diff --git a/README.md b/README.md index 6b33c1cc..3a67aead 100644 --- a/README.md +++ b/README.md @@ -948,181 +948,181 @@ Extracting roads is challenging due to the occlusions caused by other objects an ### 2.13. Segmentation - Miscellaneous - 2.13.1. [awesome-satellite-images-segmentation](https://github.com/mrgloom/awesome-semantic-segmentation#satellite-images-segmentation) +- [awesome-satellite-images-segmentation](https://github.com/mrgloom/awesome-semantic-segmentation#satellite-images-segmentation) - 2.13.2. [Satellite Image Segmentation: a Workflow with U-Net](https://medium.com/vooban-ai/satellite-image-segmentation-a-workflow-with-u-net-7ff992b2a56e) is a decent intro article +- [Satellite Image Segmentation: a Workflow with U-Net](https://medium.com/vooban-ai/satellite-image-segmentation-a-workflow-with-u-net-7ff992b2a56e) is a decent intro article - 2.13.3. [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) -> Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA, Potsdam, Vaihingen & iSAID +- [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) -> Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA, Potsdam, Vaihingen & iSAID - 2.13.4. [segmentation_gym](https://github.com/Doodleverse/segmentation_gym) -> A neural gym for training deep learning models to carry out geoscientific image segmentation +- [segmentation_gym](https://github.com/Doodleverse/segmentation_gym) -> A neural gym for training deep learning models to carry out geoscientific image segmentation - 2.13.5. [How to create a DataBlock for Multispectral Satellite Image Semantic Segmentation using Fastai](https://towardsdatascience.com/how-to-create-a-datablock-for-multispectral-satellite-image-segmentation-with-the-fastai-v2-bc5e82f4eb5) +- [How to create a DataBlock for Multispectral Satellite Image Semantic Segmentation using Fastai](https://towardsdatascience.com/how-to-create-a-datablock-for-multispectral-satellite-image-segmentation-with-the-fastai-v2-bc5e82f4eb5) - 2.13.6. [Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye](https://github.com/Vooban/Smoothly-Blend-Image-Patches) -> python code to blend predicted patches smoothly. See [Satellite-Image-Segmentation-with-Smooth-Blending](https://github.com/MaitrySinha21/Satellite-Image-Segmentation-with-Smooth-Blending) +- [Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye](https://github.com/Vooban/Smoothly-Blend-Image-Patches) -> python code to blend predicted patches smoothly. See [Satellite-Image-Segmentation-with-Smooth-Blending](https://github.com/MaitrySinha21/Satellite-Image-Segmentation-with-Smooth-Blending) - 2.13.7. [DCA](https://github.com/Luffy03/DCA) -> Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation +- [DCA](https://github.com/Luffy03/DCA) -> Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation - 2.13.8. [SCAttNet](https://github.com/lehaifeng/SCAttNet) -> Semantic Segmentation Network with Spatial and Channel Attention Mechanism +- [SCAttNet](https://github.com/lehaifeng/SCAttNet) -> Semantic Segmentation Network with Spatial and Channel Attention Mechanism - 2.13.9. [unetseg](https://github.com/dymaxionlabs/unetseg) -> A set of classes and CLI tools for training a semantic segmentation model based on the U-Net architecture, using Tensorflow and Keras. This implementation is tuned specifically for satellite imagery and other geospatial raster data +- [unetseg](https://github.com/dymaxionlabs/unetseg) -> A set of classes and CLI tools for training a semantic segmentation model based on the U-Net architecture, using Tensorflow and Keras. This implementation is tuned specifically for satellite imagery and other geospatial raster data - 2.13.10. [Semantic Segmentation of Satellite Imagery using U-Net & fast.ai](https://medium.com/dataseries/image-semantic-segmentation-of-satellite-imagery-using-u-net-e99ae13cf464) -> with [repo](https://github.com/raoofnaushad/Image-Semantic-Segmentation-of-Satellite-Imagery-using-U-Net.) +- [Semantic Segmentation of Satellite Imagery using U-Net & fast.ai](https://medium.com/dataseries/image-semantic-segmentation-of-satellite-imagery-using-u-net-e99ae13cf464) -> with [repo](https://github.com/raoofnaushad/Image-Semantic-Segmentation-of-Satellite-Imagery-using-U-Net.) - 2.13.11. [clusternet_segmentation](https://github.com/zhygallo/clusternet_segmentation) -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network +- [clusternet_segmentation](https://github.com/zhygallo/clusternet_segmentation) -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network - 2.13.12. [TDD](https://github.com/Jingtao-Li-CVer/TDD) -> One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning +- [TDD](https://github.com/Jingtao-Li-CVer/TDD) -> One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning - 2.13.13. [Efficient-Transformer](https://github.com/zyxu1996/Efficient-Transformer) -> Efficient Transformer for Remote Sensing Image Segmentation +- [Efficient-Transformer](https://github.com/zyxu1996/Efficient-Transformer) -> Efficient Transformer for Remote Sensing Image Segmentation - 2.13.14. [weakly_supervised](https://github.com/LobellLab/weakly_supervised) -> Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery +- [weakly_supervised](https://github.com/LobellLab/weakly_supervised) -> Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery - 2.13.15. [HRCNet-High-Resolution-Context-Extraction-Network](https://github.com/zyxu1996/HRCNet-High-Resolution-Context-Extraction-Network) -> High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images +- [HRCNet-High-Resolution-Context-Extraction-Network](https://github.com/zyxu1996/HRCNet-High-Resolution-Context-Extraction-Network) -> High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images - 2.13.16. [Semantic segmentation of SAR images using a self supervised technique](https://github.com/cattale93/pytorch_self_supervised_learning) +- [Semantic segmentation of SAR images using a self supervised technique](https://github.com/cattale93/pytorch_self_supervised_learning) - 2.13.17. [satellite-segmentation-pytorch](https://github.com/obravo7/satellite-segmentation-pytorch) -> explores a wide variety of image augmentations to increase training dataset size +- [satellite-segmentation-pytorch](https://github.com/obravo7/satellite-segmentation-pytorch) -> explores a wide variety of image augmentations to increase training dataset size - 2.13.18. [Spectralformer](https://github.com/danfenghong/IEEE_TGRS_SpectralFormer) -> Rethinking hyperspectral image classification with transformers +- [Spectralformer](https://github.com/danfenghong/IEEE_TGRS_SpectralFormer) -> Rethinking hyperspectral image classification with transformers - 2.13.19. [Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels](https://github.com/mpBarbato/Unsupervised-Segmentation-of-Hyperspectral-Remote-Sensing-Images-with-Superpixels) +- [Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels](https://github.com/mpBarbato/Unsupervised-Segmentation-of-Hyperspectral-Remote-Sensing-Images-with-Superpixels) - 2.13.20. [Semantic-Segmentation-with-Sparse-Labels](https://github.com/Hua-YS/Semantic-Segmentation-with-Sparse-Labels) +- [Semantic-Segmentation-with-Sparse-Labels](https://github.com/Hua-YS/Semantic-Segmentation-with-Sparse-Labels) - 2.13.21. [SNDF](https://github.com/mi18/SNDF) -> Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation +- [SNDF](https://github.com/mi18/SNDF) -> Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation - 2.13.22. [Satellite-Image-Classification](https://github.com/yxian29/Satellite-Image-Classification) -> using random forest or support vector machines (SVM) and sklearn +- [Satellite-Image-Classification](https://github.com/yxian29/Satellite-Image-Classification) -> using random forest or support vector machines (SVM) and sklearn - 2.13.23. [dynamic-rs-segmentation](https://github.com/keillernogueira/dynamic-rs-segmentation) -> Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks +- [dynamic-rs-segmentation](https://github.com/keillernogueira/dynamic-rs-segmentation) -> Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks - 2.13.24. [2023GRIC](https://github.com/biluko/2023GRIC) -> Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global Warming +- [2023GRIC](https://github.com/biluko/2023GRIC) -> Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global Warming - 2.13.25. [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch) -> Segmentation models with pretrained backbones, has been used in multiple winning solutions to remote sensing competitions +- [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch) -> Segmentation models with pretrained backbones, has been used in multiple winning solutions to remote sensing competitions - 2.13.26. [SSRN](https://github.com/zilongzhong/SSRN) -> Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework +- [SSRN](https://github.com/zilongzhong/SSRN) -> Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework - 2.13.27. [SO-DNN](https://github.com/PanXinZebra/SO-DNN) -> Simplified object-based deep neural network for very high resolution remote sensing image classification +- [SO-DNN](https://github.com/PanXinZebra/SO-DNN) -> Simplified object-based deep neural network for very high resolution remote sensing image classification - 2.13.28. [SANet](https://github.com/mrluin/SANet-PyTorch) -> Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images +- [SANet](https://github.com/mrluin/SANet-PyTorch) -> Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images - 2.13.29. [aerial-segmentation](https://github.com/alpemek/aerial-segmentation) -> Learning Aerial Image Segmentation from Online Maps +- [aerial-segmentation](https://github.com/alpemek/aerial-segmentation) -> Learning Aerial Image Segmentation from Online Maps - 2.13.30. [IterativeSegmentation](https://github.com/gaudetcj/IterativeSegmentation) -> Recurrent Neural Networks to Correct Satellite Image Classification Maps +- [IterativeSegmentation](https://github.com/gaudetcj/IterativeSegmentation) -> Recurrent Neural Networks to Correct Satellite Image Classification Maps - 2.13.31. [Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation](https://affine.medium.com/detectron2-fpn-pointrend-model-for-amazing-satellite-image-segmentation-183456063e15) -> 15% increase in accuracy when compared to the U-Net model +- [Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation](https://affine.medium.com/detectron2-fpn-pointrend-model-for-amazing-satellite-image-segmentation-183456063e15) -> 15% increase in accuracy when compared to the U-Net model - 2.13.32. [HybridSN](https://github.com/purbayankar/HybridSN-pytorch) -> Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification +- [HybridSN](https://github.com/purbayankar/HybridSN-pytorch) -> Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification - 2.13.33. [TNNLS_2022_X-GPN](https://github.com/B-Xi/TNNLS_2022_X-GPN) -> Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification +- [TNNLS_2022_X-GPN](https://github.com/B-Xi/TNNLS_2022_X-GPN) -> Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification - 2.13.34. [singleSceneSemSegTgrs2022](https://github.com/sudipansaha/singleSceneSemSegTgrs2022) -> Unsupervised Single-Scene Semantic Segmentation for Earth Observation +- [singleSceneSemSegTgrs2022](https://github.com/sudipansaha/singleSceneSemSegTgrs2022) -> Unsupervised Single-Scene Semantic Segmentation for Earth Observation - 2.13.35. [A-Fast-and-Compact-3-D-CNN-for-HSIC](https://github.com/mahmad00/A-Fast-and-Compact-3-D-CNN-for-HSIC) -> A Fast and Compact 3-D CNN for Hyperspectral Image Classification +- [A-Fast-and-Compact-3-D-CNN-for-HSIC](https://github.com/mahmad00/A-Fast-and-Compact-3-D-CNN-for-HSIC) -> A Fast and Compact 3-D CNN for Hyperspectral Image Classification - 2.13.36. [HSNRS](https://github.com/Walkerlikesfish/HSNRS) -> Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery +- [HSNRS](https://github.com/Walkerlikesfish/HSNRS) -> Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery - 2.13.37. [GiGCN](https://github.com/ShuGuoJ/GiGCN) -> Graph-in-Graph Convolutional Network for Hyperspectral Image Classification +- [GiGCN](https://github.com/ShuGuoJ/GiGCN) -> Graph-in-Graph Convolutional Network for Hyperspectral Image Classification - 2.13.38. [SSAN](https://github.com/EtPan/SSAN) -> Spectral-Spatial Attention Networks for Hyperspectral Image Classification +- [SSAN](https://github.com/EtPan/SSAN) -> Spectral-Spatial Attention Networks for Hyperspectral Image Classification - 2.13.39. [drone-images-semantic-segmentation](https://github.com/ayushdabra/drone-images-semantic-segmentation) -> Multiclass Semantic Segmentation of Aerial Drone Images Using Deep Learning +- [drone-images-semantic-segmentation](https://github.com/ayushdabra/drone-images-semantic-segmentation) -> Multiclass Semantic Segmentation of Aerial Drone Images Using Deep Learning - 2.13.40. [Satellite-Image-Segmentation-with-Smooth-Blending](https://github.com/MaitrySinha21/Satellite-Image-Segmentation-with-Smooth-Blending) -> uses [Smoothly-Blend-Image-Patches](https://github.com/Vooban/Smoothly-Blend-Image-Patches) +- [Satellite-Image-Segmentation-with-Smooth-Blending](https://github.com/MaitrySinha21/Satellite-Image-Segmentation-with-Smooth-Blending) -> uses [Smoothly-Blend-Image-Patches](https://github.com/Vooban/Smoothly-Blend-Image-Patches) - 2.13.41. [BayesianUNet](https://github.com/tha-santacruz/BayesianUNet) -> Pytorch Bayesian UNet model for segmentation and uncertainty prediction, applied to the Potsdam Dataset +- [BayesianUNet](https://github.com/tha-santacruz/BayesianUNet) -> Pytorch Bayesian UNet model for segmentation and uncertainty prediction, applied to the Potsdam Dataset - 2.13.42. [RAANet](https://github.com/Lrr0213/RAANet) -> A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images +- [RAANet](https://github.com/Lrr0213/RAANet) -> A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images - 2.13.43. [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 - 2.13.44. [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 +- [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 - 2.13.45. [hypernet](https://github.com/ESA-PhiLab/hypernet) -> library which implements hyperspectral image (HSI) segmentation +- [hypernet](https://github.com/ESA-PhiLab/hypernet) -> library which implements hyperspectral image (HSI) segmentation - 2.13.46. [ST-UNet](https://github.com/XinnHe/ST-UNet) -> Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation +- [ST-UNet](https://github.com/XinnHe/ST-UNet) -> Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation - 2.13.47. [EDFT](https://github.com/h1063135843/EDFT) -> Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation +- [EDFT](https://github.com/h1063135843/EDFT) -> Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation - 2.13.48. [WiCoNet](https://github.com/ggsDing/WiCoNet) -> Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images +- [WiCoNet](https://github.com/ggsDing/WiCoNet) -> Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images - 2.13.49. [CRGNet](https://github.com/YonghaoXu/CRGNet) -> Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations +- [CRGNet](https://github.com/YonghaoXu/CRGNet) -> Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations - 2.13.50. [SA-UNet](https://github.com/Yancccccc/SA-UNet) -> Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features +- [SA-UNet](https://github.com/Yancccccc/SA-UNet) -> Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features - 2.13.51. [MANet](https://github.com/lironui/Multi-Attention-Network) -> Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images +- [MANet](https://github.com/lironui/Multi-Attention-Network) -> Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images - 2.13.52. [BANet](https://github.com/lironui/BANet) -> Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images +- [BANet](https://github.com/lironui/BANet) -> Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images - 2.13.53. [MACU-Net](https://github.com/lironui/MACU-Net) -> MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images +- [MACU-Net](https://github.com/lironui/MACU-Net) -> MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images - 2.13.54. [DNAS](https://github.com/faye0078/DNAS) -> Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation +- [DNAS](https://github.com/faye0078/DNAS) -> Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation - 2.13.55. [A2-FPN](https://github.com/lironui/A2-FPN) -> A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images +- [A2-FPN](https://github.com/lironui/A2-FPN) -> A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images - 2.13.56. [MAResU-Net](https://github.com/lironui/MAResU-Net) -> Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images +- [MAResU-Net](https://github.com/lironui/MAResU-Net) -> Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images - 2.13.57. [ml_segmentation](https://github.com/dgriffiths3/ml_segmentation) -> semantic segmentation of buildings using Random Forest, Support Vector Machine (SVM) & Gradient Boosting Classifier (GBC) +- [ml_segmentation](https://github.com/dgriffiths3/ml_segmentation) -> semantic segmentation of buildings using Random Forest, Support Vector Machine (SVM) & Gradient Boosting Classifier (GBC) - 2.13.58. [RSEN](https://github.com/YonghaoXu/RSEN) -> Robust Self-Ensembling Network for Hyperspectral Image Classification +- [RSEN](https://github.com/YonghaoXu/RSEN) -> Robust Self-Ensembling Network for Hyperspectral Image Classification - 2.13.59. [MSNet](https://github.com/taochx/MSNet) -> multispectral semantic segmentation network for remote sensing images +- [MSNet](https://github.com/taochx/MSNet) -> multispectral semantic segmentation network for remote sensing images - 2.13.60. [k-textures](https://zenodo.org/record/6359859#.Yytt6OzMK3I) -> K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation +- [k-textures](https://zenodo.org/record/6359859#.Yytt6OzMK3I) -> K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation - 2.13.61. [Swin-Transformer-Semantic-Segmentation](https://github.com/koechslin/Swin-Transformer-Semantic-Segmentation) -> Satellite Image Semantic Segmentation +- [Swin-Transformer-Semantic-Segmentation](https://github.com/koechslin/Swin-Transformer-Semantic-Segmentation) -> Satellite Image Semantic Segmentation - 2.13.62. [UDA_for_RS](https://github.com/Levantespot/UDA_for_RS) -> Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer +- [UDA_for_RS](https://github.com/Levantespot/UDA_for_RS) -> Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer - 2.13.63. [A-3D-CNN-AM-DSC-model-for-hyperspectral-image-classification](https://github.com/hahatongxue/A-3D-CNN-AM-DSC-model-for-hyperspectral-image-classification) -> Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification +- [A-3D-CNN-AM-DSC-model-for-hyperspectral-image-classification](https://github.com/hahatongxue/A-3D-CNN-AM-DSC-model-for-hyperspectral-image-classification) -> Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification - 2.13.64. [contrastive-distillation](https://github.com/edornd/contrastive-distillation) -> A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images +- [contrastive-distillation](https://github.com/edornd/contrastive-distillation) -> A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images - 2.13.65. [SegForestNet](https://github.com/gritzner/SegForestNet) -> SegForestNet: Spatial-Partitioning-Based Aerial Image Segmentation +- [SegForestNet](https://github.com/gritzner/SegForestNet) -> SegForestNet: Spatial-Partitioning-Based Aerial Image Segmentation - 2.13.66. [MFVNet](https://github.com/weichenrs/MFVNet) -> MFVNet: Deep Adaptive Fusion Network with Multiple Field-of-Views for Remote Sensing Image Semantic Segmentation +- [MFVNet](https://github.com/weichenrs/MFVNet) -> MFVNet: Deep Adaptive Fusion Network with Multiple Field-of-Views for Remote Sensing Image Semantic Segmentation - 2.13.67. [Wildebeest-UNet](https://github.com/zijing-w/Wildebeest-UNet) -> detecting wildebeest and zebras in Serengeti-Mara ecosystem from very-high-resolution satellite imagery +- [Wildebeest-UNet](https://github.com/zijing-w/Wildebeest-UNet) -> detecting wildebeest and zebras in Serengeti-Mara ecosystem from very-high-resolution satellite imagery - 2.13.68. [segment-anything-eo](https://github.com/aliaksandr960/segment-anything-eo) -> Earth observation tools for Meta AI Segment Anything (SAM - Segment Anything Model) +- [segment-anything-eo](https://github.com/aliaksandr960/segment-anything-eo) -> Earth observation tools for Meta AI Segment Anything (SAM - Segment Anything Model) - 