Geospatial IoT Insights – November 10

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Shivon Zilis and James Cham from Bloomberg Beta have published an updated summary of the competitive landscape for Machine Intelligence. Planet OS is represented as one of the Enterprise Intelligence solutions for sensor data, along with GE Predix, Maana, Alluvium, and others. Over the past year, machine intelligence has exploded, with $5 billion in venture investment. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses. These tools deliver productivity gains and means to outrun and outlast the competitors. [Harvard Business Review]


Mitigating the weather’s impact on trucking. The estimated cost of weather-related delay to the U.S. trucking companies ranges from $2.2 billion to $3.5 billion dollars annually. There are real-world weather information services that will help protect drivers and cargo better on the road going forward. AccuWeather is one service provider putting together just such a package through its AccuWeather Enterprise Solutions forecasting system. Their system offers an average of 24.6 minutes of lead time with a “false alarm” rate of only 11% to warn truck drivers about sudden weather incidents such as roads closed due to flooding, snowstorms, and the like so they can stop or re-route – keeping them safe and their freight moving on time. [FleetOwner]

Ocean CO2 Data Since 1970

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Oceans absorb approximately half of man-made CO2 emissions. The warming of the ocean surface and changes in wind patterns can limit the transfer of CO2 to deeper levels and thus limit oceans ability to absorb greenhouse gases. Shifts in the biological pump and ecosystem functioning are also likely to appear.

This week we’ve added Surface Ocean CO2 Variability and Vulnerability (Socat V4) dataset to Planet OS Datahub.

With historical data since 1970, the Socat V4 dataset is the common format of all publicly available fugacity of CO2 data for the oceans surface. It has become the foundation for the marine carbon community upon which they build the future.

Curious to find out more? Explore all datasets on Planet OS Datahub.

Geospatial IoT Insights – November 3

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Big data is about to transform renewable energy. Developments in the US and China are eroding Europe’s lead in renewable energy. But if the EU could harness the power of the big data, it could increase outputs from wind energy by 20 percent – without much building. Big data enables new methods of processing vast existing troves of digital information in a way that maximizes efficiency, and this article shows how different wind energy companies are solving it. [Deutsche Welle]

Climate change is causing trouble to weather forecast industry, as using the past to inform the future becomes more challenging. For example when analyzing weather in the far north, meteorologists are relying on data from recent years because climate change is a more recent phenomenon that’s made older data, collected before the Arctic began to warm so quickly, less relevant. [KTOO]

Giant fall in generating costs from offshore wind. Competitive bidding for projects has driven global costs of electricity from offshore wind down 22% to a benchmark estimate of $126 per megawatt-hour in the second half of 2016. As the latest landmark, in September, two offshore wind projects in Danish waters totaling 350 megawatts were awarded to Vattenfall with a record-breaking bid of just 60 euros ($67.33) per MWh. [Bloomberg]

35 Years of Sea Surface Temperature

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This week we’ve added several decades of historical NOAA Daily OISST data to our Datahub. The dataset is an analysis constructed by combining observations from different platforms (satellites, ships, buoys) on a regular global grid, with values from 1981 until now.

In addition to standard sea surface temperature, the Daily Optimum Interpolation Sea Surface Temperature dataset also includes SST anomalies, which means the daily OISST minus a 30-year climatological mean.

The Daily OISST data are great for investigating upwellings and downwellings, and have been used to track signs of El Niño and La Niña. In fact, forecasters now think there’s a 70% chance that La Niña conditions will develop this fall.

Looking for Earth data? Explore the Planet OS Datahub.

Baltic Sea Real Time Observations Now Available

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This week we’ve added Baltic Sea Real Time Observations to Planet OS Datahub.

The Baltic Sea recently experienced a significant sea level drop which can be easily viewed with this timeseries dataset. Due to three weeks long eastern winds and high air pressure, over 200 km3 of water was blown from the Baltic Sea into the ocean through the Scandinavian straits.

Baltic Sea observations data are collected from 150 stations and includes sea surface temperature, sea surface height and wave properties. The dataset provides observations within 24 to 48 hours of acquisition, with quality controlled according to standardized procedures that have been defined in collaboration with BOOS (Baltic Operational Oceanographic System).

Are you working with Earth science data? Planet OS Datahub streamlines the data integration process and reduces operational overhead. Stop wrestling with undocumented interfaces, outdated formats, and broken integration scripts. With Datahub you can focus on what truly matters – delivering valuable data-driven insights and applications. See what other high-quality data we have made available.

Planet OS Data Challenge at ExpeditionHack NYC

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We’re thrilled to be part of the Expedition Hackathon NYC happening on November 12-13! This is your chance to map the future of sustainability with NGA, Mapbox, IBM Bluemix, Planet OS and others. The hackathon’s focus areas are Oceans, Forests, Conservation and Indigenous People.

To add some motivation to the hours of intense coding and hustling, we decided to put out tons of high-quality environmental data, data integration and computational infrastructure, and reward the best teams with some cool prizes.

