Extracting features and Land Use Land Cover using Panoptic Segmentation

• Data Science
• Deep Learning and Panoptic Segmentation

Introduction

Land cover classification is one of the most common tasks in remote sensing. It can have multiple applications and can be very useful in understanding an area, its geography, landscape, demographic, and more. Landcover classification can be further combined with building footprint extraction and object detection for even more detailed analysis. Currently, this is achieved through multiple models. However, using Panoptic Segmentation, we can detect objects of interest and also classify the pixels of a raster using a single model.

Panoptic Segmentation works with both types of classes - stuff and things. In deep learning jargon, stuff refers to the class that is indistinctive and uncountable, for example, vegetation, road, water, sky, etc. Most of the land cover classes will classify as stuff. Things refers to classes that are distinctive, for example, buildings, cars, pools, solar panels, etc., and are also referred to as instance classes.

Panoptic Segmentation models use three inputs during training - a raster, a feature layer or a classified raster that classifies each pixel in the raster into individual classes, and a feature layer containing polygons for the objects of interest (the instance classes). This notebook demonstrates how to use the Panoptic Segmentation model MaXDeepLab to classify Land Use Land Cover (LULC) and extract building footprints and cars using very high-resolution satellite imagery of Los Angeles County. The model has been trained using the ArcGIS Python API. The trained model can then be deployed in ArcGIS Pro or ArcGIS Enterprise to classify LULC and extract building footprints and cars.

Export training data

# Connect to GIS
from arcgis.gis import GIS
gis = GIS("home")
gis_ent = GIS('https://pythonapi.playground.esri.com/portal/')

The following imagery layer contains very high resolution imagery of a part of LA County in California. The spatial resolution of the imagery is 7.5 cm (4 inches), and it contains 3 bands: Red, Green, and Blue. It is used as the 'Input Raster' for exporting the training data.

training_raster = gis_ent.content.get('c3bb538699964697bae61570a77768cb')
training_raster

la_county_rgb_raster
RGB Raster for a part of LA CountyImagery Layer by api_data_owner
Last Modified: January 02, 2023
0 comments, 2 views

To export training data for panoptic segmentation, we need two input layers.

1. A feature class or a classified raster for pixel classification.
2. A feature class with polygons for the instance classes to be detected.

The following feature layer contains the polygons for the LULC classes for the corresponding imagery of LA County. It is used as the 'Input Feature Class' for exporting the training data.

input_feature_class = gis.content.get('d64f11aea3ae47c19084f21c204ba318')
input_feature_class

input_features_lulc
Feature layer with Land Use Land Cover for a region of LA CountyFeature Layer Collection by api_data_owner
Last Modified: September 13, 2022
0 comments, 2 views

The following feature layer contains the polygons for the building footprints and cars and will be used as the 'Instance Feature Class' for exporting the training data.

instance_feature_class = gis.content.get('22b83520db8f42ea98a5279b71684afd')
instance_feature_class

instance_features_buildings_cars
A feature layer containing polygons for building footprints and cars in a region of LA County.Feature Layer Collection by api_data_owner
Last Modified: September 13, 2022
0 comments, 1 views

Training data can be exported by using the Export Training Data For Deep Learning tool available in ArcGIS Pro and ArcGIS Enterprise. For this example, we prepared the training data in ArcGIS Pro in the Panoptic Segmentation format using a chip_size of 512px and a cell_size of 0.32 ft. The Input Raster, the Input Feature Class and the Instance Feature Class have been made available to export the required training data. We have also provided a subset of the exported training data in the next section, if you want to skip this step.

Train the model

Necessary imports

%matplotlib inline

import os
import zipfile
from pathlib import Path
from arcgis.learn import prepare_data, MaXDeepLab

Get training data

We have already exported a smaller dataset and made it available so that it can be used by following the steps below. Exporting a larger dataset is suggested for better results.

training_data = gis.content.get('69544aca740d42d3a3e36f7ad5ddb485')
training_data

panoptic_segmentation_training_data
Sample training data in Panoptic Segmentation format.Image Collection by api_data_owner
Last Modified: September 14, 2022
0 comments, 2 views

filepath = training_data.download(file_name=training_data.name)

import zipfile
with zipfile.ZipFile(filepath, 'r') as zip_ref:
    zip_ref.extractall(Path(filepath).parent)

data_path = Path(os.path.join(os.path.splitext(filepath)[0]))

Prepare data

We will specify the path to our training data and a few hyperparameters.

