changes amended as suggested Anushriya
"source": [
"With the change in global climate, glaciers all over the world are experiencing an increasing mass loss, resulting in changing calving fronts. This calving front delineation is important to monitor the rate of glacial mass loss. Currently, most of the calving front delineation is done manually resulting in huge time consumption and under-utilization of satellite imagery.\n",
"\n",
- "Extracting calving fronts from satellite images of marine-terminating glaciers is a two-step process: first segmenting the front using different segmentation techniques and second use of different post-processing mechanisms to extract the terminus line. This notebook presents the use of HRNet model from `arcgis.learn` module to accomplish the first task of segmenting calving fronts. We have used the data provided in [CALFIN](https://github.com/daniel-cheng/CALFIN) repository. The training data includes 1600+ Greenlandic glaciers and 200+ Antarctic glaciers/ice shelves images from Landsat (optical) and Sentinel-1 (SAR) satellites."
+ "Extracting calving fronts from satellite images of marine-terminating glaciers is a two-step process. First, segmenting the Front using different segmentation techniques, and second, post-processing mechanisms to extract the terminus line. This notebook presents the use of HRNet model from `arcgis.learn` module to accomplish the first task of segmenting calving fronts. We have used the data provided in [CALFIN](https://github.com/daniel-cheng/CALFIN) repository. The training data includes 1600+ Greenlandic glaciers and 200+ Antarctic glaciers/ice shelves images from Landsat (optical) and Sentinel-1 (SAR) satellites."
]
},
{
"\n",
"- `Input Raster`: The raster layer you want to classify.\n",
"- `Model Definition`: It will be located inside the saved model in the 'models' folder in '.emd' format.\n",
- "- `Padding`: The 'Input Raster' is tiled and the deep learning model classifies each individual tile separately before producing the final 'Output Classified Raster'. This may lead to unwanted artifacts along the edges of each tile as the model has little context to predict accurately. Padding as the name suggests allows us to supply some extra information along the tile edges, this helps the model to predict better.\n",
- "- `Cell Size`: Should be close to the size used to train the model. This was specified in the Export training data step.\n",
+ "- `Padding`: The 'Input Raster' is tiled and the deep learning model classifies each individual tile separately before producing the final 'Output Classified Raster'. This may lead to unwanted artifacts along the edges of each tile as the model has little context to predict accurately. Padding allows us to supply some extra information along the tile edges, this helps the model to predict better.\n",
+ "- `Cell Size`: Should be close to the size used to train the model.\n",
"- `Processor Type`: This allows you to control whether the system's 'GPU' or 'CPU' will be used to classify pixels, by 'default GPU' will be used if available."
]
},
"source": [
"The gif image below shows the segmented calving front for Rink Isbrae, a major West Greenland outlet glacier achieved using the model trained in this notebook.\n",
"\n",
- "<p align=\"center\"><img src=\"../../static/img/Glacier_tips.gif\" /></p>\n",
- "<br></br>\n",
- "<center>Segmented calving front, Risk Isbrae, West Greenland</center>"
+ "<p align=\"center\"><img src=\"../../static/img/Glacier_tips.gif\" /></p>"
]
},
{
