Make Every Tree Count with ArcGIS | Esri India

 Urban tree inventories are crucial for tracking green goals and identifying opportunities to enhance tree cover efficiently. Traditional methods like field surveys or manual digitization of imagery are time-consuming and labor-intensive. To expedite this process, automated tree inventories using deep learning tools in ArcGIS prove invaluable.

Speeding Up Tree Detection

For quick tree detection in your neighborhood, leverage pre-trained models like the Tree detection model available in ArcGIS Living Atlas of the World.

Customizing Models for Your Geography

You have the flexibility to create custom tree detection models tailored to your specific geography using ArcGIS Pro with Image Analyst. This involves:

1. Labeling Trees: Use tools such as Label Objects for Deep Learning pane or Training Samples Manager to mark trees as the feature of interest.

2. Exporting Training Data: Export training data in formats like KITTI Labels, RCNN Masks, or PASCAL Visual Object Classes to train models such as MaskRCNN or FasterRCNN.

3. Training and Deployment: Utilize the "Train Deep Learning Model" geoprocessing tool to train your model. Once trained, deploy it with "Detect Objects Using Deep Learning" to detect trees in high-resolution imagery.

Achieving Objectives in Noida

In a practical application in Noida, I used ArcGIS Pro Image Analyst Deep Learning tools to accomplish the following:

• Tree Count: Detected approximately 137,224 trees in 158 minutes, significantly faster than manual methods.
• Canopy Size Breakdown: Classified trees based on canopy size using arcade expressions, distinguishing between large, medium, and small trees.
• Tree Density Mapping: Created a tree density map to pinpoint sectors with low tree coverage, using ArcGIS Dashboards for visual representation.

Enhanced Insights and Resources

With ArcGIS, enrich your tree inventory data using Esri GeoEnrichment Service to obtain statistics such as trees per square kilometer, per person, or per household.

Conclusion

By harnessing deep learning capabilities in ArcGIS Pro, I efficiently conducted comprehensive tree inventories in Noida, achieving accurate tree counts, canopy size classifications, and detailed tree density mapping. This approach not only streamlines urban management efforts but also provides actionable insights for sustainable urban planning and development.

Further Reading

Explore more about deep learning tools in ArcGIS and install necessary frameworks to empower your own projects:

• Deep Learning in ArcGIS Pro
• Install Deep Learning Frameworks for ArcGIS

With these tools and resources, you can accelerate your urban tree inventories and pave the way for greener, more sustainable cities.

To Know More Visit : Esri India