2.13.69. [HR-Image-classification_SDF2N](https://github.com/SicongLiuRS/HR-Image-classification_SDF2N) -> A Shallow-to-Deep Feature Fusion Network for VHR Remote Sensing Image Classification +- [HR-Image-classification_SDF2N](https://github.com/SicongLiuRS/HR-Image-classification_SDF2N) -> A Shallow-to-Deep Feature Fusion Network for VHR Remote Sensing Image Classification # ## 3. Instance segmentation In instance segmentation, each individual 'instance' of a segmented area is given a unique lable. For detection of very small objects this may a good approach, but it can struggle seperating individual objects that are closely spaced. - 3.1. [Mask_RCNN](https://github.com/matterport/Mask_RCNN) generates bounding boxes and segmentation masks for each instance of an object in the image. It is very commonly used for instance segmentation & object detection +- [Mask_RCNN](https://github.com/matterport/Mask_RCNN) generates bounding boxes and segmentation masks for each instance of an object in the image. It is very commonly used for instance segmentation & object detection - 3.2. [Instance segmentation of center pivot irrigation system in Brazil](https://github.com/saraivaufc/instance-segmentation-maskrcnn) using free Landsat images, mask R-CNN & Keras +- [Instance segmentation of center pivot irrigation system in Brazil](https://github.com/saraivaufc/instance-segmentation-maskrcnn) using free Landsat images, mask R-CNN & Keras - 3.3. [Building-Detection-MaskRCNN](https://github.com/Mstfakts/Building-Detection-MaskRCNN) -> Building detection from the SpaceNet dataset by using Mask RCNN +- [Building-Detection-MaskRCNN](https://github.com/Mstfakts/Building-Detection-MaskRCNN) -> Building detection from the SpaceNet dataset by using Mask RCNN - 3.4. [Oil tank instance segmentation with Mask R-CNN](https://github.com/georgiosouzounis/instance-segmentation-mask-rcnn) with [accompanying article](https://medium.com/@georgios.ouzounis/oil-storage-tank-instance-segmentation-with-mask-r-cnn-77c94433045f) using Keras & Airbus Oil Storage Detection Dataset on Kaggle +- [Oil tank instance segmentation with Mask R-CNN](https://github.com/georgiosouzounis/instance-segmentation-mask-rcnn) with [accompanying article](https://medium.com/@georgios.ouzounis/oil-storage-tank-instance-segmentation-with-mask-r-cnn-77c94433045f) using Keras & Airbus Oil Storage Detection Dataset on Kaggle - 3.5. [Mask_RCNN-for-Caravans](https://github.com/OrdnanceSurvey/Mask_RCNN-for-Caravans) -> detect caravan footprints from OS imagery +- [Mask_RCNN-for-Caravans](https://github.com/OrdnanceSurvey/Mask_RCNN-for-Caravans) -> detect caravan footprints from OS imagery - 3.6. [parking_bays_detectron2](https://github.com/spiyer99/parking_bays_detectron2) -> Detecting parking bays with satellite imagery. Used Detectron2 and synthetic data with Unreal, superior performance to using Mask RCNN +- [parking_bays_detectron2](https://github.com/spiyer99/parking_bays_detectron2) -> Detecting parking bays with satellite imagery. Used Detectron2 and synthetic data with Unreal, superior performance to using Mask RCNN - 3.7. [Locate buildings with a dark roof that feed heat island phenomenon using Mask RCNN](https://towardsdatascience.com/my-rooftop-project-a-satellite-imagery-computer-vision-example-e45a296129a0) -> with [repo](https://github.com/vintel38/RoofTop-Project), used INRIA dataset & labelme for annotation +- [Locate buildings with a dark roof that feed heat island phenomenon using Mask RCNN](https://towardsdatascience.com/my-rooftop-project-a-satellite-imagery-computer-vision-example-e45a296129a0) -> with [repo](https://github.com/vintel38/RoofTop-Project), used INRIA dataset & labelme for annotation - 3.8. [Circle_Finder](https://github.com/zinsmatt/Circle_Finder) -> Circular Shapes Detection in Satellite Imagery, 2nd place solution to the Circle Finder Challenge +- [Circle_Finder](https://github.com/zinsmatt/Circle_Finder) -> Circular Shapes Detection in Satellite Imagery, 2nd place solution to the Circle Finder Challenge - 3.9. [Lawn_maskRCNN](https://github.com/matthewnaples/Lawn_maskRCNN) -> Detecting lawns from satellite images of properties in the Cedar