SOME PRICKLY POINTS WITH EARTH OBSERVATION ANALYSIS TOOLS
If we look back, we see that the journey for the pioneers in earth observation was not always easy. Billions of earth observation objects were left in the archive with little to no exploration due to the amount of time, and expertise that was required to sift through them.
Even with improved technology, many EO producers find it difficult to aggregate data from different sources for studies. Like, how do you know that you have chosen the right image to analyse? This alone, can be a tedious exercise that takes a lot of man-hours, sometimes, looking at millions of images with no certainty in results.
While some tools can now visualise the objects, often there isn’t an option to pre-visualise, allowing one to check if the image meets a criteria for the query like; cloud coverage or mission type.
It is no wonder that most of these tasks are mostly relegated to data scientists and IT professionals within teams as they often require some knowledge in coding.
ARLAS® FRAMEWORK FOR EARTH OBSERVATION
As ARLAS® framework was initially conceived for earth observation, its development has evolved to absorb some prickly points in geospatial big data technologies’ usage.
The main principle behind ARLAS is to make it accessible. One does not have to be a coder nor a data scientist to use it. This then cuts the amount of time spent on the technical details – setting up and filtering – to be used for instantaneous query of the data.
One of the many resourceful features of ARLAS when conducting a search is, getting results to your query plus, suggestions close to your query. This gives you the option to quickly pick the right image.
There are several built-in time-saving configurations like the ability to save your filters if you use them often. Advance filters by detail like excluding images with cloud coverage or even sharing your URL with teammates to quickly view before making decisions.