As the applications of ML for GIS grow in complexity, generating high-quality ground truth or training data for these novel applications can become difficult. In a special guest feature with Geospatial World, iMerit’s Director of Sales – GIS & Mapping, Kyle Miller discussed geospatial applications for the private and public sector and the future of data solutions for geospatial intelligence.
Here are 5 key takeaways from the article:
- The use cases of geospatial applications run a broad gamut of public and private sector activities, including land use planning, commercial and residential insurance, agriculture, national security, oil and gas exploration, and retail.
- For geospatial intelligence, data is gathered through various aerial sources that capture everything within a specific geographic area, and the data annotation required varies depending on the final use case. For example, in the case of insurance companies, iMerit uses image classification and 2D polygon annotation to capture features of a building to assess the insurance premium rate.
- The amount of data available and being collected has increased drastically. So has the demand for higher resolution and better-quality data. There are different types of data that are emerging, such as synthetic aperture data (SAR), which is relatively new.
- We are starting to see different ways of collecting data. The one that is really transformative is LIDAR, which is helping to create highly accurate 3D representations or models of different aspects of the world.
- GIS and machine learning will continue to evolve, bringing unique use cases and subsequent complexities in data usage to the forefront. This dynamism requires training and developing skills of data annotators to seamlessly provide high-quality data to feed artificial intelligence and machine learning-led systems.
Read the complete article here: Training Data for GIS Applications of Machine Learning
