{"id":35738,"date":"2023-08-04T10:50:43","date_gmt":"2023-08-04T09:50:43","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=35738"},"modified":"2023-08-04T10:50:43","modified_gmt":"2023-08-04T09:50:43","slug":"unlocking-the-power-of-satellite-imagery-for-commercial-intelligence","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/unlocking-the-power-of-satellite-imagery-for-commercial-intelligence\/35738\/","title":{"rendered":"Unlocking the power of satellite imagery for commercial intelligence"},"content":{"rendered":"

David Proulx, Chief Product Officer at SkyWatch, discusses how the Earth Observation industry can make use of the power of satellite imagery as a valuable intelligence tool.<\/h2>\n

According to industry analysts, the Earth Observation (EO) industry is expected to grow 100x in the next ten years. It is helpful to consider where all that growth will come from. Will it be driven by the traditional consumers of EO, those GIS (geographic information system) \u2018geeks\u2019 who spend their days doing band-math indices and making ESRI maps?<\/p>\n

Certainly, in part, but a picture (a human-readable one) can be worth way more than a thousand words in the eyes of the proper recipients \u2013 most of whom will come from industries many standard deviations away from the mean of legacy GIS. And this becomes particularly true with the emergence of no-code AI, computer vision tools, and the power of satellite imagery to detect, classify, and count things.<\/p>\n

There is much talk about the transparency afforded by Earth Observation as a driver for change and action<\/a> in areas such as climate change, peace and security, and other critical areas that affect life on Earth.<\/p>\n

How can businesses unlock the power of satellite imagery?<\/h3>\n

With multi-modal, high-resolution sensors revisiting every square inch of the Earth<\/a> multiple times a day, there is nowhere to hide from the power of satellite imagery \u2013 especially when the data collected by those sensors are available at market rates to anybody with a credit card and a web browser.<\/p>\n

We can use hyperspectral imagery (HSI) to keep tabs on those nasty carbon emitters in the oil and gas industry; we can use synthetic aperture radar (SAR) or middle-wave infrared (MWIR) to watch military forces amass on a border under cover of darkness and foliage. That\u2019s powerful. But who wants to pay for it?<\/p>\n

What if we extend the concept of EO-derived transparency to the value chains that support every good and service delivered by the economy’s private sector? What can we see, and who wants to pay for it?<\/p>\n

We call this the \u2018commercial intelligence\u2019 sector, and we\u2019ll illustrate with an \u2018earthy\u2019 example from the prosaic but $13bn agricultural sector.<\/p>\n

Let\u2019s say that we have a feedlot operator in the rural US. Her feedlots span many acres and contain thousands of heads of beef cattle at any given time. What difference does the power of satellite imagery make\u2014and to whom?<\/p>\n

Producers<\/strong><\/p>\n

This is their livelihood: the number of cattle, fence integrity, the comings and goings of suppliers, employees etc., all matter.<\/p>\n

Commodity owners<\/strong><\/p>\n

Our feedlot operator doesn\u2019t necessarily own the livestock they are managing; they are owned by someone else outright or via a derivative investment (typically a considerable agri-business\/food production concern).<\/p>\n

This person wants the contracted producer to know what they are doing.<\/p>\n

Input providers<\/strong><\/p>\n

Silage bunker operators and feed transporters are interested in what\u2019s happening on the feedlot.<\/p>\n

Lenders
\n<\/strong><\/p>\n

Intensive agriculture is a highly leveraged business; a financial institution will have loaned the cash.<\/p>\n

Insurers and reinsurers
\n<\/strong><\/p>\n

Agriculture is an inherently risky venture; weather, supply chain, disease, etc., can all impact yields.<\/p>\n

Somebody \u2013 or, likely, a consortium of somebodies \u2013 is underwriting this risk. How they model that risk, and the premiums paid by our producer to manage that risk, can be derived from point-in-time or time series analysis.<\/p>\n

Public regulators<\/strong><\/p>\n

Agriculture is a highly regulated practice. Regulators are interested in whether:<\/p>\n