All hackathon participants will get free, unlimited access to:

The prizes:

  • All teams that use our data tools will secure an unlimited free access to Planet OS data tools
  • The team with the best solution will get special swag and surprises from Planet OS
  • The general Grand Prize of the hackathon is $3000 and a round trip to DC from NGA to meet NGA Executives.

We have already validated a few business ideas that the teams could work on. Stay tuned for updates! All the updates will be shared on this page so it would be wise to bookmark it. Contact us at for further questions.

#PlanetOS  #DataChallenge

Geospatial IoT Insights – October 27

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The latest edition of the International Energy Agency’s Medium-Term Renewable Market Report now sees renewables growing 13% more between 2015 and 2021 than it did in last year’s forecast, due to strong policy support in key countries and sharp cost reductions. Based on the updated growth forecast, 2.5 wind turbines and 30,000 solar panels will be installed every hour around the world over the next five years. We are expecting this trend to accelerate also the renewable energy data market that is being targeted by Planet OS products. [International Energy Agency]

Energy firms face data challenges in IoT pursuits. An inability to standardize data across systems slows many companies’ progress, says David Mount, a partner with venture capital firm Kleiner Perkins Caufield & Byers. Data aggregation and standardization is where people are getting stuck right now. Organizing data is a time-consuming but necessary step that companies need to perfect before they can take on more advanced IoT projects. [The Wall Street Journal]

As they struggle to keep pace with more frequent outbreaks of severe weather, U.S. forecasters are looking for better ways of incorporating more data into storm and flood predictions. U.S. storm forecasts have been criticized for relatively low resolution compared to European models that are leveraging exascale computing, analytics and storage. In response, NOAA announced $6 million in research grants this week designed to help to field new improved forecasts models, new weather sensors and better methods of assimilating data into weather forecast models. [Datanami]

How Big Data Makes Renewable Energy More Competitive

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Two weeks ago we attended the Renewables UK Marketplace and I presented the Planet OS story by participating in a session about big data. I was joined by several other leaders from the renewable energy big data market. Paul Usher started off the session and made it clear that renewable energy companies could gain significant competitive advantage through advanced analytics and big data.

However, this business advantage doesn’t come easy because big data analytics are hard to implement, and the most time consuming part is simply preparing the data for analysis. Most big data teams spend 40-60% of their time preparing data or ‘data wrangling’ as it’s called by many. The problem is even worse with geospatial data which has many diverse formats including time series, raster, and vector data. Luckily, there are new emerging technologies like Kinetica and Datahub that help companies reduce the time they spend wrangling geospatial data. The time saved can be spent instead on developing advanced analytical models that deliver competitive advantage to the business.

I observed many parallel themes between this event and the Wind Europe Summit. The wind energy industry is clearly maturing, although in different ways between onshore and offshore. Innovation is a major goal across the entire supply chain as companies rush to increase revenues and drive costs down. New technologies like floating turbines and energy storage could have an enormous impact on the future of renewable energy. Meanwhile the industry is tirelessly optimizing the performance of both operations and assets to reduce costs and be more competitive with traditional energy sources like coal and gas. Advanced analytics is a critical tool for renewable energy companies to achieve this competitive advantage.

Geospatial IoT Insights – October 20

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Historical records may underestimate the rising sea level. A new study using NASA satellite data finds that tide gauges — the longest and highest-quality records of historical ocean water levels — may have underestimated the amount of global average sea level rise that occurred during the 20th century. [NASA]

The here and now of big geospatial data. Another brain-stimulating overview of recent real-world examples of using geospatial data. Logistics, fraud data, retail, finance, shipping, advertising – and the list continues. The article also lists specialized geospatial databases which is something one needs in order to manage and access geospatial big data. [Datanami]

McKinsey has released an extensive report on the monetization of car data. Abundance of sensors and continuous connectivity is almost a standard for new cars today, and this will create new market opportunities. McKinsey claims that the global revenue from car data monetization could reach $750 billion by 2030. The 61-page report sheds light on how industry players can quickly build and test car data-driven products and services and develop new business models around them. It also identifies and assesses the value and requirements of potential car data-enabled use cases.  [McKinsey]

Join Planet OS Data Challenge At Garage48


We’re thrilled to be part of the Garage48 Open & Big Data Hackathon happening this weekend in Tartu, Estonia! To add some motivation to the 48h of intense coding and hustling, we decided to put out tons of high-quality environmental data, data integration and computational infrastructure, and reward the best teams with some cool prizes.

All hackathon participants will get free, unlimited access to:

The prizes:
- All teams that use our data tools will secure an unlimited free access to Planet OS data tools
– Three teams with the best solutions will get special swag and surprises from Planet OS
– The team that comes out with the best prototype that runs on Planet OS Datahub, will be rewarded with €1,000!

We have already validated a few business ideas that the teams could work on. Stay tuned for updates! All the updates will be shared on this page so it would be wise to bookmark it. Note that you will definitely stand out if your solution uses some elements of Machine Learning.

#PlanetOS  #DataChallenge