InfraredSolarModules

Motivation

InfraredSolarModules is a machine learning dataset that contains real-world imagery of different anomalies found in solar farms. This dataset can be used for machine learning research to gain efficiencies in the solar industry. Infrared imagery is not widely available to researchers. In order to combat the lack of publicly available data on infrared imagery of anomalies in solar PV, this project presents a novel, labeled dataset to facilitate research to solve problems well suited for machine learning that can have environmental impact.

Data

The dataset consists of 20,000 infrared images that are 24 by 40 pixels each. There are 12 defined classes of solar modules presented in this paper with 11 classes of different anomalies and the remaining class being No-Anomaly (i.e. the null case).

Class Name	Images	Description
Cell	1,877	Hot spot occurring with square geometry in single cell.
Cell-Multi	1,288	Hot spots occurring with square geometry in multiple cells.
Cracking	941	Module anomaly caused by cracking on module surface.
Hot-Spot	251	Hot spot on a thin film module.
Hot-Spot-Multi	247	Multiple hot spots on a thin film module.
Shadowing	1056	Sunlight obstructed by vegetation, man-made structures, or adjacent rows.
Diode	1,499	Activated bypass diode, typically 1/3 of module.
Diode-Multi	175	Multiple activated bypass diodes, typically affecting 2/3 of module.
Vegetation	1,639	Panels blocked by vegetation.
Soiling	205	Dirt, dust, or other debris on surface of module.
Offline-Module	828	Entire module is heated.
No-Anomaly	10,000	Nominal solar module.

The file 2020-02-14_InfraredSolarModules.zip contains the images directory and module_metadata.json that describes each image. The JSON file is structured as follows:

{
  "<image_number>": {
    "image_filepath": "images/<image_number>.jpg", 
    "anomaly_class": "<class_name>"
  },
  ...
}

References

This dataset was originally published at ICLR 2020 in AI for Earth Sciences workshop.

https://ai4earthscience.github.io/iclr-2020-workshop/papers/ai4earth22.pdf