The Evolution of Remote Sensing: Delivering on the Promise of IoT
Remote sensing technology has come a long way since Gaspard Felix Tournachon’s pioneering aerial photographs in 1858. While the technology has advanced, there is one aspect of remote sensing that has remained relatively unchanged: the frequency of data acquisition.
Now, with the advent of new acquisition platforms, smaller and more efficient sensors, as well as forces like cloud computing, remote sensing is again on the precipice of significant innovation. We are moving beyond the traditional interpretation of remote sensing as an aerial mapping, GIS and earth observation discipline to something that resembles another recent development, the industrial Internet of Things (IoT). Interconnected devices on converged platforms streaming data on a continuous basis will paint a new vision of the world in which we live.
Since those early days, more than 160 years ago, when Tournachon leaned over the side of a hot air balloon to photograph the landscape, the practice of remote sensing for mapping purposes has evolved tremendously – from human interpretation of aerial images to measurements using digital photogrammetry; from the evolution of visible imagery to spectral imagery; and from photogrammetry to lidar (light detection and ranging), which relies on pulsed laser light to make 3-D digital representations of a target area.
Different Use Cases, Similar Goals
While remote sensing for geospatial applications has enabled us to see and understand more about our surroundings, it is usually not been thought of as a means for continuous monitoring. A big part of the reason is that remote sensing platforms are still typically attached to aircraft – an artifact of the history of remote sensing and the use cases that have motivated development in this area.
Those use cases – mostly centered around mapping and earth observations – required platforms like satellites and aircraft. The form factor of the sensors followed these use cases. As a result, remote sensing is associated with collection frequencies that are often measured in months. Even satellites – which offer the highest frequency – are limited in the type of sensors and the accuracy of the data.
Fueled by the Internet, broadband networks, and advances in Micro-Electro-Mechanical Systems (MEMS), IoT has been motivated by a different set of use cases. Born from the notion of “pervasive” or ubiquitous computing, the phrase IoT was coined in the context of supply chain management, specifically around the idea of coupling RFID data with the internet.
Since then, developments in IoT have stayed close to the original concepts of intrinsic or embedded sensors transmitting data via the internet. Depending on the application and the type of device, IoT sensors can relay information about water speed, water or air pressure, temperature, wind speed, heart rate, room temperatures and a host of other data points. Unlike remote sensing, for which sensors use characteristics of the electromagnetic (EM) reflection from a surface, IoT sensors are usually electro mechanical sensors that record physical measurements. Depending on the findings, the system then can return commands to start or stop certain actions, or create alerts about potential problems to ensure proactive maintenance.
Blurring the Lines Between Remote Sensing and IoT
The advances in the disciplines of remote sensing and IoT are blurring the distance between the two previously distinct worlds. Both movements are motivated by similar needs, but have evolved based on the circumstances under which each was conceived. Indeed, remote sensing and IoT are akin to the same flower or fruit developing on neighboring islands with different characteristics. For example, both came about from the the need to collect data on a large scale efficiently without requiring humans to create the data. And, both have evolved to create data and analytics that reduce vast amounts of data into digestible insights.
There are several monitoring and inspection applications where the situation requires frequent observation due to the possibility of changes within small time increments that could be costly if not detected in time.
For these applications, remote sensing and IoT are essentially complementary methods that contribute different strengths to solving a problem. In fact, under these circumstances, remote sensing methods can essentially be considered “extrinsic IoT” to contrast it with the more traditional definition of IoT, which uses embedded or intrinsic sensors. They bring together external observations possible only from extrinsic sensors and the data stream delivered by embedded IoT sensors.
For example, sensors in a pipe may detect a drop in pressure across a section, but external hyperspectral sensors can correlate this pressure drop to the presence of oil or water in the ground. Analyzed together, the data produced by these technologies have the potential to offer a wide range of new insights that can reduce costs, improve system performance and proactive warn of impending problems.
The advances driving this convergence include the emergence of platforms, like robots, drones and spectrographic sensors, which can be mounted on these smaller platforms. The mobility and costs of these new platforms make them ideal for constant deployment rather than the episodic deployment of the more traditional airborne platforms. The lighter, less expensive sensors complete the picture.
Other catalysts include the maturity of cloud computing for big data – remote sensing data is nothing if not big – and the availability of open software platforms for autonomously operating robots and obstacle detection using lidar. All of these developments together create a favorable environment to develop and deploy high ROI extrinsic IoT solutions.
As these trends converge – all leveraging the Internet, cloud, and analytics to deliver greater insights – there is an opportunity for a whole host of new use cases for protecting critical infrastructure, preserving the environment and improving public safety.
Real World Applications
Consider the electrical substation. Constant current and voltage measurements are necessary and vital. Today most digital substations have installed sensors, such as Fiber-Optic Current Sensors (FOCS), that are an improvement over more traditional instrument transformer technologies. The measurements collected by these sensors are incorporated into dashboards at electric compan control rooms.
At the same time, forward-looking utilities are beginning to install autonomous robots equipped with lidar, and thermal and multispectral sensors, for monitoring electrical substations. These substations are typically remote, which limits how often they can be visited by utility employees. By employing a robot to continually monitor substation conditions, utilities can remotely track physical and spectral changes, detect and identify stray objects, monitor for corrosion or leaks, and detect temperature changes that could portend the potential for equipment failure.
When notified of certain parameters that exceed accepted thresholds, the utility could send workers out to the substation to complete repairs or replace components before they caused a catastrophic outage. The combined data not only offers a richer stream of information and provides near real-time awareness, it also creates a multidimensional data history that can be mined for trends.
Similarly, pipeline operators typically use pressure, temperature and vibration sensors along the pipeline to continuously record measurements. Now several large commercial drones have the capability to stay aloft for long periods of time and integrate a payload of multiple sensors. As Beyond Visual Line of Sight (BVLOS) restrictions on drone use ease up, drones equipped with sensors will become even more popular as a means of continuously monitoring data and visualizing potential problems, supplementing the sensor data system operators currently receive.
These examples are only the start of how converged remote sensing and IoT, combined with advanced analytics, have the power to transform the way we work, live and play.
The premise of industrial IoT has been that a connected world of sensory data creates opportunities for analytics and insights that will dramatically increase efficiency, and make organizations smarter and more agile. Remote sensing data from new platforms add new capabilities and give organizations an additional complement of tools to help realize the full promise of IoT.
About the Author: Anand Iyer is senior vice president of products and marketing at Quantum Spatial. Anand has more than 25 years of experience in applying data science, machine learning and analytics to a diverse group of industries and organizations ranging from startups to Fortune 100 companies. Over the course of his career, Anand has been a general manager, entrepreneur, technology innovator, management consultant, product manager and data scientist – on occasion, all at the same time. He earned a Ph.D. in Systems and Industrial Engineering from the University of Arizona.