DZone

With the firehose of imagery that’s streaming down daily from a variety of sensors, the need for using AI to automate feature extraction is only increasing. Deep learning models can be readily used to automate the tedious task of digitizing and extracting geographical features from satellite imagery and point cloud datasets. Here are six pre-trained models that you can use for everything from extracting building footprints to detecting shipwrecks.

Building Footprint Extraction

The Building Footprint Extraction model is the most popular model so far. This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications.

Source: DZone