A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS
Abstract
:1. Introduction
- A novel data-centric approach is introduced, consisting of a new strategy of annotating wind plants (including the shadows and turbines), which increases the size of the objects, facilitating the recognition. In addition, this strategy allows the use of 2-m resolution images for small-object detection. Wind plants have a low representation in nadir images, but they present an extensive shadow due to their height, which facilitates indirect detection by deep learning methods.
- A novel semantic segmentation dataset is introduced, the first considering CBERS-4A images covering the entire Brazilian territory. CBERS-4A presents the advantages of being free of charge and having a 2-m resolution for the panchromatic band. Thus, the inference procedure is much faster by only considering one band.
- A novel semantic to instance segmentation conversion using geographic information system (GIS) software. The spatial distribution of wind farms, appearing sparsely without contacts between them, makes the polygonization of the semantic features create a specific identifier per instance, in addition to favoring the removal of noisy features based on the size of the objects.
2. Materials and Methods
2.1. Data
2.1.1. Study Area Selection and Image Acquisition
2.1.2. Annotations
2.1.3. Deep Learning Samples
2.2. Deep Learning Approach
2.3. Sliding Windows Approach for Classifying Large Areas
2.4. Semantic to Instance Conversion by Using GIS
2.5. Deep Learning Metrics
3. Results
3.1. Model Evaluation and Comparison
3.2. Sliding Window Results
3.3. Final GIS Representation
4. Discussion
4.1. Importance of the Application and Dataset
4.2. Significance of the Data-Centric Approach
- Detailed Object Assessment: By using semantic segmentation to delineate the features of wind turbines, we can gather more information about the object, allowing for a broader range of future studies, such as the proper identification of different stages of wind plant construction.
- Consistent Annotation Pattern: Our approach ensures a more consistent pattern in the annotations, leading to more accurate results. Point-based approaches can be unpredictable and reduce the model’s ability to generalize.
- Easy Conversion to Points: Our pipeline includes transforming predictions into vectors, making it easy to convert the segmentation results into points. However, the reverse application is not possible. For example, we have created a point shapefile for each target by using GIS software (Figure 9).
- Robust Dataset: Our annotation pattern is more understandable for other researchers and helps increase the robustness of the dataset. Point-based methods can be prone to error, but our approach avoids this problem by considering the entire object.
- Leveraging Shadows: Our approach leverages the shadow cast by wind turbines, which is often an undesired feature, as one of the main features for our annotations. By doing so, we reduce the possibility of predicting noisy features and improve the accuracy of the results.
4.3. Interpretation and Comparison of the Deep Learning Results
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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State | Location | # of Wind Plants | # of Patches | Train/Val/Test |
---|---|---|---|---|
BA | 42°40’48.852” W 14°4’17.174” S | 407 | 656 | Train |
BA | 41°27’37.008” W 11°51’22.643” S | 113 | 290 | Train |
BA | 41°15’58.126” W 11°2’6.711” S | 250 | 228 | Train |
BA | 42°35’57.95” W 14°24’38.101” S | 303 | 377 | Train |
BA | 41°23’53.288” W 10°31’20.718” S | 225 | 251 | Train |
BA | 40°42’49.748” W 7°40’6.644” S | 270 | 288 | Train |
CE | 39°42’39.391” W 3°4’52.63” S | 174 | 250 | Train |
CE | 39°19’57.904” W 3°16’45.851” S | 233 | 315 | Train |
