How To: Classify a Feature with Multiple Labels Using ArcGIS Pro
TLDRThis tutorial video demonstrates how to utilize a multi-label feature classification deep learning model with ArcGIS Pro. It explains the concept of multi-label classification, which allows features to have multiple labels simultaneously, unlike traditional classifiers that assign a single label. The video provides a practical example of using this model to survey residential properties for the presence of swimming pools and solar panels, a task that would be time-consuming if done manually. The workflow is divided into three steps: exporting the label dataset, training the model with the exported data, and performing inference using the trained model. The video also covers the process of exporting training data, training the deep learning model with customizable parameters, and classifying objects using the trained model. The result is a high-resolution, efficient method for identifying features with multiple attributes.
Takeaways
- 📚 **Multi-Label Feature Classification**: The video explains how to use a deep learning model in ArcGIS Pro that can classify features with multiple labels, unlike traditional classifiers that assign a single label.
- 🏠 **Real-World Application**: The example given is classifying residential properties to identify those with swimming pools and solar panels, which can be useful for property valuation and other assessments.
- 📈 **Workflow Overview**: The process involves three main steps: exporting the label dataset, training the model using the exported data, and performing inference with the trained model.
- 🔍 **Attribute Table Details**: The script highlights the importance of field names in the attribute table for multi-label classification, which follow a specific format to identify labels.
- 📏 **Exporting Label Data**: The process of exporting the label dataset is described, including setting parameters such as tile size and metadata format.
- 🤖 **Model Training**: The video outlines how to train the deep learning model with the exported data, including customizable parameters like the number of epochs and batch size.
- 💻 **GPU Utilization**: It is mentioned that using a GPU can accelerate the model training process, which is an option provided in the tool.
- 📉 **Inference Process**: The final step of using the trained model to classify objects is explained, including adjusting batch size and using a GPU for faster inference.
- 🏊♂️ **Example Inference Results**: The video demonstrates the results of the classification, showing high confidence levels for correct classifications, such as properties with pools or solar panels.
- 📋 **Label Field Naming Convention**: The takeaway emphasizes the naming convention for multi-label fields, which helps the tool automatically identify and use the label names.
- 🕒 **Time Efficiency**: The entire workflow is presented as a time-saving method that can accomplish tasks that would otherwise take weeks or months to complete manually.
Q & A
What is a multi-label feature classifier in the context of ArcGIS Pro?
-A multi-label feature classifier is a type of deep learning model that can assign multiple labels to a single feature, unlike traditional classifiers that assign only one label per feature.
How does a multi-label classifier differ from a single-label classifier?
-A multi-label classifier can classify a feature as having multiple attributes or labels simultaneously, whereas a single-label classifier can only assign one label to each feature.
What is the practical application of using a multi-label classifier for residential properties?
-A multi-label classifier can be used by city authorities to survey residential properties and determine which properties have features like swimming pools and solar panels, which can help in assessing property rates and understanding the affinity level in the area.
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 ArcGIS Pro?
-The label data set is exported using the 'Export Training Data for Deep Learning' tool, where the user provides the input raster, input feature layer, and specifies the type and metadata format as multi-label tiles.
What is the significance of the field names in the attribute table for multi-label classification?
-The field names for multi-label classification follow a specific format, starting with 'multi-label M_L_' followed by the name of the label. This format helps the tool automatically identify and pick up the label names for classification.
Why is the 'multi-label none' field important in the dataset?
-The 'multi-label none' field is important because it allows the model to learn and classify land parcels that do not have any of the specified features, such as neither a swimming pool nor a solar panel.
What are some parameters that can be adjusted when training a deep learning model in ArcGIS Pro?
-Parameters that can be adjusted include the number of epochs, batch size, chip size, learning rate, and the choice of different model backbones. Additionally, the validation set can be split differently, and the processing environment, such as using a GPU, can be specified.
How is the inference performed using a trained deep learning model in ArcGIS Pro?
-Inference is performed using the 'Classify Objects Using Deep Learning' tool, where the user provides the input features, the trained model definition, and can adjust the batch size and environment settings, such as using a GPU for faster processing.
What does the confidence score in the inference results indicate?
-The confidence score represents the model's certainty in its classification of a feature. A higher score indicates a higher confidence in the assigned labels.
How does the multi-label feature classifier help in automating the survey of residential properties?
-The multi-label feature classifier automates the survey process by classifying features with multiple attributes, reducing the time required from weeks to months to just a few hours, making the process more efficient.
Outlines
📚 Introduction to Multi-Label Feature Classification in ArcGIS Pro
The video begins by introducing a multi-label feature classification deep learning model for use with ArcGIS Pro. It explains that unlike single-label classifiers, multi-label models can assign multiple labels to a single feature. This is illustrated with an example of classifying features as A, B, C, or combinations thereof. The practical application discussed involves city authorities using the model to survey residential properties for the presence of swimming pools and solar panels. The workflow is outlined in three steps: exporting the label dataset, training the model with the dataset, and performing inference using the trained model. The video also covers how to interpret the attribute table and field names specific to multi-label classification, and demonstrates the export process with a high-resolution setting.
🔧 Training and Inference with the Deep Learning Model
The second paragraph delves into the training process of the deep learning model. It discusses customizable parameters such as the batch size, chip size, learning rate, and model backbones. The video also covers the use of a GPU for training, if available, and how to initiate the training process. After training, the model is used for inference with the 'Classify Objects Using Deep Learning' tool. The inference process is demonstrated with a pre-trained model, highlighting how to adjust batch size and environment settings. The results of the inference are shown, with the model accurately classifying land parcels as having swimming pools, solar panels, or neither, along with the confidence levels for each classification. The video concludes with a recap of the process and a thank you note to the viewers.
Mindmap
Keywords
💡Multi-label Feature Classifier
💡ArcGIS Pro
💡Deep Learning Model
💡Land Parcel Polygons
💡Attribute Table
💡Export Training Data
💡Model Training
💡Inference
💡Batch Size
💡GPU
💡Confidence Score
Highlights
The video demonstrates how to use a multi-label feature classifier deep learning model with ArcGIS Pro.
Multi-label models can classify features with multiple labels, unlike traditional single-label classifiers.
An example is given where a feature can be classified as having both a swimming pool and solar panels.
The workflow for using the model involves exporting a label dataset, training a model, and performing inference.
City authorities can use this model to survey residential properties for amenities like swimming pools and solar panels.
The process can significantly reduce the time taken for manual surveys from weeks to just a few hours.
The attribute table for multi-label classification has a specific format with fields named 'multi-label M_L_
The tool automatically identifies labels from the attribute table based on the specified prefix.
Exporting the label dataset is done using the 'Export Training Data for Deep Learning' tool with specific parameters.
High resolution is chosen for the tile size to ensure detailed analysis of features like solar panels.
The deep learning model is trained using the exported data with customizable parameters like batch size and learning rate.
Different model backbones can be experimented with during the training process.
Inference is performed using the 'Classify Objects Using Deep Learning' tool with the trained model.
The model can accurately classify land parcels with or without amenities and provide confidence scores for each label.
The video shows an example where a land parcel is correctly classified as having no amenities with high confidence.
The multi-label feature classifier deep learning model in ArcGIS Pro can greatly enhance the efficiency of property surveys.
The video provides a step-by-step guide on how to use the multi-label feature classifier for property feature classification.
The model training can take some time, but the results can be used for multiple inferences to save time in the long run.
The video concludes by thanking viewers for their interest in using a multi-label feature classifier with ArcGIS Pro.