Parcel Delineation - Fixes #212
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manugv
approved these changes
Jul 15, 2025
manugv
approved these changes
Jul 15, 2025
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| [VITO Remote Sensing](https://remotesensing.vito.be) | ||
| This algorithm performs parcel delineation using Sentinel-2 data and a pre-trained`U-Net machine learning model. The process involves the following steps: |
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There is "`"U-Net. Is that needed?
| @@ -207,7 +207,7 @@ | |||
| }, | |||
| "id": "parcel_delineation", | |||
| "summary": "Parcel delineation using Sentinel-2 data retrieved from the CDSE and processed on openEO.", | |||
| "description": "Parcel delineation using Sentinel-2", | |||
| "description": "# Parcel Delineation\nParcel delineation refers to the identification and marking of agricultural boundaries. \nThis process is *essential* for tasks such as crop yield estimation and land management. \nAccurate delineation also aids in classifying crop types and managing farmland more effectively.\n \n## Algorithm for Parcel Delineation Using Sentinel-2 Data \n\nThis algorithm performs parcel delineation using Sentinel-2 data and a pre-trained`U-Net machine learning model. The process involves the following steps:\n1. **Pre-processing Sentinel-2 Data:**\n 1. Filter data to ensure a maximum of 10% cloud coverage. \n 2. Apply a cloud mask based on the SCL layer. \n2. **Compute NDVI:**\n 1. The Normalized Difference Vegetation Index (NDVI) is calculated from the pre-processed data.\n 2. The NDVI serves as input to the U-Net model. \n3. **Predict Delineation:**\n 1. The U-Net model predicts parcel delineation boundaries. \n4. **Optimization and Labeling:**\n 1. Apply a Sobel filter to enhance edge detection. \n 2. Use Felzenszwalb's algorithm for segmentation and labeling of delineated parcels.\n ", | |||
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This PR should fix the typos in the current parcel delineation service and include a more extensive description.