PI | 41°32’16.008” W 8°39’0.225” S | 309 | 323 | Train |
RJ | 41°4’37.302” W 21°34’28.83” S | 18 | 45 | Train |
RN | 36°26’53.947” W 5°14’40.991” S | 203 | 285 | Train |
RN | 35°55’58.156” W 5°20’52.179” S | 818 | 836 | Train |
RS | 53°19’10.675” W 33°35’48.462” S | 305 | 340 | Train |
RS | 49°35’52.656” W 28°27’51.075” S | 60 | 60 | Train |
BA | 40°58’22.055” W 10°5’0.823” S | 113 | 124 | Validation |
PB | 36°43’49.184” W 6°58’1.276” S | 59 | 101 | Validation |
RS | 52°13’4.441” W 32°13’40.063” S | 32 | 32 | Validation |
PI | 40°37’23.185” W 7°59’46.973” S | 98 | 118 | Test |
RN | 36°12’37.606” W 5°44’52.836” S | 53 | 62 | Test |
RS | 52°21’33.646” W 32°25’18.929” S | 40 | 40 | Test |
RN | 37°2’11.031” W 5°7’59.359” S | 382 | - | SW test |
Metric | Equation |
---|---|
Overall accuracy | |
Precision | |
Recall | |
F-score | |
Intersection over union (IoU) |
Architecture | Backbone | Accuracy | Precision | Recall | F-score | IoU |
---|---|---|---|---|---|---|
DLv3+ | Eff-B7 | 99.58 | 79.61 | 79.77 | 79.69 | 66.24 |
X-101 | 99.57 | 78.01 | 79.96 | 78.97 | 65.25 | |
R-101 | 99.56 | 79.01 | 78.11 | 78.56 | 64.69 | |
U-Net | Eff-B7 | 99.63 | 83.13 | 80.31 | 81.69 | 69.05 |
X-101 | 99.63 | 85.17 | 77.60 | 81.21 | 68.36 | |
R-101 | 99.61 | 81.99 | 78.94 | 80.44 | 67.28 | |
LinkNet | Eff-B7 | 99.66 | 84.30 | 82.55 | 83.41 | 71.55 |
X-101 | 99.62 | 82.99 | 79.04 | 80.97 | 68.02 | |
R-101 | 99.62 | 82.39 | 79.74 | 81.04 | 68.13 | |
FPN | Eff-B7 | 99.59 | 80.28 | 79.20 | 79.73 | 66.30 |
X-101 | 99.58 | 79.11 | 79.39 | 79.25 | 65.63 | |
R-101 | 99.58 | 80.41 | 78.46 | 79.42 | 65.87 | |
U-Net++ | Eff-B7 | 99.64 | 83.86 | 80.74 | 82.27 | 69.88 |
X-101 | 99.63 | 85.17 | 77.60 | 81.21 | 68.36 | |
R-101 | 99.61 | 81.99 | 78.94 | 80.44 | 67.28 |
Architecture | Backbone | Training Period (s) | Inference Time (ms) |
---|---|---|---|
DLv3+ | Eff-B7 | 65 | 42.98 |
X-101 | 40 | 21.58 | |
R-101 | 23 | 14.50 | |
U-Net | Eff-B7 | 58 | 48.17 |
X-101 | 40 | 23.16 | |
R-101 | 28 | 16.44 | |
LinkNet | Eff-B7 | 62 | 44.44 |
X-101 | 38 | 22.82 | |
R-101 | 29 | 16.98 | |
FPN | Eff-B7 | 60 | 44.77 |
X-101 | 37 | 23.27 | |
R-101 | 27 | 16.81 | |
U-Net++ | Eff-B7 | 64 | 43.30 |
X-101 | 65 | 21.81 | |
R-101 | 48 | 16.62 |
Stride 16 | Stride 32 | Stride 64 | Stride 128 | |
---|---|---|---|---|
ROC AUC | 98.23 | 97.96 | 95.94 | 94.03 |
PR AUC | 87.22 | 85.41 | 82.27 | 71.68 |
IoU | 69.38 | 68.95 | 66.28 | 60.78 |
Mapping time (hr:min:sec) | 22:01:21 | 5:30:20 | 01:22:32 | 00:21:58 |
Metric | Result |
---|---|
True Positives | 369 |
False Positives | 37 |
False Negatives | 0 |
Accuracy | 90.88 |
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Share and Cite
de Carvalho, O.L.F.; de Carvalho Junior, O.A.; de Albuquerque, A.O.; Orlandi, A.G.; Hirata, I.; Borges, D.L.; Gomes, R.A.T.; Guimarães, R.F. A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS. Remote Sens. 2023, 15, 1240. https://doi.org/10.3390/rs15051240
de Carvalho OLF, de Carvalho Junior OA, de Albuquerque AO, Orlandi AG, Hirata I, Borges DL, Gomes RAT, Guimarães RF. A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS. Remote Sensing. 2023; 15(5):1240. https://doi.org/10.3390/rs15051240
Chicago/Turabian Stylede Carvalho, Osmar Luiz Ferreira, Osmar Abílio de Carvalho Junior, Anesmar Olino de Albuquerque, Alex Gois Orlandi, Issao Hirata, Díbio Leandro Borges, Roberto Arnaldo Trancoso Gomes, and Renato Fontes Guimarães. 2023. "A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS" Remote Sensing 15, no. 5: 1240. https://doi.org/10.3390/rs15051240
APA Stylede Carvalho, O. L. F., de Carvalho Junior, O. A., de Albuquerque, A. O., Orlandi, A. G., Hirata, I., Borges, D. L., Gomes, R. A. T., & Guimarães, R. F. (2023). A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS. Remote Sensing, 15(5), 1240. https://doi.org/10.3390/rs15051240