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clean Segmentation - Solar panels
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### 2.10. Segmentation - Solar panels

2.10.1. [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in .. Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)
- [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in .. Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)

2.10.2. [hyperion_solar_net](https://github.com/fvergaracontesse/hyperion_solar_net) -> trained classificaton & segmentation models on RGB imagery from Google Maps
- [hyperion_solar_net](https://github.com/fvergaracontesse/hyperion_solar_net) -> trained classificaton & segmentation models on RGB imagery from Google Maps

2.10.3. [3D-PV-Locator](https://github.com/kdmayer/3D-PV-Locator) -> Large-scale detection of rooftop-mounted photovoltaic systems in 3D
- [3D-PV-Locator](https://github.com/kdmayer/3D-PV-Locator) -> Large-scale detection of rooftop-mounted photovoltaic systems in 3D

2.10.4. [PV_Pipeline](https://github.com/kdmayer/PV_Pipeline) -> DeepSolar for Germany
- [PV_Pipeline](https://github.com/kdmayer/PV_Pipeline) -> DeepSolar for Germany

2.10.5. [solar-panels-detection](https://github.com/dbaofd/solar-panels-detection) -> using SegNet, Fast SCNN & ResNet
- [solar-panels-detection](https://github.com/dbaofd/solar-panels-detection) -> using SegNet, Fast SCNN & ResNet

2.10.6. [predict_pv_yield](https://github.com/openclimatefix/predict_pv_yield) -> Using optical flow & machine learning to predict PV yield
- [predict_pv_yield](https://github.com/openclimatefix/predict_pv_yield) -> Using optical flow & machine learning to predict PV yield

2.10.7. [Large-scale-solar-plant-monitoring](https://github.com/osmarluiz/Large-scale-solar-plant-monitoring) -> Remote Sensing for Monitoring of Photovoltaic Power Plants in Brazil Using Deep Semantic Segmentation
- [Large-scale-solar-plant-monitoring](https://github.com/osmarluiz/Large-scale-solar-plant-monitoring) -> Remote Sensing for Monitoring of Photovoltaic Power Plants in Brazil Using Deep Semantic Segmentation

2.10.8. [Panel-Segmentation](https://github.com/NREL/Panel-Segmentation) -> Determine the presence of a solar array in the satellite image (boolean True/False), using a VGG16 classification model
- [Panel-Segmentation](https://github.com/NREL/Panel-Segmentation) -> Determine the presence of a solar array in the satellite image (boolean True/False), using a VGG16 classification model

2.10.9. [Roofpedia](https://github.com/ualsg/Roofpedia) -> an open registry of green roofs and solar roofs across the globe identified by Roofpedia through deep learning
- [Roofpedia](https://github.com/ualsg/Roofpedia) -> an open registry of green roofs and solar roofs across the globe identified by Roofpedia through deep learning

2.10.10. [Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data](https://medium.com/nam-r/predicting-the-solar-potential-of-rooftops-using-image-segmentation-and-structured-data-61198c39d57c) Medium article, using 20cm imagery & Unet
- [Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data](https://medium.com/nam-r/predicting-the-solar-potential-of-rooftops-using-image-segmentation-and-structured-data-61198c39d57c) Medium article, using 20cm imagery & Unet

2.10.11. [solar-pv-global-inventory](https://github.com/Lkruitwagen/solar-pv-global-inventory)
- [solar-pv-global-inventory](https://github.com/Lkruitwagen/solar-pv-global-inventory)

2.10.12. [remote-sensing-solar-pv](https://github.com/Lkruitwagen/remote-sensing-solar-pv) -> A repository for sharing progress on the automated detection of solar PV arrays in sentinel-2 remote sensing imagery
- [remote-sensing-solar-pv](https://github.com/Lkruitwagen/remote-sensing-solar-pv) -> A repository for sharing progress on the automated detection of solar PV arrays in sentinel-2 remote sensing imagery

