New ENVI Deep Learning Object Detection Tool Reduces Labeling and Training Time
The newest release of the new ENVI Deep Learning can drastically reduce labeling and training time. If your features of interest are objects rather than landcover, the new object detection tool in ENVI Deep Learning 1.2 provides the ability to create object detection models alongside the existing pixel segmentation. Object labeling is performed by creating vector bounding boxes to encapsulate areas of pixels. You can also easily convert existing pixel segmentation projects to object detection training images by using the “Build Object Detection Raster from ROI” tool. It’s as simple as choosing a starting point and an ending point.
This blog outlines a disaster response project where I mapped rooftop tarps following Hurricane Delta in 2020. When I started the project, the latest imagery available was collected just after Hurricane Delta made its way through the state of Louisiana causing $3 billion in damage. Initially I made a pixel segmentation Deep Learning model to classify blue and green tarps using aerial imagery hosted by the NOAA’s Emergency Response Imagery website https://storms.ngs.noaa.gov/. This dataset contained 1,233 tiled images covering 2.8 by 2.8 km with a spatial resolution of 20 cm.
Out of the 1,233 tiled images, I chose six tiles containing damaged residential areas to label both blue and green tarps to train a pixel segmentation deep learning model. Once the model was trained, I used ENVI Modeler to batch classify each of the 1,233 tiles, convert the classification images to shapefiles and then upload to ArcGIS Portal for public dissemination
Visit L3Harris Blog to see all details on the March 2022 ArcGIS Field Maps Updates .
Source: JP Metcalf 2022, L3Harris Geospatial, accessed 06 April 2022, <L3Harris Blog>