area. Additionally, we deeply analyzed the location features of the wind turbines and further improved the recognition accuracy based on these known features using geographic constraints. Based on the location information of wind turbines and substations, a clustering model organizes them effectively into complete wind farms. A comprehensive evaluation of the clustering model verifies its scientific validity and reliability. Specifically, this framework was systematically tested throughout Vietnam with remarkable results, using high-resolution historical images provided by Google Earth. Indeed, our framework achieved 90.45% recall and 95.73% accuracy for wind turbines and 81.37% recall and 78.96% accuracy for substations. Finally, we successfully obtained the spatial location and distribution of 15 completed wind farms, demonstrating that the proposed scheme can quickly and accurately identify wind power plants in a large-scale area, which supports wind power management and energy utilization planning.

Keywords: wind farms; deep learning; GIS spatial analysis; remote sensing; object detection; spatial clustering

1 Introduction

With the proposal of a carbon neutrality goal and the growing global energy demand, wind energy, as a clean and harmless renewable energy source, is increasingly emphasized by countries worldwide for its natural green and low carbon emission characteristics [1,2,3]. As of 2022, the global total installed capacity of wind energy is 906 GW, adding 78 GW compared to 2021, a year-on-year growth of 9%, with this growth rate expected to grow [4]. Therefore, obtaining timely information on the number and spatial distribution of wind turbines will provide important data to support governments in formulating sustainable energy policies, making investment decisions, and protecting the environment and climate. Such information will help promote the development of the renewable energy sector and reduce the dependence on fossil fuels, thus contributing to achieve sustainability goals regarding energy and the environment. Nevertheless, the spatial distribution of wind farms is challenging to obtain promptly due to their short construction period [5]. Due to the rapid growth rate of installed wind energy capacity, the use of manual surveys is not only laborious but also inefficient. Therefore, the development of an automatic and fast method for detecting wind turbines is urgently needed.

With the continuous development of remote sensing technology, extracting targets from remotely sensed images is possible [6], providing valuable and timely information about wind farms’ spatial and spectral attributes to support detection and monitoring tasks. Wind farms mainly comprise wind turbines, so the main goal is to identify wind turbines. Chen and Yue used a traditional FM saliency detection method for better extracting the regions of interest of windmill targets in the early stage of network training while removing irrelevant salient regions in the image to reduce the degree of interference of background information on the model. This method obtained high accuracy and recall [7]. Mandroux et al. developed an automated wind turbine detector based on an inverse method for low-resolution satellite images, utilizing wind turbine shadows and hub geometry [8]. Manso-Callejo et al. introduced a two-iteration generation based on a semantic segmentation model for wind turbine detection [9]. There are few studies on the identification and distribution of wind turbines over a wide area using remote sensing images, which employ a single remote sensing object identification method. Given that wind turbines belong to the targets in the wide-area range, in recent years, wide-area object detection has been applied to targets such as airplanes, dams, and solar photovoltaic panels [10,11,12]. For detecting targets from a wide area range, most researchers extract candidate regions for target detection [13]. For instance, Zeng et al. used a global land cover dataset to reduce the possible areas of airport candidate regions to 0.56% of the total area [14]. Fang et al. successfully applied the ResNet-50 model, which is based on a convolutional neural network framework, to achieve high accuracy (91.45%) in global-scale reservoir identification in Landsat 8 imagery [15]. Yu et al. developed the DeepSolar deep learning framework for recognizing the location and size of solar photovoltaic (PV) panels and constructed a high-fidelity solar deployment database [16]. Balaniuk et al. trained a full convolutional neural network on Google Colab using the Google Earth Engine platform and the TensorFlow 2 API to automatically identify and classify open pit mines and mine tailings dams across Brazil. Some scholars have used spatial analysis and image classification models to extract candidate regions [17]. For example, Li et al. relied on spatial analysis and the ResNet-101 model to detect unknown airports efficiently and accurately over a wide range [18,19]. Candidate region extraction methods have effective results in narrowing the search area and improving the search speed for large-range object detection.

