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Update README.md
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robmarkcole committed May 3, 2024
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Expand Up @@ -1356,7 +1356,7 @@ Detecting the most noticeable or important object in a scene

- [mappingchallenge](https://github.com/krishanr/mappingchallenge) -> YOLOv5 applied to the AICrowd Mapping Challenge dataset

### Object detection - Ships & boats
### Object detection - Ships, boats, vessels & wake

- [kaggle-ships-in-Google-Earth-yolov5](https://github.com/robmarkcole/kaggle-ships-in-Google-Earth-yolov5) -> Applying YOLOv5 to Kaggle Ships in Google Earth dataset

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- [vessel-detection-viirs](https://github.com/allenai/vessel-detection-viirs) -> Model and service code for streaming vessel detections from VIIRS satellite imagery

- [wakemodel_llmassist](https://github.com/pbeukema/wakemodel_llmassist) -> wake detection in Sentinel-2, uses an EfficientNet-B0 architecture adapted for keypoint detection

### Object detection - Cars, vehicles & trains

- [Detection of parkinglots and driveways with retinanet](https://github.com/spiyer99/retinanet)
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- [Cloud-removal-model-collection](https://github.com/littlebeen/Cloud-removal-model-collection) -> A collection of the existing end-to-end cloud removal models

- [SEnSeIv2](https://github.com/aliFrancis/SEnSeIv2) -> Sensor Independent Cloud and Shadow Masking with Ambiguous Labels and Multimodal Inputs

#
## Change detection

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- [ClearSCD](https://github.com/tangkai-RS/ClearSCD) -> Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery

- [RSCaMa](https://github.com/Chen-Yang-Liu/RSCaMa) -> Remote Sensing Image Change Captioning with State Space Model

- [ChangeBind](https://github.com/techmn/changebind) -> A Hybrid Change Encoder for Remote Sensing Change Detection

#
## Time series

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### Single image super-resolution (SISR)

- [sentinel2_superresolution](https://github.com/Evoland-Land-Monitoring-Evolution/sentinel2_superresolution) -> Super-resolution of 10 Sentinel-2 bands to 5-meter resolution, starting from L1C or L2A (Theia format) products. Trained on Sen2Venµs

- [Super Resolution for Satellite Imagery - srcnn repo](https://github.com/WarrenGreen/srcnn)

- [TensorFlow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" adapted for working with geospatial data](https://github.com/CosmiQ/VDSR4Geo)
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