ENVI Analytics Symposium 2016 – Geospatial Signatures to Analytical Insights

HySpeed Computing is pleased to announce our sponsorship of the upcoming ENVI Analytics Symposium taking place in Boulder, CO from August 23-24, 2016.

EAS 2016

Building on the success of last year’s inaugural symposium, the 2016 ENVI Analytics Symposium “continues its exploration of remote sensing and big data analytics around the theme of Geospatial Signatures to Analytical Insights.

“The concept of a spectral signature in remote sensing involves measuring reflectance/emittance characteristics of an object with respect to wavelength. Extending the concept of a spectral signature to a geospatial signature opens the aperture of our imagination to include textural, spatial, contextual, and temporal characteristics that can lead to the discovery of new patterns in data. Extraction of signatures can in turn lead to new analytical insights on changes in the environment which impact decisions from national security to critical infrastructure to urban planning.

“Join your fellow thought leaders and practitioners from industry, academia, government, and non-profit organizations in Boulder for an intensive exploration of the latest advancements of analytics in remote sensing.”

Key topics to be discussed at this year’s event include Global Security and GEOINT, Big Data Analytics, Small Satellites, UAS and Sensors, and Algorithms to Insights, among many others.

There will also be a series of pre- and post-symposium workshops to gain in-depth knowledge on various geospatial analysis techniques and technologies.

For more information: http://harrisgeospatial.com/eas/Home.aspx

It’s shaping up to be a great conference. We look forward to seeing you there.

Constellations, Clouds & the Conundrum of Big Data Processing

HySpeed Computing is proud to be featured in the inaugural issue of UPWARD, the quarterly magazine of the ISS National Lab.


Constellations, Clouds & the Conundrum of Big Data Processing

“For millennia, humans have looked up to the sky to find constellations of stars, wondering what mysteries they hold. Today, we live in a world where constellations of satellites look down on us, hoping to unravel mysteries as well – by capturing highly complex images of Earth.

“In the commercial remote sensing market, the imaging of Earth from space has experienced a technical tsunami, giving rise to a population explosion of smaller but far more capable satellites with new sensing and communication capabilities. In the near future, constellations of nano-, micro-, and other small-sats will swarm low Earth orbit like drones filling the skies on Earth.”

See the full article beginning on page 10…

Conundrum of Big Data Processing

From here to there – and everywhere – with Geospatial Cloud Computing

Reposted from Exelis VIS, Imagery Speaks, June 30, 2015, by James Goodman, CEO HySpeed Computing.

In a previous article we presented an overview of the advantages of cloud computing in remote sensing applications, and described an upcoming prototype web application for processing imagery from the HICO sensor on the International Space Station.

First, as a follow up, we’re excited to announce availability of the HICO Image Processing System – a cloud computing platform for on-demand remote sensing image analysis and data visualization.

HICO IPS - Chesapeake Bay - Chlorophyll

HICO IPS allows users to select specific images and algorithms, dynamically launch analysis routines in the cloud, and then see results displayed directly in an online map interface. System capabilities are demonstrated using imagery collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station, and example algorithms are included for assessing coastal water quality and other nearshore environmental conditions.

This is an application-server, and not just a map-server. Thus, HICO IPS is delivering on-demand image processing of real physical parameters, such as chlorophyll concentration, inherent optical properties, and water depth.

The system was developed using a combination of commercial and open-source software, with core image processing performed using the recently released ENVI Services Engine. No specialized software is required to run HICO IPS. You just need an internet connection and a web browser to run the application (we suggest using Google Chrome).

Beyond HICO, and beyond the coastal ocean, the system can be configured for any number of different remote sensing instruments and applications, thus providing an adaptable cloud computing framework for rapidly implementing new algorithms and applications, as well as making these applications and their output readily available to the global user community.

