Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning
Contributions of This Work
- The first publicly available dataset of UAV imagery of very small (less than 100 Watts) SHS (Section 3). We collected, annotated, and openly shared the first UAV-based very small solar panel dataset with precise ground sampling distance and flight altitude. The dataset contains 423 images, 60 videos and 2019 annotated solar panel instances. The dataset contains annotations for training object detection or segmentation models.
- Evaluating the robustness and detection performance of deep learning object detection for solar PV UAV data (Section 5). We evaluate the performance of SHS detection performance with a U-Net architecture with a pre-trained ResNet50 backbone. We controlled for the data collection resolution (or 1/altitude): sampling every 10 m of altitude across an interval from 50 m–120 m. We controlled for the dimension of panel size by using 5 diverse solar PV panel sizes
- Cost/benefit analysis of UAV- and satellite-based solar PV mapping (Section 6). We estimate a cost-performance curve for comparing remote sensing based data collection for both UAV and satellite systems for direct comparison. We demonstrate that using the highest resolution satellite imagery currently available, very small SHS are hardly detectable; thus, even the highest-resolution commercially available satellite imagery does not present a viable solution for assessment of very small (less than 100 Watt) solar panel deployments.
- Case study in Rwanda illustrating the potential of drone-based solar panel detection for very small SHS installations (Section 7). By applying our models to drone data collected in the field in Rwanda, we demonstrate an example of the practical performance of using UAV imagery for solar panel detection. Comparing the results to our experiments with data collected under controlled conditions, we identified the two largest obstacles to achieving improved performance are the resolution of the imagery and the diversity of the training data.
2. Related Work
3. The SHS Drone Imagery Dataset
- Adequate ground sampling distance (GSD) range and granularity. Due to variations in factors such as hardware and elevation change, the GSD of drone imagery can vary significantly in practice. Therefore, we want our dataset to contain imagery with a range of image GSDs (shown in Table 1) that are sufficient to represent a variety of real-world conditions, as well as detect SHS.
- Diverse and representative solar panels. As solar panels can have different configurations affecting the visual appearance (polycrystal or monocrystal, size, aspect ratio), we chose our solar panels carefully so that they form a diverse and representative (in terms of power capacity) set (Table A1) of actual solar panels that would be deployed in developing countries.
- Fixed camera angle of 90 degrees and different flying speeds: To investigate the robustness of solar panel detection as well as data collection cost (that is correlated with flying speed), we want our dataset to have more than one flight speed.
Data Collection Process
4. Post-Processing and Metrics
5. Experiment #1: Solar Panel Detection Performance Using UAV Imagery
5.1. Detection Performance Comparison over Imagery Resolution
5.2. Detection Performance with Respect to Resolution Mismatch
5.3. Solar Panel Detection Performance with Respect to Flight Speed
6. Experiment #2: Cost Analysis of UAV-Based Solar Panel Detection and Comparison to Satellite Data
6.1. Cost Analysis: Methods
- The UAV is operated five days each week, six hours per day (assuming an eight-hour work day, and allowing two hours for local transportation and drone site setup).
- Each UAV operator rents one car and one UAV; the upfront cost of the UAV is amortized over the expected useful life of the UAV.
- Total UAV image collection time is capped at three months, but multiple pilots (each with their own UAV) may be hired if necessary to complete the collection.
- UAV lifetime is assumed to be 800 flight hours (estimate from consultation with a UAV manufacturer).
- A sufficient quantity of UAV batteries is purchased for operating for a full day.
- The probability of inclement weather is fixed at 20%. and no operation would be carried out under those conditions.
- Legal and permit: The legal and permitting cost of getting the credentials for flying in a certain country or region. As an example, in the US, although state laws may vary, at a federal level, flying for non-hobbyist purposes (class G airspace, below 120 m) requires the drone pilot to have a Part 107 permit, which requires payment of a fee as well as successful completion of a knowledge test. The legal and permitting costs are inherently location-dependent, and cost variation may be large.
- Transportation: The total transportation cost for the drone operator. For the purposes of our estimate here, we assume one drone pilot (thus, total data collection time is a linear function of area covered). Note that this category includes travel to and from the data collection location, which is assumed to include air travel, local car rental, car insurance, fuel costs, and (when the operational crew is foreign to the language) a translation service.
- Labor-related expenses: Umbrella category of all labor-related costs including wages and fringe benefits or overhead paid to the drone pilot, as well as boarding and hotel costs.