Rapids area using Mask-R-CNN +- [Lawn_maskRCNN](https://github.com/matthewnaples/Lawn_maskRCNN) -> Detecting lawns from satellite images of properties in the Cedar Rapids area using Mask-R-CNN - 3.10. [CropMask_RCNN](https://github.com/ecohydro/CropMask_RCNN) -> Segmenting center pivot agriculture to monitor crop water use in drylands with Mask R-CNN and Landsat satellite imagery +- [CropMask_RCNN](https://github.com/ecohydro/CropMask_RCNN) -> Segmenting center pivot agriculture to monitor crop water use in drylands with Mask R-CNN and Landsat satellite imagery - 3.11. [Mask RCNN for Spacenet Off Nadir Building Detection](https://github.com/ashnair1/Mask-RCNN-for-Off-Nadir-Building-Detection) +- [Mask RCNN for Spacenet Off Nadir Building Detection](https://github.com/ashnair1/Mask-RCNN-for-Off-Nadir-Building-Detection) - 3.12. [CATNet](https://github.com/yeliudev/CATNet) -> Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images +- [CATNet](https://github.com/yeliudev/CATNet) -> Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images - 3.13. [Object-Detection-on-Satellite-Images-using-Mask-R-CNN](https://github.com/ThayN15/Object-Detection-on-Satellite-Images-using-Mask-R-CNN) -> detect ships +- [Object-Detection-on-Satellite-Images-using-Mask-R-CNN](https://github.com/ThayN15/Object-Detection-on-Satellite-Images-using-Mask-R-CNN) -> detect ships - 3.14. [FactSeg](https://github.com/Junjue-Wang/FactSeg) -> Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS), also see [FarSeg](https://github.com/Z-Zheng/FarSeg) and [FreeNet](https://github.com/Z-Zheng/FreeNet), implementations of research paper +- [FactSeg](https://github.com/Junjue-Wang/FactSeg) -> Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS), also see [FarSeg](https://github.com/Z-Zheng/FarSeg) and [FreeNet](https://github.com/Z-Zheng/FreeNet), implementations of research paper - 3.15. [aqua_python](https://github.com/tclavelle/aqua_python) -> detecting aquaculture farms using Mask R-CNN +- [aqua_python](https://github.com/tclavelle/aqua_python) -> detecting aquaculture farms using Mask R-CNN - 3.16. [RSPrompter](https://github.com/KyanChen/RSPrompter) -> Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model +- [RSPrompter](https://github.com/KyanChen/RSPrompter) -> Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model # ## 4. Object detection @@ -1325,7 +1325,7 @@ Detecting the most noticeable or important object in a scene - [3DBuildingInfoMap](https://github.com/LllC-mmd/3DBuildingInfoMap) -> simultaneous extraction of building height and footprint from Sentinel imagery using ResNet --[Solar Panel Detection](https://medium.com/analytics-vidhya/solar-panel-detection-from-aerial-view-or-satellite-images-648c22c260ba) -> using Faster R-CNN & Tensorflow object detection API. With [repo](https://github.com/shiva2410/Solar_Panel-Detection-in-Aerial-Images) +- [Solar Panel Detection](https://medium.com/analytics-vidhya/solar-panel-detection-from-aerial-view-or-satellite-images-648c22c260ba) -> using Faster R-CNN & Tensorflow object detection API. With [repo](https://github.com/shiva2410/Solar_Panel-Detection-in-Aerial-Images) - [DeepSolaris](https://github.com/thinkpractice/DeepSolaris) -> a EuroStat project to detect solar panels in aerial images, further material [here](https://github.com/FHNW-IVGI/workshop_geopython2019/tree/master/Ex.02_SolarPanels) @@ -1627,7 +1627,7 @@ A variety of techniques can be used to count animals, including object detection - [small-object-detection-benchmark](https://github.com/fcakyon/small-object-detection-benchmark) -> Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection (SAHI) --[OD-Satellite-iSAID](https://github.com/muzairkhattak/OD-Satellite-iSAID) -> Object Detection in Aerial Images: A Case Study on Performance Improvement using iSAID +- [OD-Satellite-iSAID](https://github.com/muzairkhattak/OD-Satellite-iSAID) -> Object Detection in Aerial Images: A Case Study on Performance Improvement using iSAID - [Large-Selective-Kernel-Network](https://github.com/zcablii/Large-Selective-Kernel-Network) -> Large Selective Kernel Network for Remote Sensing Object Detection