├── 2020-02-14_InfraredSolarModules.zip ├── LICENSE └── README.md /2020-02-14_InfraredSolarModules.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RaptorMaps/InfraredSolarModules/88e2d1febbcefe401c17ec80b8973f36a02a1653/2020-02-14_InfraredSolarModules.zip -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Raptor Maps, Inc. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # InfraredSolarModules 2 | 3 | ## Motivation 4 | 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. 5 | 6 | ## Data 7 | 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). 8 | 9 | | Class Name | Images | Description | 10 | |:--------------:|:------:|:-------------------------------------------------------------------------:| 11 | | Cell | 1,877 | Hot spot occurring with square geometry in single cell. | 12 | | Cell-Multi | 1,288 | Hot spots occurring with square geometry in multiple cells. | 13 | | Cracking | 941 | Module anomaly caused by cracking on module surface. | 14 | | Hot-Spot | 251 | Hot spot on a thin film module. | 15 | | Hot-Spot-Multi | 247 | Multiple hot spots on a thin film module. | 16 | | Shadowing | 1056 | Sunlight obstructed by vegetation, man-made structures, or adjacent rows. | 17 | | Diode | 1,499 | Activated bypass diode, typically 1/3 of module. | 18 | | Diode-Multi | 175 | Multiple activated bypass diodes, typically affecting 2/3 of module. | 19 | | Vegetation | 1,639 | Panels blocked by vegetation. | 20 | | Soiling | 205 | Dirt, dust, or other debris on surface of module. | 21 | | Offline-Module | 828 | Entire module is heated. | 22 | | No-Anomaly | 10,000 | Nominal solar module. | 23 | 24 | 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: 25 | 26 | ``` 27 | { 28 | "": { 29 | "image_filepath": "images/.jpg", 30 | "anomaly_class": "" 31 | }, 32 | ... 33 | } 34 | ``` 35 | 36 | ## References 37 | This dataset was originally published at ICLR 2020 in AI for Earth Sciences workshop. 38 | 39 | https://ai4earthscience.github.io/iclr-2020-workshop/papers/ai4earth22.pdf 40 | --------------------------------------------------------------------------------