In recent years, with the rapid development of deep learning methods in artificial intelligence, especially in computer vision, high-resolution remote sensing image analysis has ushered in a new opportunity. Compared with traditional machine learning methods, deep learning based on convolutional neural networks (CNNs) [20] has excellent feature extraction capability and accuracy. This makes deep learning methods show great potential for application in land use classification, object recognition, and object extraction at the regional scale. At the same time, considering the characteristics of blurred target features in remote sensing images and the background complexity, most researchers employ deep learning methods and achieve better accuracy and robustness. While CNN-based object detection has not been extensively applied to identify wind turbines, it has proven successful in various other object recognition tasks within remote sensing. Notable applications include detecting buildings, vehicles, airports, dams, and precision agriculture [21,22,23,24,25]. Hu et al. proposed a new DAGL-Faster algorithm (Adaptive Globally-Locally Aligned Faster R-CNN), which enhances the detection of vehicles in rainy weather [26]. Qi et al. tested the TripleSAT dataset by utilizing the single-stage detection model RetinaNet to detect storage tanks and airplanes in the dataset [27,28]. Their test results showed that RetinaNet had an average accuracy of 96.55 and 92.80% on storage tanks and airplanes, respectively.

Due to the background complexity of a remote sensing image, false detections are inevitable using deep learning methods. Thus, post-processing after detection must be improved to increase the object detection accuracy. Sun and Wu successfully applied a geospatial analysis approach for dam detection, which effectively reduced the number of false detections by 50%, resulting in a 9% improvement in recognition accuracy from 78.6 to 87.6%. The recall rate decreased slightly by 0.5% from 87 to 86.5% [29]. Spatial analysis using geographic data can help us analyze objects’ characteristics on the ground and their relationships, solve complex positioning problems, and provide new perspectives for decision-making.

This study develops a wind farm detection framework that combines remote sensing object recognition and GIS spatial analysis to extract wind farms from high-resolution remote sensing images from a large area perspective. The objectives are: (1) Combine the detection and classification models from deep learning to give the best model. (2) Combine the deep learning model with geospatial analysis to further optimize the detection results from deep learning. The above study provides data support for researchers to assess regional wind energy utilization and plan the layout of future Wind Farms. Meanwhile, the proposed method provides fast, automatic, low-cost wind farm regulation detection, especially in areas where economic conditions hinder ground-based monitoring.

2 Materials and methods

2.1 Research area

This study uses the whole area of Vietnam (latitude 8°10′–23°24′N, longitude 102°09′–109°30′E), which covers an area of 329,556 km2 (Figure 1). The main reason for choosing Vietnam as the study area is because it has a 3,000 km long coastline with relatively rich wind energy resources, with average annual wind speeds of up to 8.0 m/s or more in some areas. Additionally, 8.6% of the country’s land area is suitable for wind power generation, including the inland area and sea islands [30]. According to the World Bank, the theoretical reserves of wind energy resources in Vietnam are 2,099 GW, of which areas such as central, south-central, and southern Vietnam have great potential for development, especially in Ninh Thuan and Binh Thuan provinces. In recent years, Vietnam’s new energy sector has been developing rapidly, and the National Strategy for Green Growth 2021–2030 Toward 2050, published by the Vietnamese government, has set a target of 15–20% renewable energy share of total energy supply by 2025 [31]. In addition, we obtained administrative boundary vector data for the study area from the Global Administrative Boundaries Database. Mean wind speed data for the study area at a height of 50 m were obtained from the Global Wind Atlas, a dataset that covers the entire decadal period from 2008 to 2017. The European Space Agency provided us with 10 m resolution land use data, which contains 11 different land use types. We also obtained 30 m resolution digital elevation model (DEM) data for the study area from the U.S. Geological Survey. OpenStreetMap (OSM) data were obtained from OSM and Google tile data (17 levels) covering the study area were obtained totaling 4.6 million from Google Maps. Detailed data sources and data forms can be found in Table 1.

Figure 1 Experimental study area in Vietnam.
Figure 1

Experimental study area in Vietnam.

