“The report is about 30 pages long and does highlight the range of maturity that we see across water utilities,” said Herrin. “many are a lot farther along in their journey of going digital. We see a lot that emphasizes the different silos that exist within those utilities as well. Whether those are departmental silos of different parts of the organization, not interacting or not getting as much value as they could from each other, or data silos where the systems that are being used aren’t as connected as they could be, to provide the types of insight that could be shared.”
In the report, Herrin said throughout the report there is an emphasis on the fact that there are technical challenges, but then there also are people challenges. The challenges are primarily about getting departments to work together more closely. There are challenges even around, people aging out of their company. Aging infrastructure gets worse over time. Aging staff provide a whole different set of challenges, but also some opportunities. There is a need for utilities to be able to bring their technologies together in a way that’s more open and more flexible so that they can get the things that are more insightful across those different departmental silos.
New OGC Working Group aims to remove a major bottleneck to advanced Earth Observation science applications by defining a standard to document, store, and share Machine Learning sample data
The Open Geospatial Consortium (OGC) seeks public comment on the formation of the new Sample Markup Language for Artificial Intelligence Machine Learning (SampleML-AI/ML) Standards Working Group (SWG), which will focus on developing the Sample Markup Language for AI/ML candidate standard for consideration by OGC membership for approval as an OGC Standard. Comments are due by May 5, 2021.
Artificial Intelligence (AI) is expected to play a crucial role in many domains and will revolutionize existing technologies. During the last decade, Machine Learning (ML) techniques, especially Deep Learning, have improved significantly due to an abundance of data and advancements in high-performance computing. ML reorients and transforms geographic information systems (GIS) and Remote Sensing (RS). ML-based applications are now being deployed across diverse markets to provide new solutions and increase human efficiency. Increasingly, the science community is also using these techniques to better harness the ever-increasing volume of Earth Observation (EO) data for geospatial analysis in various domains – such as smart cities, environmental management, and disaster management.
