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While many consider the technology to still be in its infancy, geospatial AI solutions have already demonstrated the ability to automate many manual processes and provide analyses far more quickly and more accurately than humans can, while using fewer resources – given the right conditions. In this article, we will discuss geospatial AI use cases […]

The basis of all machine learning projects is data collection and acquisition. But that is also the first stumbling block where many ML projects fail. In this article, we will discuss the challenges of text, audio, photo, and video data collection for ML training models so that you can predict and avoid the many pitfalls. […]

Spatial data is so ever-present that we tend to forget about its significance – from the simplest of maps to weather forecasts to GPS – all of this is based on spatial data. Spatial data science simply takes this to the next level – utilizing data science techniques and methods to use spatial data in […]

Named entity recognition (NER) is vital to natural language processing (NLP)

Despite fears of AI taking over the world, at its core, AI is still reactionary and doesn’t truly learn on its own. It can only make predictions based on the data it’s given. That is why data quality for machine learning and AI should be at the core of every project. Without high-quality training data, […]

You will hear many data scientists use the phrase ‘rubbish in, rubbish out’. In the world of machine learning, it means that an algorithm will not serve its intended purpose if the training data is no good. And professional data acquisition for machine learning is the first step to having good training data. That is […]