Table 1

Data sources

Datasets Spatial resolution (m) Time Sources URL
Windspeed 10 2008–2017 Global wind atlas https://globalwindatlas.info/en
World cover 10 2020 European space agency https://viewer.esa-worldcover.org/worldcover/
SRTM1 30 2022 United States geological survey https://www.usgs.gov/
Tiles 1.2 (level 17) 2023 Google Earth http://mt1.google.com/vt/lyrs=s&hl=zh-CN&x={}&y={}&z={}&s=Gali
Administrative boundaries — — GADM organization https://gadm.org/
OSM — 2023 OSM https://www.openstreetmap.org

2.2 Method

Figure 2 presents the proposed research framework, which is divided into three main parts: (1) Constructing remote sensing sample dataset, (2) wind turbine facility identification model construction, and (3) geospatial analysis and clustering of model detection results. It is worth noting that the resolution of the tile image is usually 256 × 256, and when the spatial resolution is high, each tile’s field of view range is small. In order to expand the field of view of the image and facilitate the subsequent validation work, we stitched the 17 levels of tiles in the study area according to a 3 × 3 size. This way, the spliced image has a wider field of view, reducing the original 4.6 million images to 510,000 and improving the model’s processing efficiency.

Figure 2 Wind farm detection framework.
Figure 2

Wind farm detection framework.

2.2.1 Constructing remote sensing sample dataset

In order to construct the proposed model, we used the QGIS (https://qgis.org/) software to annotate 2,000 wind turbine samples and 500 substation samples as well as 1,000 background images without wind turbines and substations globally, as shown in Figure 3 (annotated samples) and Figure 4 (global distribution). After the labeling was completed, the labeled boxes were exported to a GeoJson format file, and the images containing the location of each labeled box were downloaded according to the labeled file for model training. These images were divided into a training, validation, and test set based on a 6:2:2 ratio, limited by the number of labeled samples. Note that the samples must be augmented to train a model to enhance its generalization ability. Using the image processing method to transform the sample images into geometric space and color space can enrich the sample types and expand the size of the sample set. Table 2 presents the specific transformation method. Through the expansion, we increase the original data to 10 times the original.

Figure 3 Sample annotations for wind power facility samples.
Figure 3

Sample annotations for wind power facility samples.

Figure 4 Sample distribution of wind power facilities.
Figure 4

Sample distribution of wind power facilities.

Table 2

Image augmentation methods

Type Method Parameters Multiplier
Rotating 30°, 60°, 90° 3
Geometric augmentation Flip Vertical direction 2
Zoom 40, 80% 2
Brightness value Random 0.5–1.5 times 1
Color augmentation Contrast ratio Random 0.5–1.5 times 1
Gaussian noise — 1
Figure 10

2.2.2 Wind turbine facility identification model construction

At present, deep learning based target detection methods are mainly divided into four categories based on region proposal, object regression, anchor-free, and transformer. We selected typical methods in each category to train our detection model.

First, in the region proposal based method, we used Faster R-CNN [32] to train our model. Faster R-CNN extracts features from remote sensing images by CNN and introduces a region proposal network (RPN) to generate candidate target frames. The RPN extracts candidate frames on the image by sliding windows, calculates the probability that each frame contains a target, and then passes these candidate frames to the classifier and bounding box regressor for further processing.

Second, in the transformer-based method, we chose DETR [33] to train our model. The DETR model adopts the transformer architecture, which transforms the object detection task into an ensemble prediction problem, discarding the steps of anchor frames and non-maximum suppression that are traditionally used in object detection. the architecture of DETR consists of an encoder and a decoder, which, through the mechanism of attention, can effectively capture the global contextual information of the image, providing an end-to-end solution for remote sensing object recognition.

In the anchor-free based method, we choose Centernet [34] to train our model. Centernet discards the practice of presetting a large number of anchors in traditional object detection by directly predicting the centroid, width, and height of the bounding box, and the category of the object. The detector needs to use non-maximal suppression (NMS) to remove redundant bounding boxes in the post-processing stage, making the model not strictly end-to-end trained in the inference stage.

For the object regression based method, we chose YOLOv5 [35] to train our model. YOLOv5 is a real-time object detection model that directly predicts multiple objects in an image through a single forward propagation. The introduction of multi-scale detection and the use of CSPDarknet53 as the backbone network improve the learning ability of the model. Mosaic data enhancement techniques and lightweight design allow for better generalization while maintaining high accuracy.