2.10.13. [solar-panel-segmentation)](https://github.com/gabrieltseng/solar-panel-segmentation) -> Finding solar panels using USGS satellite imagery
- [solar-panel-segmentation)](https://github.com/gabrieltseng/solar-panel-segmentation) -> Finding solar panels using USGS satellite imagery

2.10.14. [solar_seg](https://github.com/tcapelle/solar_seg) -> Solar segmentation of PV modules (sub elements of panels) using drone images and fast.ai
- [solar_seg](https://github.com/tcapelle/solar_seg) -> Solar segmentation of PV modules (sub elements of panels) using drone images and fast.ai

2.10.15. [solar_plant_detection](https://github.com/Amirmoradi94/solar_plant_detection) -> boundary extraction of Photovoltaic (PV) plants using Mask RCNN and Amir dataset
- [solar_plant_detection](https://github.com/Amirmoradi94/solar_plant_detection) -> boundary extraction of Photovoltaic (PV) plants using Mask RCNN and Amir dataset

2.10.16. [SolarDetection](https://github.com/A-Stangeland/SolarDetection) -> unet on satellite image from the USA and France
- [SolarDetection](https://github.com/A-Stangeland/SolarDetection) -> unet on satellite image from the USA and France

2.10.17. [adopptrs](https://github.com/francois-rozet/adopptrs) -> Automatic Detection Of Photovoltaic Panels Through Remote Sensing using unet & pytorch
- [adopptrs](https://github.com/francois-rozet/adopptrs) -> Automatic Detection Of Photovoltaic Panels Through Remote Sensing using unet & pytorch

2.10.18. [solar-panel-locator](https://github.com/TorrBorr/solar-panel-locator) -> the number of solar panel pixels was only ~0.2% of the total pixels in the dataset, so solar panel data was upsampled to account for the class imbalance
- [solar-panel-locator](https://github.com/TorrBorr/solar-panel-locator) -> the number of solar panel pixels was only ~0.2% of the total pixels in the dataset, so solar panel data was upsampled to account for the class imbalance

2.10.19. [projects-solar-panel-detection](https://github.com/top-on/projects-solar-panel-detection) -> List of project to detect solar panels from aerial/satellite images
- [projects-solar-panel-detection](https://github.com/top-on/projects-solar-panel-detection) -> List of project to detect solar panels from aerial/satellite images

2.10.20. [Satellite_ComputerVision](https://github.com/mjevans26/Satellite_ComputerVision) -> UNET to detect solar arrays from Sentinel-2 data, using Google Earth Engine and Tensorflow. Also covers parking lot detection
- [Satellite_ComputerVision](https://github.com/mjevans26/Satellite_ComputerVision) -> UNET to detect solar arrays from Sentinel-2 data, using Google Earth Engine and Tensorflow. Also covers parking lot detection

2.10.21. [photovoltaic-detection](https://github.com/riccardocadei/photovoltaic-detection) -> Detecting available rooftop area from satellite images to install photovoltaic panels
- [photovoltaic-detection](https://github.com/riccardocadei/photovoltaic-detection) -> Detecting available rooftop area from satellite images to install photovoltaic panels

2.10.22. [Solar_UNet](https://github.com/mjevans26/Solar_UNet) -> U-Net models delineating solar arrays in Sentinel-2 imagery
- [Solar_UNet](https://github.com/mjevans26/Solar_UNet) -> U-Net models delineating solar arrays in Sentinel-2 imagery

### 2.11. Segmentation - Other manmade

2.11.1. [Aarsh2001/ML_Challenge_NRSC](https://github.com/Aarsh2001/ML_Challenge_NRSC) -> Electrical Substation detection
- [Aarsh2001/ML_Challenge_NRSC](https://github.com/Aarsh2001/ML_Challenge_NRSC) -> Electrical Substation detection

2.11.2. [electrical_substation_detection](https://github.com/thisishardik/electrical_substation_detection)
- [electrical_substation_detection](https://github.com/thisishardik/electrical_substation_detection)

