Recent years have been a breakthrough for UAVs. But how did it happen that drones gained such a popularity? Why this technology is so ubiquitous and where that leaves us? In order to answer these questions, we should look a bit more closely at UAVs history and how it affected our world. Fortunately, I found an interesting infographic by PureFunds that brings us closer to answering these questions.
The infographic chronologically shows the most important projects and historical events that have contributed to the spread of UAVs in the past. We will find out about origins of the word “drone”, information about why this technology was needed in the past and why it’s needed today. Interestingly, authors try to foresee how drones will affect the economy and what will be their role in the forthcoming years. It’s definitely worth to look at it.
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It’s no secret organizations today have access to more data than ever before, and it only continues to grow at an astounding rate. Now the problem is not gaining access to the data, it’s the ability to scale and process such large amounts to help solve business problems. After all, the data is meaningless if we cannot make sense of it. Location-based data is no exception, of course.
There are many breakthroughs and success stories in the geospatial industry right now that are propelling the science forward more rapidly than ever before. Integrating IoT data, mapping drone information, analyzing imagery from small sats – GIS is at the root of these technology trends, and we are only beginning to really understand how these new sources of information can help us make better decisions.
Open source technologies, which continues to proliferate in modern IT enterprises, have become an essential component for gathering, organizing, and connecting the dots between vast amounts of the spatial data at our fingertips. Open source enables organizations to harness limitless scalability to understand and solve emerging business challenges.
With the emergence of machine learning concepts, geoprocessing techniques can be applied to make sense of data streams and “big data” architectures. Organizations are not only understanding the “what” behind location content, but now also understand the “why.” The ability to gain deeper insights into change detection, trend analysis, and predictive modeling will take hold in these open, elastic infrastructures.
This is a great time to see the next generation GIS taking shape and what it will become in the years ahead.