Object detection over a large area usually produces a large number of false detections, so we use an object classification model to process the results of the detection model to improve the accuracy of the detection. We chose the EfficientNet [36] model, which was proposed by Google in 2019, to achieve more efficient model design through composite tuning of depth, width, and resolution. Using depth-separable convolution, efficient modules, and AutoML techniques, EfficientNet achieves superior performance in image classification tasks while reducing the number of parameters and computational cost.

In order to evaluate the effectiveness of our model training, we used accuracy and recall as the evaluation metrics, defined as follows:

(1) R = TP TP + FN ,

(2) P = TP TP + FP = TP N ,

where R represents the recall rate, P is the accuracy rate, TP denotes the number of wind turbines or substations correctly recognized by the model, FP is the number of false detections, N is the number of all wind turbines or substations detected, and FN is the number of missed detections.

All models were trained and validated on a workstation equipped with an Intel Core i9-9900K processor and an NVIDIA GeForce RTX 2080Ti graphics card with 32 GB of RAM and a 1.8 TB solid state drive. Table 3 provides some key parameters during model training and validation.

Table 3

Hyperparameters for model training

Model Faster R-CNN DETR CenterNet YOLOv5 EfficientNet
Backbone ResNet50 ResNet50 Hourglass CSPDarknet-53 MBConv
Batch size 16 16 16 16 8
Learning rate 0.01 0.01 0.01 0.01 0.001
Learning rate decay 0.0001 0.0001 0.0001 0.0001 0.00001
Optimizer SGD SGD SGD SGD SGD
Momentum 0.9 0.9 0.9 0.9 0.9
Epoch 200 200 200 200 200

2.2.3 Geospatial analysis

The aim of introducing geospatial analysis is to optimize the detection results further. Thus, we converted the pixel coordinates of the detection frame of the deep learning model into geographic coordinates to obtain the spatial location of the detection results. This process involves four key steps:

  1. Based on the wind farm siting criteria and the actual construction situation [37], we determined the land cover types of the wind turbine and substation construction sites. Specifically, we defined woodland, shrubs, grassland, cropland, bare ground/sparse vegetation, and moss/lichen as potential wind farm construction areas based on the ESA land cover criteria. We obtained the potential wind farm areas in the study area by eliminating the ineligible land cover types.

  2. From the DEM data of the study area, we calculated the slope of the entire area. Following wind farm construction standards, we determined the maximum construction slope to be 6°. To ensure the robustness of the results, we selected 10° as the maximum construction slope, thus excluding non-compliant areas and obtaining potential wind farm areas.

  3. Based on the information on nature reserves and residential areas in the OSM data of the study area, we further screened out the areas that meet the criteria for wind power plant construction.

  4. Wind speed is one of the important factors for wind farm construction. Only areas with abundant wind resources are suitable for constructing wind power plants. Based on the annual average wind speed data of the study area, we excluded the areas with annual average wind speed less than 3 m/s as shown in Figure 5 to get the potential wind farm areas.

The results obtained from the above analysis are vector data, so the potential wind farm areas in the whole study area can be obtained by superimposing these results. The model detection results were vectorially superimposed with the spatial analysis results, and those detection box locations that did not intersect with the spatial analysis results were considered erroneous detection results. All geospatial analyses were conducted in the World Geodetic System -1984 (WGS_1984).

Figure 5 Areas with wind speeds greater than 3 m/s at 50 m height in the study area.
Figure 5

Areas with wind speeds greater than 3 m/s at 50 m height in the study area.

2.2.4 Clustering model and evaluation metrics

Clustering aims to correlate the detected single group of wind turbines, thus forming a wind farm. This section employs Density-based spatial clustering of applications with noise (DBSCAN) [38], which is a density-based clustering algorithm that finds data points with sufficient density, divides them into clusters, and efficiently handles noisy data points. Unlike traditional clustering algorithms (e.g., K-means), DBSCAN does not require the number of clusters to be determined and can discover clusters automatically. The algorithm is based on two important parameters: the radius ε (epsilon) and the minimum number of data points (MinPts). DBSCAN starts from a core point and explores the data points in its ε-neighborhood, and a cluster is formed if at least MinPts data points are found in the ε-neighborhood. By scanning all data points step by step, DBSCAN categorizes the data points into core, boundary, and noise points to form independent clusters. This feature makes DBSCAN perform well in dealing with datasets with noise and anomalies, and it is more advantageous in discovering clusters of irregular shape and size. Figure 6 illustrates the schematic diagram of the DBSCAN algorithm.