2.11.3. [PLGAN-for-Power-Line-Segmentation](https://github.com/R3ab/PLGAN-for-Power-Line-Segmentation) -> Generative Adversarial Networks for Power-Line Segmentation in Aerial Images
- [PLGAN-for-Power-Line-Segmentation](https://github.com/R3ab/PLGAN-for-Power-Line-Segmentation) -> Generative Adversarial Networks for Power-Line Segmentation in Aerial Images

2.11.4. [MCAN-OilSpillDetection](https://github.com/liyongqingupc/MCAN-OilSpillDetection) -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training
- [MCAN-OilSpillDetection](https://github.com/liyongqingupc/MCAN-OilSpillDetection) -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training

2.11.5. [plastics](https://github.com/earthrise-media/plastics) -> Detecting and Monitoring Plastic Waste Aggregations in Sentinel-2 Imagery for [globalplasticwatch.org](https://globalplasticwatch.org/)
- [plastics](https://github.com/earthrise-media/plastics) -> Detecting and Monitoring Plastic Waste Aggregations in Sentinel-2 Imagery for [globalplasticwatch.org](https://globalplasticwatch.org/)

2.11.6. [mining-detector](https://github.com/earthrise-media/mining-detector) -> detection of artisanal gold mines in Sentinel-2 satellite imagery for [Amazon Mining Watch](https://amazonminingwatch.org/). Also covers clandestine airstrips
- [mining-detector](https://github.com/earthrise-media/mining-detector) -> detection of artisanal gold mines in Sentinel-2 satellite imagery for [Amazon Mining Watch](https://amazonminingwatch.org/). Also covers clandestine airstrips

2.11.7. [EG-UNet](https://github.com/tist0bsc/EG-UNet) Deep Feature Enhancement Method for Land Cover With Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining
- [EG-UNet](https://github.com/tist0bsc/EG-UNet) Deep Feature Enhancement Method for Land Cover With Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining

### 2.12. Panoptic segmentation

2.12.1. [Things and stuff or how remote sensing could benefit from panoptic segmentation](https://softwaremill.com/things-and-stuff-or-how-remote-sensing-could-benefit-from-panoptic-segmentation/)
- [Things and stuff or how remote sensing could benefit from panoptic segmentation](https://softwaremill.com/things-and-stuff-or-how-remote-sensing-could-benefit-from-panoptic-segmentation/)

2.12.2. [utae-paps](https://github.com/VSainteuf/utae-paps) -> PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation
- [utae-paps](https://github.com/VSainteuf/utae-paps) -> PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation

2.12.3. [pastis-benchmark](https://github.com/VSainteuf/pastis-benchmark)
- [pastis-benchmark](https://github.com/VSainteuf/pastis-benchmark)

2.12.4. [Panoptic-Generator](https://github.com/abilius-app/Panoptic-Generator) -> This module converts GIS data into panoptic segmentation tiles
- [Panoptic-Generator](https://github.com/abilius-app/Panoptic-Generator) -> This module converts GIS data into panoptic segmentation tiles

2.12.5. [BSB-Aerial-Dataset](https://github.com/osmarluiz/BSB-Aerial-Dataset) -> an example on how to use Detectron2's Panoptic-FPN in the BSB Aerial Dataset
- [BSB-Aerial-Dataset](https://github.com/osmarluiz/BSB-Aerial-Dataset) -> an example on how to use Detectron2's Panoptic-FPN in the BSB Aerial Dataset

### 2.13. Segmentation - Miscellaneous

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- [RAANet](https://github.com/Lrr0213/RAANet) -> A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images

-. [wheelRuts_semanticSegmentation](https://github.com/SmartForest-no/wheelRuts_semanticSegmentation) -> Mapping wheel-ruts from timber harvesting operations using deep learning techniques in drone imagery
- [wheelRuts_semanticSegmentation](https://github.com/SmartForest-no/wheelRuts_semanticSegmentation) -> Mapping wheel-ruts from timber harvesting operations using deep learning techniques in drone imagery

- [LWN-for-UAVRSI](https://github.com/syliudf/LWN-for-UAVRSI) -> Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images, applied to Vaihingen, UAVid and UDD6 datasets

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