New Pretrained Deep Learning Models (September 2022)

Our library of pretrained deep learning models in ArcGIS Living Atlas of the World is growing! Eliminating the need for huge volumes of training data, massive compute resources, and extensive artificial intelligence (AI) knowledge, users can leverage pretrained models to accelerate their geospatial workflows and extract meaningful insights from imagery. 

As of September 2022, users can now choose from 43 different pretrained models to use. These models are available as deep learning packages (DLPKs) that can be used with ArcGIS Pro, Image Server and ArcGIS API for Python.

Here is an overview of our newer models:  

Water Body Extraction (SAR) – USA

Water management activities such as monitoring the changing course of rivers and streams, regional planning, flood management, agriculture require survey and planning, including accurate mapping of water bodies. Hence, extraction of water bodies from remote sensing data is critical to record how this dynamic changes and map their current forms. This deep learning model can be used to automate the task of extracting water bodies from SAR imagery.

Extract water bodies from Sentinel-1 data

Building Footprint Extraction – China

Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Use this deep learning model to automate this process; reduce  time and effort required for acquiring building footprints in China from high-resolution (15–25 cm) imagery.

Extract building footprints in China from high-resolution aerial or satellite imagery

Tree Detection

Tree detection can be used for applications such as vegetation management, forestry, urban planning, and so on. This deep learning model is used to detect trees in high-resolution drone or aerial imagery.   

Detect trees in high resolution imagery

Seabird (Tern) Detection – Africa

The Royal tern and Caspian tern are two of 350 seabird species. These adult terns could be of size 45-60 cm weighing 350-750 gm. Their size puts them in the category of small objects and thus we need very high-resolution imagery to detect them. This deep learning model helps automate the task of detecting seabirds (Royal and Caspian terns) from high-resolution aerial imagery to help map effective site protection areas for seabirds.

Deep learning model to detect seabird (tern) using aerial imagery

Elephant Detection 

Elephants are the largest terrestrial living species and are endangered due to many reasons. To avoid life-threatening incidents, and for their conservation, monitoring the elephants and their movements is of high importance. This deep learning model helps automate the task of detecting elephants from high-resolution aerial imagery. 

Detect elephants using aerial imagery
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