You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This algorithm performs parcel delineation using Sentinel-2 data and a pre-trained`U-Net machine learning model. The process involves the following steps:
9
+
1.**Pre-processing Sentinel-2 Data:**
10
+
1. Filter data to ensure a maximum of 10% cloud coverage.
11
+
2. Apply a cloud mask based on the SCL layer.
12
+
2.**Compute NDVI:**
13
+
1. The Normalized Difference Vegetation Index (NDVI) is calculated from the pre-processed data.
14
+
2. The NDVI serves as input to the U-Net model.
15
+
3.**Predict Delineation:**
16
+
1. The U-Net model predicts parcel delineation boundaries.
17
+
4.**Optimization and Labeling:**
18
+
1. Apply a Sobel filter to enhance edge detection.
19
+
2. Use Felzenszwalb's algorithm for segmentation and labeling of delineated parcels.
Copy file name to clipboardExpand all lines: algorithm_catalog/vito/parcel_delineation/openeo_udp/parcel_delineation.json
+1-1Lines changed: 1 addition & 1 deletion
Original file line number
Diff line number
Diff line change
@@ -207,7 +207,7 @@
207
207
},
208
208
"id": "parcel_delineation",
209
209
"summary": "Parcel delineation using Sentinel-2 data retrieved from the CDSE and processed on openEO.",
210
-
"description": "Parcel delineation using Sentinel-2",
210
+
"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 ",
0 commit comments