INTEROPERABLE

Supports CNNs from popular Deep Learning frameworks like Tensorflow, Caffe and YOLO

SCALABLE

Deploys to the Cloud as a Docker Image, REST API and behind your firewall.Deploys to the Cloud as a Docker Image, REST API and behind your firewall.

OPTIMIZED

Built from the ground up to process any size file in a variety of geospatial formats.Built from the ground up to process any size file in a variety of geospatial formats.

DeepCore is a utility toolkit, written in modern C++ (C++11) by Radiant Solutions

It allows a user to download, perform either image classification or object detection, and manipulate geospatial vector files. DeepCore is intended to be a machine learning framework agnostic toolkit, allowing for a simple, clean, consistent programmatic interface. It also provides easy access to the DigitalGlobe imagery archive. As new machine learning techniques and frameworks emerge, they can easily be integrated into DeepCore. This allows developers using DeepCore to easily extend their applications with the latest technology, without having to worry about the complexities of each framework or technique DeepCore’s machine learning features can also be accelerated by the use of Nvidia Graphics Processing Units (GPUs) using CUDA technology. By enabling GPU mode, the process of object detection becomes very quick, allowing for faster and more efficient processing of large geographic areas. The use of GPUs to accelerate machine learning and object detection processes is highly recommended.

Main Modules

The DeepCore SDK Modules are as follows

Download

Choose from the available downloads here

REQUEST DOWNLOAD

Blog Posts

2018 GTC-DC – DeepCore, CityBox and xTerrain

For anyone heading in to DC this afternoon, please attend the presentation by Buzz Roberts[…]

OpenSpaceNet User Guide

OpenSpaceNet is our command line tool built on our DeepCore SDK that allows a user to[…]

Validation and Verification of Machine Learning Detections using Tomnod

In our blog post entitled Discovering Pattern of Life Activity using Machine Learning, we described[…]

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