However, this is but one application. Significantly greater work is needed throughout the remote sensing community to leverage these and other exciting new tools and processing capabilities. To participate in a discussion of how the future of geospatial image processing is evolving, and see a presentation of the HICO IPS, join us at the upcoming ENVI Analytics Symposium in Boulder, CO, August 25-26.

With this broader context in mind, and as a second follow-up, we ask the important question when envisioning this future of how we as an industry, and as a research community, are going to get from here to there?

The currently expanding diversity and volume of remote sensing data presents particular challenges for aggregating data relevant to specific research applications, developing analysis tools that can be extended to a variety of sensors, efficiently implementing data processing across a distributed storage network, and delivering value-added products to a broad range of stakeholders.

Based on lessons learned from developing the HICO IPS, here we identify three important requirements needed to meet these challenges:

  • Data and application interoperability need to continue evolving. This need speaks to the use of broadly accessible data formats, expansion of software binding libraries, and development of cross-platform applications.
  • Improved mechanisms are needed for transforming research achievements into functional software applications. Greater impact can be achieved, larger audiences reached, and application opportunities significantly enhanced, if more investment is made in remote sensing technology transfer.
  • Robust tools are required for decision support and information delivery. This requirement necessitates development of intuitive visualization and user interface tools that will assist users in understanding image analysis output products as well as contribute to more informed decision making.

These developments will not happen overnight, but the pace of the industry indicates that such transformations are already in process and that geospatial image processing will continue to evolve at a rapid rate. We encourage you to participate.

About HySpeed Computing: Our mission is to provide the most effective analysis tools for deriving and delivering information from geospatial imagery. Visit us at hyspeedcomputing.com.

To access the HICO Image Processing System: http://hyspeedgeo.com/HICO/

ENVI Analytics Symposium – Come explore the next generation of geoanalytic solutions

HySpeed Computing is pleased to announce our sponsorship of the upcoming ENVI Analytics Symposium taking place in Boulder, CO from August 25-26, 2015.

ENVI Analytics Symposium

The ENVI Analytics Symposium (EAS) will bring together the leading experts in remote sensing science to discuss technology trends and the next generation of solutions for advanced analytics. These topics are important because they can be applied to a diverse range of needs in environmental and natural resource monitoring, global food production, security, urbanization, and other fields of research.

The need to identify technology trends and advanced analytic solutions is being driven by the staggering growth in high-spatial and spectral resolution earth imagery, radar, LiDAR, and full motion video data. Join your fellow thought leaders and practitioners from industry, academia, government, and non-profit organizations in Boulder, Colorado for an intensive exploration of the latest advancements of analytics in remote sensing.

Core topics to be discussed at this event include Algorithms and Analytics, Applied Research, Geospatial Big Data, and Remote Sensing Phenomenology.

For more information: http://www.exelisvis.com/eas/HOME.aspx

We look forward to seeing you there.

Advantages of Cloud Computing in Remote Sensing Applications

The original version of this post appears in the June 26 edition of Exelis VIS’s Imagery Speaks, by James Goodman, CEO HySpeed Computing

Below we explore the role of cloud computing in geospatial image processing, and the advantages this technology provides to the overall remote sensing toolbox.

The underlying concept of cloud computing is not new; dating back to the advent of the client-server model in mainframe computing, where the utilization of local devices to perform tasks on a server, or set of connected servers, has a long history within the computing industry.

With the rise of the personal computer, and the relative cost efficiency of memory and processing speed for these systems, there ensued a similarly rich history of computing using the local desktop environment.

As a result, in many application domains, including that of remote sensing, a dichotomy developed in the computing industry, with a large portion of the user community reliant on personal computers and mostly the government and big business utilizing large-scale servers.

More recently, however, there has been an industry-wide surge in the prevalence of cloud computing applications within the general user community. Driven in large part by rapidly growing data volumes and the profound increase and diversity of mobile computing devices, as well as a desire for access to centralized analytics, cloud computing is now a common component in our everyday experience.

