How To: Classify a Feature with Multiple Labels Using ArcGIS Pro
TLDRThis video tutorial demonstrates how to employ a multi-label feature classified deep learning model within ArcGIS Pro. It explains the concept of multi-label classification, which allows features to be tagged with multiple labels simultaneously, unlike traditional single-label classifiers. The workflow is divided into three steps: exporting a labeled dataset, training a deep learning model using the dataset, and performing inference with the trained model. The video uses a real-world example of classifying residential properties with features like swimming pools and solar panels, showcasing the efficiency and high-resolution capabilities of the model. The process is detailed, including the setup for exporting data, model training parameters, and the inference process, ultimately leading to accurate classification results.
Takeaways
- 🌟 A multi-label feature classifier is a deep learning model that can assign multiple labels to a single feature, unlike traditional classifiers that assign only one label.
- 🏠 The example given is classifying residential properties based on the presence of swimming pools and solar panels, which can be time-consuming manually but can be expedited using this model.
- 📊 The workflow consists of three parts: exporting the label dataset, training the model using the exported dataset, and performing inference with the trained model.
- 🗂️ The dataset includes land parcel polygons marked with presence (1) or absence (0) of features like swimming pools and solar panels.
- 📝 Field names in the multi-label dataset follow a specific format, which helps the tool automatically identify and label the features.
- 🔍 Exporting the label dataset involves using the 'Export Training Data for Deep Learning' tool, specifying input raster and feature layer, and setting parameters like cell size and metadata format.
- 🛠️ Training the deep learning model is done using the 'Train Deep Learning Model' tool, where you can adjust parameters like the number of epochs, batch size, and learning rate.
- 📱 The tool also allows for GPU acceleration if available, which can speed up the training process.
- 🔮 Inference is performed using the 'Classify Objects Using Deep Learning' tool, which applies the trained model to classify features in the input features.
- 📈 The confidence levels for each label are displayed, allowing users to assess the accuracy of the classification.
- 🎥 The video demonstrates the entire process in ArcGIS Pro, showcasing the efficiency of using a multi-label feature classifier for complex classification tasks.
Q & A
What is a multi-label feature classifier?
-A multi-label feature classifier is a type of model that can assign multiple labels to a single feature, unlike traditional classifiers that assign only one label.
How does a multi-label feature classifier differ from a single-label classifier?
-A single-label classifier can only assign one label to a feature, whereas a multi-label feature classifier can assign multiple relevant labels to the same feature.
What is an example of a task that benefits from multi-label classification?
-A city authority surveying residential properties to identify which ones have swimming pools and solar panels is an example that benefits from multi-label classification.
What are the three parts of the workflow for using a multi-label feature classifier in ArcGIS Pro?
-The workflow consists of exporting the label dataset, training a model using the exported dataset, and performing inference using the trained model.
How is the label data set exported in the multi-label classification process?
-The label data set is exported using the 'Export Training Data for Deep Learning' tool, which requires specifying input raster, input feature layer, and other parameters like tile size and metadata format.
What is the specific format for field names in a multi-label classification dataset?
-The field names have a specific format: 'multi-label_ML_' followed by the name of the label, which helps the tool automatically identify and use the label names.
What is the purpose of the 'multi-label none' field in the dataset?
-The 'multi-label none' field is used to represent land parcels that do not have any of the features being classified, such as swimming pools or solar panels.
How long does it typically take to train a deep learning model for multi-label classification?
-The training time can vary depending on factors like the complexity of the model, the size of the dataset, and the computational resources available, such as GPU memory.
What are some parameters that can be adjusted during the training of a deep learning model?
-Parameters that can be adjusted include the number of epochs, batch size, chip size, learning rate, and the choice of model backbones.
How is the inference process performed in ArcGIS Pro using a trained multi-label model?
-The inference is performed using the 'Classify Objects Using Deep Learning' tool, where the input features and the trained model definition are provided, along with other settings like batch size and environment.
What does the confidence score in the inference results indicate?
-The confidence score indicates the certainty of the model's classification for a particular feature, with higher scores representing greater confidence in the assigned labels.
Outlines
📚 Introduction to Multi-Label Feature Classification
This paragraph introduces the concept of multi-label feature classification in deep learning models, contrasting it with single-label classifiers. It explains that multi-label models can assign multiple labels to a single feature, unlike traditional classifiers. The example given involves classifying residential properties based on the presence of swimming pools and solar panels, showcasing the efficiency of this method over manual surveys. The workflow is outlined in three parts: exporting label data, training a model, and performing inference.
🔍 Exporting Label Data for Training
The paragraph details the process of exporting a label dataset for training a multi-label classifier. It describes the use of land parcel polygons as features and the importance of the specific field names format for multi-label classification. The script also explains how to use the 'Export Training Data for Deep Learning' tool, including setting parameters such as cell size and metadata format, and the need to blacken around features for high-resolution training data.
🤖 Training the Deep Learning Model
This section covers the training of the deep learning model using the exported label dataset. It discusses the configuration options available, such as batch size, chip size, learning rate, and model backbones. The importance of selecting the appropriate GPU environment for training is highlighted, and the paragraph notes that model training can be time-consuming, though for the demo, the model has already been trained.
🔮 Performing Inference with the Trained Model
The final paragraph demonstrates the use of the trained model for inference using the 'Classify Objects Using Deep Learning' tool. It explains how to set up the tool with the input features and the trained model, and the importance of maintaining consistent batch size and environment settings. The results of the inference are shown, with the model accurately classifying features based on the presence or absence of swimming pools and solar panels, and the confidence levels for each classification are discussed.
Mindmap
Keywords
💡Multi-label Feature Classifier
💡Deep Learning Model
💡ArcGIS Pro
💡Land Parcel Polygons
💡Label Data Set
💡Training Data
💡Inference
💡Export Training Data
💡Attribute Table
💡Batch Size
💡Backbones
Highlights
The video demonstrates how to use a multi-label feature classified deep learning model with ArcGIS Pro.
A multi-label classifier can assign multiple labels to a single feature, unlike traditional classifiers which assign only one label.
The video uses the example of classifying residential properties with swimming pools and solar panels.
The workflow consists of three parts: exporting the label dataset, training a model, and performing inference.
The dataset includes land parcel polygons marked with the presence (1) or absence (0) of features like swimming pools and solar panels.
The attribute table has a specific format for multi-label fields, with a prefix that helps the tool identify and use the label names.
Exporting the label dataset involves using the 'Export Training Data for Deep Learning' tool with specific parameters.
Training the deep learning model is done using the 'Train Deep Learning Model' tool, with options to adjust parameters like batch size and learning rate.
The video mentions the use of different backbones for the model and the possibility of using a GPU for training.
Inference is performed using the 'Classify Objects Using Deep Learning' tool, applying the trained model to classify features.
The video shows how the model can accurately classify features, such as identifying a land parcel with both a swimming pool and solar panels.
The model also correctly classifies land parcels with no features, showing high confidence for the 'none' label.
The video concludes by summarizing the process and showcasing the practical applications of the multi-label feature classifier in ArcGIS Pro.