Figure 6 DBSCAN algorithm schematic.
Figure 6

DBSCAN algorithm schematic.

In order to evaluate the clustering results of the DBSCAN algorithm more intuitively, we evaluate our clustering model using the Silhouette Score [39].

The silhouette coefficient is the most widely used evaluation parameter in classification algorithms. The silhouette coefficients of the sample points are calculated as shown in equation (3).

(3) S ( i ) = b ( i ) − a ( i ) max { a ( i ) , b ( i ) } ,

where a ( i ) represents the average distance between sample point i and other targets between the same clusters, and b ( i ) represents the minimum average distance between sample point i and targets between other clusters, and the formula is shown in equations (4) and (5).

(4) a ( i ) = 1 n − 1 ∑ i = 1 , j ≠ i n distance ( i , j ) ,

(5) b ( i ) = min 1 n ∑ i = 1 n distance ( i , j ) .

In equation (4), j represents other sample points in the same class as sample i , and “distance” represents the distance between i and j . In equation (5), j represents the sample point in the other sample class with sample i , and “distance” represents the distance between i and j . Based on equations (4) and (5), equation (3) can be rewritten as equation (6).

(6) S ( i ) = 1 − a ( i ) / b ( i ) , 0 , b ( i ) / a ( i ) − 1 , a ( i ) < b ( i ) a ( i ) = b ( i ) a ( i ) > b ( i ) ,

The range of the silhouette coefficients is (−1, 1), where a value closer to 1 means that the sample is very similar to a sample in this cluster and not to a sample in another cluster. The silhouette coefficient is negative if the sample point is more similar to a sample outside the cluster. Otherwise, the contour coefficient is 0, which means that the samples in the two clusters are equally similar, and thus, the two clusters should be combined.

Since a wind farm contains more than one wind turbine, we set the minimum number of neighbors (MinPts) to 2. To determine the appropriate neighbor radius (ε), we select four sets of distances according to the equal spacing, which are 5, 10, 15, and 20 km, respectively. Calculating the contour coefficients, we obtain the clustering effect under different ε values, as reported in Table 4. Specifically, the contour coefficient is 0.7726 when ε is 5 km. As ε increases to 10 km, the contour coefficient improves to 0.8559, which indicates that the clustering effect is better at 10 km compared to 5 km. However, when ε is further increased to 15 and 20 km, the contour coefficient remains at 0.8559, implying that the clustering effect is similar in both cases. Considering the clustering effect and computational cost, we set ε = 10 km as the final neighbor radius. This adjustment considers the contour coefficients at different neighbor radii to ensure our clustering parameters achieve better clustering effects while maintaining efficiency.

Table 4

Silhouette coefficient scores for different distance thresholds

MinPts Epsilon Silhouette score
2 5 0.7726
10 0.8559
15 0.8559
20 0.8559

3 Results

3.1 Model detection of wind power facilities

We enhanced the divided dataset according to the image enhancement method described in Section 2.2.1. The obtained training images include 12,000 wind turbines and 8,000 validation images, as well as 3,000 training images and 2,000 validation images of substations. These augmented datasets were used to train, validate, and test the models presented in Section 2.2.2. Table 5 presents the metrics of the model on the test set, revealing that YOLOv5l is the best model. Therefore, we selected YOLOv5l as the benchmark model for the recognition model.

Table 5

Model validation index

Model Objective type Precision (%) Recall (%)
Faster R-CNN Wind turbine 55.4 83.2
Substation 60.3 71.2
DETR Wind turbine 83.6 93.7
Substation 78.4 85.1
CenterNet Wind turbine 96.8 90.5
Substation 82.7 89.6
YOLOv5l Wind turbine 96.2 95.5
Substation 75.8 95.3

Additionally, we prepared 6,000 background images for classification model training and 4,000 background images for classification model validation. We applied the wind turbine and substation sample images to the YOLOv5l model for training. Simultaneously, we used the wind turbine, substation, and background images to train the classification model, with Figures 7 and 8 illustrating the loss curves of the detection model based on YOLOv5l and EfficientNetB4.

