Machine Learning in the Geospatial Industry: A Beginners Guide
Modelingposted by ODSC Community August 23, 2022 ODSC Community
Geospatial data just means data that is associated with locations. As the geospatial industry evolves, so are the ways in which geospatial professionals use data to solve problems.
That’s why we’re seeing the rise of AI and ML in this industry. But what exactly is machine learning, how can you apply it in your geospatial work, and how can you start a path to learning machine learning? Keep reading to find out.
What is Machine Learning?
Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions based on those learnings. Basically, machine learning algorithms are like a child — they grow and improve as they’re exposed to new information.
A well-trained machine learning model can “think” for itself, find patterns in data, and make predictions based on those patterns. Over time, machine learning is able to identify the relationships between variables and make connections between data points that humans might not even notice.
ML in Geospatial: Where to Begin
There is a diversifying trend in the variety of machine learning topics. Like any other industry, geospatial professionals have found ways to incorporate machine learning into their workflows. But where do you even begin?
Here are some of the most common ways the geospatial industry is using machine learning today.
Classification – Classification is used for determining which category something belongs to. In the world of geospatial data, the most common usage is using deep learning to develop land use/land cover (LULC) from satellite imagery. An example of this is Google’s LULC product that is available in Google Earth Engine.
Clustering – The goal of spatial data clustering is to find groups of objects that all have similar properties. Clustering can also be performed over time to find patterns and trends over time using machine learning techniques.
Regression – Basically, regression algorithms can be used to uncover the relationship between two or more variables. For example, you can use regression with Python to determine the relationship between rainfall and the number of sales at your store. In this case, both variables have geospatial attributes attached to them.
Predictive Analytics – Predictive analytics is the process of identifying what may happen in the future based on data from the past. Through geospatial pattern recognition, users can find relationships between variables in data that might otherwise go unnoticed.
Automatic Tagging – Automatic tagging is the process of tagging geospatial data with relevant keywords. Companies like Mapillary are leveraging computer vision to tag 40+ types of objects from street view cars and adding XY locations.
The Importance of Big Data and Geospatial Analytics
Big data has become increasingly important in the geospatial industry — and has allowed us to discover new ways to use machine learning to solve problems. Big data is basically a large collection of data that is difficult to process using traditional methods. But it often comes in unstructured formats.
Big data has become increasingly important in the geospatial industry — and has led to the rise of geospatial analytics. Geospatial analytics is the process of applying data analytics, visualization, and other business intelligence methods to analyze data that has a geographic component.
For example, tools like ArcGIS Velocity consume real-time sensors to analyze “Big Data” that has a geographic component such as the Internet of Things (IoT). Leveraging cloud technologies allows you to visualize and analyze large volumes of data for better insights.
Education Path for Machine Learning in GIS
As mentioned, the geospatial industry is constantly evolving, and so are the skill sets required of geospatial professionals. With the rise of AI and ML in this industry, it’s important for geospatial practitioners to understand how to apply these new tools to their work.
That being said, if you want to start a career in machine learning, you need to realize that it’s not an easy path. Machine learning is an interdisciplinary field, meaning it requires knowledge from different disciplines — like computer science, mathematics, and statistics. To fill in this demand, there is online machine learning certification that provides you with the most relevant skills you need.
If you want to pursue a career in machine learning, it’s a good idea to start learning programming languages like Python and R. Additionally, some of the most popular libraries you can learn in machine learning are TensorFlow, PyTorch, and Keras.
If you’re just getting started in the geospatial industry and want to use machine learning in your workflows, it’s important to understand the landscape of machine learning first. And if you want to pursue a career in machine learning, it’s also a good idea to start learning some of the most commonly used programming languages today.
Machine learning is a powerful tool for geospatial professionals and is a great way to uncover hidden insights from your data. There are tons of reasons why geospatial professionals use machine learning — some of the most common reasons being to predict outcomes, discover patterns, and perform clustering or classification. As the geospatial industry continues to evolve, so will the ways in which geospatial professionals use data to solve problems.
About the Author
John Gartner specializes in data science with 5+ years of experience in the field. His main interests are in big data, computer vision, and geospatial data science. John is also an experienced business consultant and has advised local and state governments, as well as the private sector. John attended the Georgia Institute of Technology and is a continuous learner in computer science and the latest technology trends. Born in Houston, John has a passion for baseball and the Houston Astros which he has been following since his early childhood.