- Drone-related expenses: Umbrella category of all drone-related costs including purchase of the drone, batteries, and camera (if not included with the drone).
6.2. Cost Analysis: Result
7. Experiment #3: Case Study: Rwanda SHS Detection Using Drone Imagery
Case Study: Result
Data Availability Statement
Conflicts of Interest
|SDG||Sustainable Development Goal|
|UAV||Unmanned Aerial Vehicles|
|US||United States of America|
|IoU||Intersection over Union|
|CNN||Convolutional neural networks|
|SHS||Solar Home Systems|
|GSD||Ground Sampling Distance|
Appendix A.1. Data Collection Pipeline and Detailed Specifications of Solar Panels Used
|Brand||X-Crystalline||L (mm)||W (mm)||Aspect_Ratio||Area (dm)||T (mm)||Power (W)||Voltage (V)|
Appendix A.2. Satellite View for Small SHS
Appendix A.3. Algorithm and Performance Details
- Pretraining: As labeled drone datasets, especially the ones including solar panels, are extremely scarce, we use satellite imagery containing solar panels (same target as our task, but larger in size) to pre-train our network before fine-tuning it with the UAV imagery data we collected. This practice increased performance over fine-tuning from ImageNet pre-trained weights alone, IoU improved from 48% to 70%).
- Scoring pipeline: We illustrate the process of scoring in Figure A2. Note that in detection problems, the concept of true negatives is not defined. This is also precision and recall (and therefore precision-recall curves) are used for performance evaluation rather than ROC curves.
- Simulating satellite resolution imagery: In Section 5, we downsampled our UAV imagery to simulate satellite imagery resolution. To make sure the imagery has an effective resolution that is the same as satellite imagery, while keeping the same overall image dimensions so that our model has the same number of parameters, we follow the downsampling process with an up-sampling procedure using bi-linear interpolation (using OpenCV’s resizing function). The effective resolution remains at the satellite imagery level (30 cm/pixel), but the input size of each image into the convolutional neural network remains the same.
- Hyper-parameter tuning: Across the different resolutions of training data, we kept all hyperparameters constant except for the class weight of the positive class (due to the largely uneven distribution of solar panels and background imagery across changes in GSD). After tuning the other hyperparameters like learning rate and the model architecture once for all flight heights, we tuned the positive class weight individually for each of our image resolution groups due to the inherent difference in the ratio of number of solar panel pixels within each image.
- Precision-recall curves for Section 5.2.1: As only aggregate statistics were presented in Section 5.2.1, we present all relevant precision-recall curves here (Figure A3) for reference.
Appendix A.4. Household Density Estimation
Appendix A.5. Cost Estimation Calculations and Assumptions
Appendix A.6. Lodging Cost Ratio
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|Altitude||GSD||# Img||# Vid||# Annotated PV|
|50 m||1.7 cm||58||6||248|
|60 m||2.1 cm||63||7||289|
|70 m||2.5 cm||47||8||227|
|80 m||2.8 cm||60||8||295|
|90 m||3.2 cm||44||8||214|
|100 m||3.5 cm||47||9||230|
|110 m||3.9 cm||56||4||278|
|120 m||4.3 cm||48||10||238|
|Legal and permit||Part 107 certificate||$150 ||/pilot|
|Pilot training for exam||$300||/pilot|
|Drone registration fee||$5 ||/drone × year|
|Driver/translator||0 in US||/pilot|
|Labor related||Wage||$40||/hour × pilot|
|Benefit||$20 ||/hour × pilot|
|Data storage||$130||/5 TB|
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Ren, S.; Malof, J.; Fetter, R.; Beach, R.; Rineer, J.; Bradbury, K. Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning. ISPRS Int. J. Geo-Inf. 2022, 11, 222. https://doi.org/10.3390/ijgi11040222
Ren S, Malof J, Fetter R, Beach R, Rineer J, Bradbury K. Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning. ISPRS International Journal of Geo-Information. 2022; 11(4):222. https://doi.org/10.3390/ijgi11040222Chicago/Turabian Style
Ren, Simiao, Jordan Malof, Rob Fetter, Robert Beach, Jay Rineer, and Kyle Bradbury. 2022. "Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning" ISPRS International Journal of Geo-Information 11, no. 4: 222. https://doi.org/10.3390/ijgi11040222