---
title: 'SPID: Solar Panel Image Dataset Overview'
url: https://www.emergentmind.com/topics/solar-panel-image-dataset-spid
type: topic
---

# SPID: Solar Panel Image Dataset Overview

A Solar Panel Image Dataset (SPID) is a curated collection of remote sensing imagery—often aerial, satellite, or close-range RGB and multispectral images—containing annotated photovoltaic (PV) panels, arrays, or farms. SPIDs are foundational for computer vision research in solar energy infrastructure monitoring, asset mapping, performance analytics, and defect detection. Recent SPIDs are characterized by rigorous annotation, extensive metadata, geospatial referencing, and benchmarking protocols targeting both methodologic advancement and applied energy deployment [2201.02107].

## 1. Dataset Typology and Domain Scope

SPIDs span multiple image acquisition modalities and spatial scales:

- **High-resolution aerial RGB datasets** (e.g., HyperionSolarNet [2201.02107]; SPID-Fresno [1607.06029]) with sub-meter ground sampling distance (GSD ≈ 0.07–0.30 m/px), typically focused on urban or suburban regions for rooftop PV asset detection and mapping.
- **Multispectral satellite-image datasets** (e.g., GloSoFarID [2404.05180]; Global Renewables Watch [2503.14860]) covering continental or global extents, with per-pixel ground truth or regional polygon labels for utility-scale solar farm identification.
- **Crowdsourced dual-provider aerial datasets** (Kasmi et al. [2209.03726]) exploiting human annotation consensus on images from different acquisition chains (Google Earth Engine, national orthophotos) to study cross-domain generalization and calibration.
- **UAV-obtained defect datasets** (SPID-Solar Inspection [2509.05348]) designed for close-up panel defect/contamination detection under controlled lighting, altitude, and environmental conditions.
- **Time-series panel performance datasets** (PV-Net [1710.03811]) combining RGB panel imagery with environmental and power-output telemetry for soiling/defect impact analysis.

Spatial coverage ranges from small-city tests (e.g., Berkeley, CA in HyperionSolarNet) to global inventories (Global Renewables Watch, GloSoFarID) with temporal sampling from single campaigns to quarterly revisits across multiple years.

## 2. Annotation Methodologies and Data Schemas

Annotation protocols vary substantially by dataset objective and domain:

- **Polygonal and pixel-wise masks:** Per-pixel binary segmentation masks are standard for semantic segmentation tasks ([2201.02107], [2209.03726], [2404.05180], [1607.06029]), typically encoded as PNG (aerial) or GeoTIFF (satellite) with precise georegistration. Polygons are manually drawn (LabelBox, GIS GUI, crowdsourcing) or subject to consensus fusion (PAC method in Kasmi et al.).
- **Classification labels:** Binary ("solar" vs. "no_solar") assignments per image/tile for asset presence/absence (HyperionSolarNet, SPID-Fresno) or multi-class for panel state/defect type (SPID-Solar Inspection).
- **Object detection (bounding boxes):** COCO-style JSON entries indicate defects, contamination, or array units ([2509.05348]), supporting evaluation at varying IoU thresholds.
- **Metadata enrichment:** Solar PV datasets increasingly include installation-level metadata—surface area, installed capacity (kWp), tilt/azimuth, installation date, integration type, self-consumption flag—matched to imagery and annotation (Kasmi et al., Global Renewables Watch).
- **Weak supervision:** Certain datasets (PV-Net [1710.03811]) avoid explicit localization in favor of weakly supervised soiling impact through global power-loss labels and pseudo-mask fusion using network feature maps.
- **Consensus-based quality assurance:** Dual-phase and multi-annotator protocols maximize annotation fidelity, with kernel-density fusion and relative consensus thresholds (PAC ≥ 2.0, pixel inclusion ≥ 45%) [2209.03726].

## 3. Dataset Structure, Splits, and Access

SPIDs are generally organized by split type (training, validation, test) with rigorous statistics on class balance and distribution:

| Dataset                     | Train     | Validation   | Test      | Image Type         | Classes                              | Geographic Focus   |
|-----------------------------|-----------|--------------|-----------|--------------------|--------------------------------------|-------------------|
| HyperionSolarNet [2201.02107] | 1,963     | 492          | 2,243     | 416/600px RGB      | Solar/no_solar                       | 14 US states, Berkeley CA |
| PV-Net [1710.03811]           | 27,537    | 18,217       | 241 (segm.)| RGB + telemetry    | Power-loss bins                      | Lab (panels)      |
| SPID (defects) [2509.05348]   | 700       | 150          | 100       | UAV RGB 224/256px  | Bird, Clean, Dusty, Electrical, Physical | Multi-regional    |
| GloSoFarID [2404.05180]       | ≈9,592    | ≈2,055       | ≈2,056    | 256px, 13 bands    | Solar farm mask                      | USA, Global       |
| Crowdsource SPID [2209.03726] | —         | —            | —         | 400px, RGB         | Segmentation mask                    | France            |
| Fresno SPID [1607.06029]      | 40 tiles  | —            | 20 tiles  | 5,000px, RGB       | Per-pixel mask                       | Fresno CA         |
| Global Renewables [2503.14860]| —         | —            | —         | 4,096px, RGB       | Polygon, area, capacity              | Global            |

Licensing is generally open (CC BY 4.0, MIT), with some imagery subject to commercial restrictions (PlanetScope, Google Maps API).

Access modes include GitHub repositories ([2404.05180], [2509.05348]), Zenodo DOIs ([2209.03726]), dedicated project portals, or author request ([2201.02107]).