Figure 7 Training loss and accuracy based on YOLOv5l.
Figure 7

Training loss and accuracy based on YOLOv5l.

Figure 8 Training loss and accuracy based on EfficientNetB4.
Figure 8

Training loss and accuracy based on EfficientNetB4.

First, we applied the detection model to the study area and obtained 4,278 detection frames for wind turbines and 5,263 for substations. Through visual interpretation, we successfully recalled 204 wind turbines and 2,731 substations. However, there were also 14 missing wind turbines and 289 missing substations. Figure 9 illustrates some unrecalled wind turbines and substations (within the green box, the ones not recalled due to incomplete target information, and within the red box, those not recalled due to insufficient training samples). In this phase, the recall rates for wind turbines and substations are 92.72 and 83.75%, respectively, and the recall rate is calculated as the ratio of the number of wind turbines correctly detected by the model to the number of wind turbines in the OSM visual search dataset. The accuracy rates for wind turbines and substations were 11.31 and 51.89%, respectively, and were calculated as the ratio of the number of wind turbines correctly identified by the model to the number of frames identified.

Figure 9 Model missed detection (green boxes indicate misses due to insufficient feature learning, and blue boxes indicate misses due to incomplete goals).
Figure 9

Model missed detection (green boxes indicate misses due to insufficient feature learning, and blue boxes indicate misses due to incomplete goals).

Subsequently, we input the recognition results from the detection model into the classification model, in which we acquired 471 wind turbines and 4,156 substations. Among them, the recall rates of wind turbines and substations were 90.45 and 81.37%, respectively. The main reason for the slight decrease in model recall after classification is that some correctly identified results were misclassified. However, the accuracy rates for wind turbines and substations were 90.22 and 78.96%, respectively, highlighting that although the classification model loses some performance in terms of recall, it improves tremendously in terms of recognition accuracy.

3.2 Geospatial analysis

Since substations are not a feature element specific to wind farms, we only considered wind turbines for the geospatial analysis. Based on the thresholds set in Section 2.2.3, we obtained the potential wind turbines in the study area, and through geospatial analysis, we finally retained 465 wind turbines. However, we also noted instances where we mistakenly deleted six groups of wind turbines, which mainly stemmed from the lack of precision in the land use data due to deleting those wind turbines closer to water bodies. Therefore, more precise data should improve our identification accuracy.

The entire study area covers 331,700 km2. In our proposed framework, we successfully recalled 199 sets of wind turbines. Although 220 wind turbines and one substation are recorded in the OSM dataset, our framework discovered 266 sets of wind turbines that were not recorded in the OSM dataset. Overall, the wind turbines were identified with a recall rate of 90.45%, and the accuracy increased from 90.45 to 95.73%. The whole detection process required 18 h, suggesting that the proposed framework can quickly and accurately update the construction of wind turbines in a large area. Furthermore, this demonstrates that the developed method improves the accuracy of wind turbine identification and provides more accurate data support for wind farm planning.

3.3 Spatial clustering

A wind farm is a power station comprising a group of wind turbines or a cluster [40], where the most critical building blocks include wind turbines and substations. Our identification framework successfully determined the geospatial locations of wind turbines and substations in the study area. Subsequently, we applied the DBSCAN clustering algorithm to cluster individual wind turbines and grouped the spatially similar wind turbines into one category. Wind turbines in the same category were grouped into the same wind farm. Then, we converted the point elements of the same category into face elements and kept the substations that intersected the face elements. This operation allowed the otherwise scattered wind turbines and substations to form a complete wind power plant. Through this clustering method, we identified 15 completed wind farms in the study area, with their distribution illustrated in Figure 10. The cluster analysis results present the distribution range of wind farms, which provides more accurate data support for land use planning and environmental assessment. This method clarifies the boundaries of wind farms and provides a more reliable basis for related decisions.

Figure 10 Results of the proposed framework detection.
Figure 10

Results of the proposed framework detection.

