PhotonProphet is an innovative solution designed and developed by leveraging AI and Earth Observation (EO) data to develop upscaled solar Remote Monitoring Systems (RMSs).
PhotonProphet is aimed at providing predictive solutions for solar energy production output, downtime reduction, carbon credit potential, battery management, system performance optimization, potential grid instability caused by solar system integration fluctuations, maintenance schedules, and potential damage to solar installations due to forecasted natural disasters or extreme weather events. As such, PhotonProphet ensures users have insights not only into their current energy production but also forecasts for the future.
The emphasis on EO-aided AI-powered systems sets the technology apart by enhancing the reliability and efficiency of energy production.
By providing reliable geological survey, mapping analysis, and interpretation, SpectraMiner emerges as a valuable tool in mineral exploration endeavours.
SpectraMiner offers a groundbreaking solution by harnessing a combination of multispectral and hyperspectral remote sensing data, along with advanced data analytics techniques such as machine learning, for extensive image processing. This innovative approach empowers geologists to address common challenges encountered during geological field mapping, including subjective judgment in map creation and the avoidance of unnecessary expenses on prospecting in barren regions by facilitating access to remote areas with challenging terrain features.
In mineral exploration, a crucial initial step involves identifying geological features associated with target mineralization through the provision and examination of geological maps.
Effective forest fire preparedness strategies necessitate assessing both the spatial and temporal variability of fire danger.
In the event of a wildfire ignition, BlazeWise Sentinel utilizes AI and Earth Observation (EO) data to monitor and mitigate the fire's scale, further enhancing fire management efforts.
BlazeWise Sentinel offers an innovative solution by leveraging pre-fire observations of land surface temperature (LST) anomaly, the Perpendicular Moisture Index (PMI), and soil moisture forecasts to predict wildfire occurrences up to a month in advance. This advanced prediction capability enables proactive intervention to mitigate potential resource damage.