## 4. Evaluation Metrics and Benchmarking

SPIDs support benchmarking across several canonical metrics aligned to the annotation schema:

- **Semantic segmentation:** Intersection over Union (IoU), pixel-wise F1 (Dice) score, precision, recall; for a predicted mask $P$ and ground truth $G$, $\mathrm{IoU} = \frac{|P \cap G|}{|P \cup G|}$ [2201.02107], [2209.03726], [2404.05180].
- **Object detection:** Mean Average Precision (mAP) across classes and IoU thresholds (0.50:0.05:0.95); $F_1 = 2 \frac{\text{precision} \times \text{recall}}{\text{precision}+\text{recall}}$; Average Recall (AR) [2509.05348].
- **Classification:** Accuracy, precision, recall, and class-specific $F_1$ scores [2201.02107].
- **Soiling/defect analysis:** Power-loss quantification $\text{SoilingImpact} = 100 \times ( P_{\text{reference}} - P_{\text{soiled}} ) / P_{\text{reference}}$ [1710.03811].
- **Capacity estimation:** Empirical or module-efficiency-based formulas, e.g., $P_i = A_i \times \eta_\text{module} \times G_\text{ref}$ for solar farm area-to-capacity conversion [2503.14860].
- **Baselines:** A simple U-Net on Google images in Kasmi et al. achieves IoU ≈ 0.70–0.80; object-level and pixel-level PR curves are standard [1607.06029].

Reported benchmark results allow technical comparison of segmentation backbones (e.g., U-Net IoU=0.793 [2404.05180]) and object detectors (YOLOv3, Faster R-CNN, Swin Transformer in [2509.05348]), with precise performance stratified by classes and test domains.

## 5. Technical Challenges and Robustness Considerations

SPID development and utilization entail specific challenges:

- **Domain shift:** Cross-provider imagery (Google vs. IGN in [2209.03726]), sensor type, and acquisition conditions lead to distributional variance requiring normalization and data augmentation (channel norm, random crops, flips, rotations, brightness adjustment).
- **Class imbalance and sparsity:** "solar" class sparsity in real-world test sets (14% solar in HyperionSolarNet Berkeley test), managed with hard-negative mining and augmentation strategies.
- **Annotation error and uncertainty:** Manual annotation is subject to false positives (e.g., roof windows), ambiguous panel appearances, and limited inter-annotator agreement statistics; consensus fusion and post-processing filters mitigate these.
- **Spatial/geographic bias:** Urban-centric datasets (Fresno, Berkeley), national coverage (France), or global scale (GloSoFarID, Global Renewables); limitations addressed by extension to new regions/sensors.
- **Multispectral and 3D expansion:** Most SPIDs are RGB-only; incorporation of Sentinel-2 bands or LiDAR data proposed for enhanced rooftop/type discrimination and domain adaptation.
- **Temporal variability:** Longitudinal datasets (Global Renewables Watch, GloSoFarID) enable construction date assignment and land-use change analysis. Panel defects/soiling impact can be tracked in close-range time series (PV-Net).

## 6. Applications and Future Directions

SPIDs underpin multiple research and policy endeavors:

- **Automated rooftop/ground PV registry construction** for grid integration planning and TSOs, utilizing segmentation and registry metadata (area, orientation, capacity) [2209.03726].
- **Renewable energy analytics** including global deployment tracking, capacity estimation, and land-use transition mapping at sub-national to planetary scales [2503.14860].
- **Defect detection and maintenance automation** (object detection, UAV inspection) improving asset reliability [2509.05348].
- **Transfer learning and domain adaptation** exploiting dual-provider and cross-modal datasets [2209.03726].
- **Methodological research:** Benchmarking novel architectures (e.g., Half-UNet [2404.05180]), multispectral fusion, weakly supervised localization [1710.03811].
- **Crowdsourcing strategy analysis:** Annotation protocol evaluation and consensus approaches [2209.03726].

A plausible implication is that future SPIDs will more frequently incorporate multisensor fusion, richer temporal labeling, and expanded global coverage, including new annotation modalities (e.g., active learning) and cross-linked registries for performance tracking and verification.

## 7. Representative Datasets in the Literature

The following table organizes salient SPIDs and related resources:

| Name / Paper                    | Coverage / Domain             | Annotations                | Licensing / Access                          |
|----------------------------------|------------------------------|----------------------------|---------------------------------------------|
| HyperionSolarNet [2201.02107]   | 14 US states + Berkeley CA    | RGB, solar/no_solar, masks | UC Berkeley: upon request, CC BY 4.0        |
| PV-Net [1710.03811]             | Panel testbeds, lab imagery   | RGB, power-loss, weak mask | https://deep-solar-eye.github.io/           |
| GloSoFarID [2404.05180]         | Global, Sentinel-2, 2021-23   | 13 bands, masks            | GitHub/AWS S3, MIT                          |
| SPID defect [2509.05348]        | UAV over solar fields         | RGB, COCO bbox/mask, 5 cls | GitHub, CC BY 4.0                           |
| Crowdsource SPID [2209.03726]   | France, Google/IGN aerial     | RGB, masks, meta           | Zenodo, CC BY 4.0, CC0                      |
| SPID-Fresno [1607.06029]        | Fresno CA, aerial RGB         | Pixelwise masks, polygons  | Figshare, CC BY 4.0                         |
| Global Renewables [2503.14860]  | Global, PlanetScope 2017-24   | RGB, polygons, meta        | Author portal, open for research            |

Each dataset is referenced as originally described, with strict adherence to licensing and access protocols, and is directly benchmarked in supporting literature.

Source: https://www.emergentmind.com/topics/solar-panel-image-dataset-spid