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AI & ML for GIS and Mapping

The are two broad categories of machine learning which can apply to GIS application in various ways.

  1. SUPERVISED LEARNING is just fitting data to a function for prediction
  2. UNSUPERVISED LEARNING recognizes what the data is using patterns from unlabelled data.

We apply AI in areas such as classification, prediction, and segmentation for GIS.

● Image Classification (Support Vector Machine)
● Image Segmentation and Clustering with K-means
● Prediction Using Empirical Bayesian Kriging (EBK)

M&A brings together the machine learning, artificial intelligence and deep learning as away of transforming how we understand and interact with our world and everything around us. We are part of the geo-spatial world looking at better management of open geo-spatial data, the are and not limited to:


openEO develops an open API to connect R, Python, JavaScript, and other clients to big Earth observation cloud back-ends in a simple and unified way.

SpatioTemporal Asset Catalog (STAC)

SSTAC specification provides a common language to describe a range of geospatial information, so it can more easily be indexed and discovered. A ‘spatiotemporal asset’ is any file that represents information about the earth captured in a certain space and time. The goal is for all providers of spatiotemporal assets (Imagery, SAR, Point Clouds, Data Cubes, Full Motion Video, etc.) to expose their data as SpatioTemporal Asset Catalogs (STAC), so that new code doesn’t need to be written whenever a new data set or API is released.

Raster Vision

An open source framework for deep learning on satellite and aerial imagery. Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery) can leverage Raster Vision’s built-in support for chip classification, object detection, and semantic segmentation using PyTorch and Tensorflow.