4 Discussion

4.1 Application of classification model

The aim of this study is to detect as many wind turbines and substations as possible. Therefore, in the detection model, the confidence threshold was set to 0.4 (i.e., the score of the detection frame is retained if it is greater than the confidence level). However, this also resulted in more non-wind turbines being incorrectly detected. Figure 11 illustrates some of the misidentified objects, such as (a)-1 misidentifying a turnoff on a road as a wind turbine, (a)-2 misidentifying a high voltage tower as a wind turbine, (a)-3 misidentifying an airplane as a wind turbine, (b)-1 mis-detecting the roof of a building as a wind turbine, (b)-2 misidentifying a piece of farmland as a wind turbine, and (b)-3 misidentifying a solar PV panel as a wind turbine. In order to reduce the number of misidentifications, we introduced a classification model based on EfficientNetB4 to classify objects into three categories: wind turbines, substations, and others. Our experiments demonstrated that the recognition efficiency and accuracy can be improved by further processing the detection results using the classification model. Using YOLOv5l for detection in more areas extends the classification model samples and improves accuracy.

Figure 11 False detection results: a(1), a(2), and a(3) false detection for wind turbine, and b(1), b(2), and b(3) false detection for substation.
Figure 11

False detection results: a(1), a(2), and a(3) false detection for wind turbine, and b(1), b(2), and b(3) false detection for substation.

Previous studies have focused on the characteristics of the network structure to improve the model’s accuracy by improving the network structure. However, these studies are often limited to small-scale regions and do not consider the diversity of inland object species and the complexity of backgrounds in large-scale regions. Hence, we introduced a classification model to increase recognition accuracy in large-scale regions. Although this reduced recall, the recognition accuracy significantly improved. Nevertheless, this approach is suitable for recognition tasks dealing with large-scale regions. In future research, the improved model will be integrated into the present framework for higher precision detection, aiming to improve its performance and adapt it to more complex real-world application scenarios.

4.2 Geospatial analysis results

Based on the wind farm siting and construction criteria [41], we processed the acquired wind speed data, land use data, and OSM data to form potential wind turbine regions under various threshold constraints. Among these potential areas, we obtained the final wind turbine locations and conducted cluster analysis based on their locations in the geospatial space to obtain 15 completed wind farms. A detailed record of the number of wind turbines and the number of substations contained in each wind farm are reported in Table 6, which highlights that each wind farm contains at least two wind turbines and one substation, which is consistent with the actual construction situation and again verifies the accuracy of our framework. The existence of multiple substations in some wind farms is because each substation can aggregate and step up the power generated by the wind turbines in the vicinity, thus shortening the transmission lines and reducing line losses. By counting the results of the geospatial analysis, we found that the geospatial analysis improved the accuracy of wind turbine identification by 5.51%. At the same time, it should be noted that the validity of geospatial analysis depends largely on the accuracy of geographic data. Therefore, higher resolution and more accurate products such as DEM and land cover can further enhance our method’s credibility. It is worth special attention that geospatial analysis is influenced by various inter-regional economic, cultural, and geographic factors that shape the topography and landscape and directly relate to the construction and distribution of wind farms. Therefore, when applying the proposed geospatial analysis method, it is imperative to fully consider these multiple factors to accurately capture the identification accuracy of wind turbines in different regions.

Table 6

Detailed statistics of wind farm identification results

Wind farm number Central longitude Central latitude Area (km2) Number of wind turbines (group) Number of substations (unit)
1 105.80 9.22 5.42 52 1
2 106.11 9.37 36.65 39 1
3 106.45 9.53 1.32 6 1
4 108.01 10.84 0.61 5 1
5 108.36 11.04 43.97 37 3
6 108.68 11.21 3.20 38 3
7 108.93 11.45 43.99 67 8
8 109.04 11.69 22.15 69 6
9 108.56 11.88 1.61 12 1
10 108.22 13.09 124.81 57 3
11 108.09 13.49 17.46 25 1
12 109.26 13.87 10.52 27 4
13 107.92 13.93 2.62 7 1
14 106.77 16.71 1.89 22 1
15 108.70 13.98 0.64 2 1
Total 465 36

Note: The center longitude and center latitude indicate the center of mass of the polygon surrounded by all wind turbines in each wind farm.