Where does cloud computing fit within remote sensing? Given the online availability of weather maps and high-resolution satellite base maps, it can be argued that cloud computing is already regularly used in remote sensing. However, there are an innumerable number of other remote sensing applications, with societal and economic benefits, that are not currently available in the cloud.

Since most of these applications are not directed at the consumer market, but instead relevant predominantly to business, government, education and scientific concerns, what then are the advantages of cloud computing in remote sensing?

  • Provides online, on-demand, scalable image processing capabilities.
  • Delivers image-derived products and visualization tools to a global user community.
  • Allows processing tools to be efficiently co-located with large image databases.
  • Removes software barriers and hardware requirements from non-specialists.
  • Facilitates rapid integration and deployment of new algorithms and processing tools.
  • Accelerates technology transfer in remote sensing through improved application sharing.
  • Connects remote sensing scientists more directly with the intended end-users.

At HySpeed Computing we are partnering with Exelis Visual Information Solutions to develop a cloud computing platform for processing data from the Hyperspectral Imager for the Coastal Ocean (HICO) – a uniquely capable sensor located on the International Space Station (ISS). The backbone of the computing framework is based on the ENVI Services Engine, with a user interface built using open-source software tools such as GeoServer and Leaflet.

A prototype version of the web-enabled HICO processing system will soon be publically available for testing and evaluation by the community. Links to access the system will be provided on our website once it is released.

We envision a remote sensing future where the line between local and cloud computing becomes obscured, where applications can be interchangeably run in any computing environment, where developers can utilize their programming language of choice, where scientific achievements and innovations are readily shared through a distributed processing network, and where image-derived information is rapidly distributed to the global user community.

And what’s most significant about this vision is that the future is closer than you imagine.

About HySpeed Computing: Our mission is to provide the most effective analysis tools for deriving and delivering information from geospatial imagery. Visit us at hyspeedcomputing.com.


Big Data and Remote Sensing – It’s all about information and applications

From the launch of the first Earth observing satellite, to today’s growing space industry, the volume of remote sensing data continues to grow at a remarkable rate. Furthermore, with the emerging utilization of drones, aka unmanned aerial vehicles, and deployment of low-cost satellite constellations, we are on the cusp of a momentous leap forward in data accessibility.

For example, consider the growth of private-sector drones, i.e., those used for scientific research, civil applications and business development. According to a March 2013 report from the Association for Unmanned Vehicle Systems International (AUVSI), assuming the FAA determines how drones fit within commercial airspace by 2015, it is expected that in just ten years the drone industry in the U.S. will generate more than 100,000 jobs and $80 billion of revenue. This includes an immense number of individual drones, on the order of hundreds of thousands, each generating their own streams of remote sensing data.

As another example, consider the pending growth of new low-cost commercial satellite constellations, such as those planned by Skybox Imaging and Planet Labs. Current plans include 28 satellites to be launched by Planet Labs and 24+ satellites to be launched by Skybox Imaging, where each constellation has the objective of achieving cost-effective, near real-time, high-resolution imaging of our planet’s surface. Planet Labs plans to launch its constellation in early 2014, and Skybox Imaging plans to begin launching later in 2013, so data from both companies will soon be available.

There are many questions associated with all of this growth: Where will all this data be stored? How will data be efficiently discovered, accessed and visualized? What types of processing and data management tools will be needed? How will this data be used? What new types of applications will be devised to leverage the information derived from this data?

Amongst these questions, we focus our discussion here on the applications. However, note that the challenges associated with data storage, discovery and dissemination are not trivial, and are equally critical to the success of this industry. But for now let’s consider some of applications that utilize information derived from this imagery.

The AUVSI report indicates a number of areas where drones are already being utilized, including: wildfire mapping, agricultural monitoring, disaster management, power line surveys, law enforcement, telecommunication, weather monitoring, aerial imaging/mapping, television and movies production, oil and gas exploration, freight transport, and environmental mapping. Similar application areas are also highlighted in informational material from Skybox Imaging and Planet Labs, as well as in discussions throughout the remote sensing industry.