• data_path: path of the folder/list of folders containing training data.
• batch_size: Number of images your model will train on each step inside an epoch. Depends on the memory of your graphic card.
• chip_size: The same as the tile size used while exporting the dataset. A smaller chip_size crops the tile.
• n_mask: The maximum number of masks, i.e., the sum of unique instances of all things classes and total stuff classes, in a chip. The default value is 30. You can increase it if there are many objects in a single chip. A warning message will be displayed in some of the methods if the n_mask value is less than the number of objects in a chip. The function compute_n_masks can be used to calculate the maximum number of objects in any chip in a dataset.

batch_size, chip_size, and n_mask will define the GPU memory requirements.

data = prepare_data(data_path, batch_size=4, chip_size=512, n_masks=30)

Visualize training data

To get a sense of what the training data looks like, the show_batch() method will randomly pick a few training chips and visualize them. The chips are overlaid with masks representing the building footprints, cars, and the lulc classes in each image chip.

data.show_batch(alpha=0.8)

Load model architecture

arcgis.learn provides the MaXDeepLab model for panoptic segmentation task.

model = MaXDeepLab(data)

Find an optimal learning rate

Learning rate is one of the most important hyperparameters in model training. The ArcGIS API for Python provides a learning rate finder that automatically chooses the optimal learning rate for you.

lr = model.lr_find()
lr

0.001445439770745928

Fit the model

Next, we will train the model for a few epochs with the learning rate found above. We have trained the model using a large dataset for 20 epochs. If the loss continues to decrease, the model can be trained further until you start seeing overfitting.

model.fit(20, lr=lr)

We can see in the following plot that although the training loss was contantly reducing gradually, the validation loss became erratic after 12th epoch. The smallest validation loss was recorded for the 17th epoch.

MaXDeepLab has a compound loss function - a combination of multiple metrics. It has been observed that longer training with a relatively flat validation loss curve can also improve results. A diverging training and validation loss curve will still reflect overfitting.

model.plot_losses()

Visualize results in validation set

It is a good practice to see the results of the model viz-a-viz ground truth. The code below picks random samples and shows us ground truth and model predictions, side by side. This enables us to preview the results of the model we trained.

model.show_results(alpha=0.8)

Accuracy assessment

MaXDeepLab provides the panoptic_quality() method that computes the panoptic quality, a combination of recognition and segmentation quality, of the model on the validation set.

model.panoptic_quality()

Save the model

We will save the trained model as a 'Deep Learning Package' ('.dlpk' format). The Deep Learning package is the standard format used to deploy deep learning models on the ArcGIS platform.

We will use the save() method to save the trained model. By default, it will be saved to the 'models' sub-folder within our training data folder.

model.save("la_panoptic_512px_ep21_lr001", publish=True)

Deploy the model

We can now use the saved model to detect buildings and cars and classify the LULC. Here, we have provided a very high resolution sample raster for LA County for inferencing. The spatial resolution of the imagery is 0.32 ft and contains 3 bands: Red, Green, and Blue.

sample_inference_raster = gis_ent.content.get('0c8288d76d1c4301ab8b5efbad358f39')
sample_inference_raster

panoptic_inference_raster
RGB raster to be used for panoptic segmentation sample notebook for inference.Imagery Layer by api_data_owner
Last Modified: September 13, 2022
0 comments, 0 views

We have provided a trained model to use for inferencing.

trained_model = gis.content.get('4ef21be1d9fb4ced9eacb306558e8edd')
trained_model

la_panoptic_512px_ep21_lr001
Panoptic Segmentation Deep Learning modelDeep Learning Package by api_data_owner
Last Modified: September 14, 2022
0 comments, 0 views

In this step, we will use the Detect Objects Using Deep Learning tool available in both ArcGIS Pro and ArcGIS Enterprise to generate a feature layer with detected buildings and cars and a classified raster with the LULC.

Results

The model was run on the sample inference raster and the results can be viewed here.

fc = gis_ent.content.get('3b5033eb16044a089795682256e819ae')
fc

panoptic_resultsCP
Classified raster with LULC classes. The result of panoptic segmentation.Imagery Layer by api_data_owner
Last Modified: September 14, 2022
0 comments, 4 views

ic = gis.content.get('ca935d3e3e614e2392485e07ab56b97f')
ic

panoptic_results
The instance feature layer result from panoptic segmentation.Feature Layer Collection by api_data_owner
Last Modified: September 14, 2022
0 comments, 7 views

Visualize results using map widgets

Two map widgets are created showing the inference raster and the results.

map1 = gis_ent.map()
map1.add_layer(inference_raster)
map2 = gis.map()
map2.add_layer(fc)
map2.add_layer(ic)

The maps are synchronized with each other using MapView.sync_navigation functionality. It helps in comparing the inference raster with the results. Detailed description about advanced map widget options can be referred here.

map2.sync_navigation(map1)

from ipywidgets import HBox, VBox, Label, Layout

Set the layout of the map widgets using Hbox and Vbox.

hbox_layout = Layout()
hbox_layout.justify_content = 'space-around'

hb1=HBox([Label('Raster'),Label('Results')])
hb1.layout=hbox_layout

VBox([hb1,HBox([map1,map2])])

Conclusion

In this notebook we saw how the panoptic segmentation model MaXDeepLab can be used to classify pixels and detect objects at the same time. We learned how to create the data in the Panoptic Segmenation format and use it to train a model using the ArcGIS API for Python. The trained model was then used to generate results for a sample raster and was displayed in the notebook.