4.3 Comparison with other research

In this study, a wind farm detection framework based on deep learning and geospatial analysis is proposed. Chen et al. utilized the modified YOLOv3 model for wind turbine detection, which has an accuracy of 95% and a recall of 94% on the test set [42]. However, they have not yet applied the method to real geographic scenarios. We applied the proposed framework to the whole Vietnam land area and achieved 90.45% recall and 95.73% accuracy. Also, our detection framework found wind farms that were not recorded in the dataset, providing a strong addition to the data update. Putra and Wijayanto [43] implemented automatic palm tree detection based on Microsoft Bing Maps using YOLOv3, and the model achieved an accuracy and recall of 86.39 and 96.40%, respectively, on the test set. However, the model only estimated palm trees in the study area, but did not count its accuracy and recall in real scenarios. Due to the diverse components of wind farms, the detection of wind farms is more complex compared to the detection of a single palm tree. Therefore, we added geospatial constraints to the detection results of the deep learning model to improve the accuracy of wind turbine detection by 5.51%. Jing et al. used deep learning and geospatial analysis to achieve dam detection in three regions of Japan, and the accuracy and recall of the detection results of this method in the three regions were 80.0 and 91.06%, respectively [13]. However, the dam detection is performed in the water system, while the wind farm detection is performed in the land area. Due to the high similarity between the geometry of wind turbines and land features such as high voltage towers, more false detection results may occur in the land area. To solve this problem, we introduce a target classification model in the deep learning detection phase, which improves the accuracy of wind turbine and substation detection results by 79.14 and 27.07%, respectively.

4.4 Limitations of the proposed framework

As a promising method for analyzing remote sensing images, deep learning provides a wide range of research possibilities. Hence, this study combined target detection techniques with spatial analysis to explore the distribution of wind farms over a large area. However, despite the significant potential of our proposed framework, there are some limitations. Indeed, during the identification process, our framework may miss some small wind turbines, mainly because we did not fully consider the characteristics of small wind turbines in our sample set. Meanwhile, our sample data are mainly set in the context of land areas, ignoring identifying offshore wind turbines. In future research, we will consider incorporating the improved algorithm into our framework to identify offshore wind farms accurately.

5 Conclusion

The main motivation for this study stems from the fact that current research on wind farm detection on a large regional scale is relatively limited. Previous studies have focused on wind farm siting planning and the wind turbines themselves, with little in-depth exploration of the distribution characteristics of wind farms on a large regional scale. Spurred by these limitations, this study integrated convolutional neural network and spatial analysis, aiming to detect wind farms quickly and accurately from high-resolution remote sensing images.

The main steps of this research method are as follows: (1) On a global scale, we labeled the samples using QGIS software and performed the augmentation and generalization process according to the sample features. (2) In the training and application phase of the deep learning model, we combined the detection and the classification models to obtain preliminary detection results. Combining these two models improved the detection accuracy of wind turbines and substations by 78.91 and 27.07%, respectively. (3) We converted the detection results to geographic coordinates and combined them with geographic information data for comprehensive analysis, further improving the detection accuracy. Through cluster analysis, we obtained spatial distribution information from 15 completed wind power plants. Additionally, our method could detect and supplement newly built wind turbines not recorded in the dataset.

Future studies will extend this method to a global scale to enable a wider range of wind farm detection and further deepen our understanding of the distribution characteristics of wind farms on a large regional scale.

  1. Funding information: This study was financially supported by National Natural Science Foundation of China (No. 42301517 and No. 41601497).

  2. Author contributions: Chen Ji, Qizhi Zhuang and Liang Cheng: editing data curation, Supervision. Deliang Chen, Taotao Cheng: writing-original draft preparation. Yanyan Lu, Jianbo Xiao and Siyu Hong: conceived the experiment. Taotao Cheng: analysed the results. All authors reviewed the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-12-08
Revised: 2024-02-16
Accepted: 2024-04-13
Published Online: 2024-06-14

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0645
Keywords for this article
wind farms; deep learning; GIS spatial analysis; remote sensing; object detection; spatial clustering
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  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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