If any of these 10 indicators ring true for your AEC firm, it could be time to modernize

By Lucas Hayden

Legacy technology reminds me a lot of the aging but still reliable car that has faithfully carried me and my family around for the better part of two decades. Much like legacy software, that car reliably gets us from Point A to Point B — usually, at least — it has features that were ahead of their time when they were first released, many of which still work, and although it’s way out of warranty and has its obvious limitations, it does the things it was built to do. Nothing more, nothing less.

Because it still runs well enough, it’s familiar, has that vintage feel, and carries some sentimental value, an item like this, whether it’s a car or a software product, can be difficult to say goodbye to. Yet in the case of my trusty old car, I can’t help but wonder what I’ve been missing and what I stand to gain by replacing it with something more modern — the elevated features, capabilities and amenities others are enjoying while I loyally stick with the status quo.

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Four Powerful ML Apps

Thanks to constellations of imaging satellites, advanced aerial cameras and scanners and various other collection devices, the volume of available geospatial data has grown beyond the capability of humans to manually manage and analyze the datasets. To leverage this abundance of information, machine learning is the new programming paradigm that effectively extracts the wealth of knowledge contained in millions of petabytes of archived and frequently refreshed data. Using machine learning, large datasets are reviewed and analyzed in a fraction of the time compared to previous methods.

Geospatial Platform in the Cloud
To expedite searching for data in areas of interest around the world and analyzing large datasets with machine learning, a company based in Berlin named UP42 has developed a geospatial platform in the cloud. UP42 fulfills the need for data and processing algorithms as well as provides the infrastructure for high-powered computing.

For customers interested in developing machine learning algorithms that solve problems or answer a specific question, UP42 provides the building blocks for powerful geospatial products. A block essentially is a ready-to-use unit of data or processing algorithm that customers string together to form workflows. The basic data handling algorithms give developers a head start with processing blocks such as “Pan-sharpening SPOT/Pléiades images” and “K-Means Clustering for unsupervised classification.” UP42 also provides access to data blocks from multiple sources ranging from 0.5m Pléiades images to Landsat-8 and NEXTMAP digital surface models and digital terrain models.

The developer platform created by UP42 is made accessible to its customers through APIs. The platform enables browsing the datasets and selecting data blocks that meet the customer’s criteria, before applying custom or off-the-shelf algorithms. Developers can choose either to put their custom algorithms onto the platform in a private block or publish a processing block that is accessible to the whole UP42 community for a fee. In addition to facilitating analysis of geospatial data with machine learning, service offerings in the cloud are scalable to meet the need for any level of computing power.
“It is an exciting time for machine learning as a huge amount of resources is being put into the development of algorithms for many industries,” says Rodrigo Almeida, UP42 data scientist. “Many are available in open source code which really encourages more creativity and development of new geospatial applications.”
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