Fast-track Geospatial AI Applications with Watson Data Platform and Esri ArcGIS
Combine the strengths of IBM Data Platform and Esri ArcGIS to rapidly build machine learning models with high resolution geospatial data. IBM and Esri offer a powerful suite of tools for building first class geospatial applications. In this post, you’ll learn how to leverage deep learning models with Watson Machine Learning and Watson Visual Recognition in your applications. Watson Machine Learning offers a powerful Python API built for controlling the machine learning lifecycle programmatically. You can build, save, and deploy a variety of models. In this post, we’ll save a retrained TensorFlow
image recognition model based on the MobileNet SSD architecture. We’ll also make predictions using a Watson Visual Recognition
classifier.
Locate Concentrated Animal Feeding Operations (CAFOs) from Satellite imagery
We deployed a Watson Machine Learning
model with TensorFlow
trained on satellite images pulled from ArcGIS
to recognize a concentrated animal feeding operation (CAFO)
. The deployed model is accessible via API. Though we develop the model in Python, with Watson Machine Learning, we generate portable code snippets that you can use with cURL, JavaScript, Java, Scala, and more.
The tools involved in this project are:
- Watson Studio — a powerful, scalable interactive cloud computing ecosystem. We’ll use a
Python 3.5
runtime in aSmall
Environment. - Watson Machine Learning — a suite of machine learning tools for data scientists, developers, and analysts of all levels. We’ll save and deploy a deep learning model with the Python API.
- Esri
ArcGIS
— a new Python interface provides contextual tools for mapping and spatial reasoning so you can explore data and share location-based insights. We’ll useArcGIS
to visualize and work with the data for our model. - Collaboration makes your team work smarter, together. Take advantage of best-in-class collaboration tools to track, govern, and develop as a team.
Identifying Broken Insulators at Georgia Power
Use Watson Visual Recognition and Esri ArcGIS
to identify broken electrical insulators — structures which help to control the flow of electric current. Georgia Power uses aerial photography to capture images of their insulators. Then, we can visualize the data and pass the images to Watson Visual Recognition which infers whether a given image contains a damaged or broken insulator. Georgia Power has over 17,000 miles of power lines and 160,000 structures to analyze.
Check out some examples of damaged insulators.
The tools involved in this project are:
- Data Science Experience — a powerful, scalable interactive cloud computing ecosystem. We’ll use a
Python 3.5
runtime in aSmall
Environment. - Watson Visual Recognition — an IBM service built to quickly and accurately tag, classify, and train visual content using machine learning.
- Esri
ArcGIS
— a new python interface provides contextual tools for mapping and spatial reasoning so you can explore data and share location-based insights. We’ll useArcGIS
to visualize and work with the data for our model. - Collaboration — Data Science Experience makes your team work smarter, together. Take advantage of best-in-class collaboration tools to track, govern, and develop as a team.
Check out the video!
Get the code on IBM Data Science Experience.
Thanks to Alberto Nieto and the team at Esri for collaborating on this project!