To provide more specific examples, the following hypothetical applications were recently reported in an article on Skybox Imaging in Wired (06.18.13): “the number of cars in the parking lot of every Walmart in America; the number of fuel tankers on the roads of the three fastest-growing economic zones in China; the size of the slag heaps outside the largest gold mines in southern Africa; the rate at which the wattage along key stretches of the Ganges River is growing brighter.”

Other example applications include: lawn and vegetation greenness indices for marketing landscape maintenance; water surface conditions for commercial and recreational fishing; flooding and damage assessments for insurance claims; number of beach visitors for targeted advertising; crop health for precision agriculture and investment futures; and many more.

Even with these few examples we see a glimpse of the enormous economic potential for the growing remote sensing industry. A common theme throughout is the need to accurately and efficiently deliver information in a timely manner. To do so still requires the development and implementation of many new hardware and software solutions; however, in that regard there are also many opportunities. This is a significant time for remote sensing, and it will be exciting to see how the industry develops in the near future.

This is part 2 of a series on big data and remote sensing… visit part 1 here.

Big Data and Remote Sensing – Where does all this imagery fit into the picture?

There has been a lot of talk lately about “big data” and how the future of innovation and business success will be dominated by those best able to harness the information embedded in big data. So how does remote sensing play a role in this discussion?

We know remote sensing data is big. For example, the NASA Earth Observing System Data and Information System (EOSDIS), which includes multiple data centers distributed around the U.S., currently has more than 7.5 petabytes of archived imagery. Within the EROS data center alone there are over 3.5 million individual Landsat scenes totaling around 1 petabyte of data. And this is but a subset of all the past and currently operating remote sensing instruments. There are many more, particularly when considering the various international and commercial satellites, not to mention the array of classified military satellites and the many instruments yet to be launched. Remote sensing imagery therefore certainly satisfies the big data definition of size.

But what about information content? A significant aspect of the big data discussion is geared towards developing large-scale analytics to extract information and applying those results towards answering science questions, addressing societal needs, spurring further innovation, and enhancing business development. This is one of the key aspects – and challenges – of big data, i.e., not just improving the capacity to collect data but also developing the software, hardware and algorithms needed to store, analyze and interpret this data.

Remote sensing researchers have long been using remote sensing data to address localized science questions, such as assessing the amount of developed versus undeveloped land in a particular metropolitan area, or quantifying timber resources in a given forested area. Subsequently, as software and hardware capabilities for processing large volumes of imagery became more accessible, and image availability also increased, remote sensing correspondingly expanded to encompass regional and global scales, such as estimating vegetation biomass covering the Earth’s land surfaces, or measuring the sea surface temperatures of our oceans. With today’s processing capacity, this has been extended yet further to include investigations of large-scale dynamic processes, such as assessing global ecosystem shifts resulting from climate change, or improving the modeling of weather patterns and storm events around the world.

Additionally, consider the contribution remote sensing makes to the planning and development of transportation infrastructure in the northern hemisphere, where the opening of new trans-arctic shipping routes and changes to other existing high-latitude shipping routes are being predicted using models that depend on remote sensing data for input and/or validation. And also consider agricultural crop forecasting, which relies heavily on information and observations derived from remote sensing data, and can not only have economic impacts but also be used to indicate potential regions of economic and political instability resulting from insufficient food supplies.

Such examples, and others like them, represent a logical progression as research and applications keep pace with greater data availability and ongoing improvements in processing tools. But the field of remote sensing, and its associated data, is continuing to grow. What else can remote sensing tell us and how else can this immense volume of data be used? Are there relationships yet to be exploited that can be used to indicate consumer behavior and habits in certain markets? Are there geospatial patterns in population expansion that can be used to better predict future development and resource utilization?

There’s a world of imagery out there. What are your ideas on how to use it?