Remote Sensing

Platforms (Helicopter / Fixed Wing / UAS / Mobile)

Sensors (LiDAR / Visual Imagery / Spectral Imagery)

Analytics (Machine Learning, Artificial Intelligence)

Turnkey Program Management

Significant developments in multi-sensor data fusion combined with Artificial Intelligence (AI), including machine learning and pattern recognition for feature extraction and advanced analytics have the potential to provide massive reductions in the cost of data processing while improving the accuracy and automation of anomaly detection and analysis.

Furthermore, massive investments in consumer 3-D gaming technology is morphing into opportunities to leverage this capability along with high definition data to create a whole new class of Virtual Reality (VR) work products, potentially improving safety and productivity while reducing operating costs.

Finally, utilities have a unique need for data to be formatted and delivered in such a way that it can be integrated with back-office systems including geographic information systems (GIS), payment systems, work management (WMS) and outage management systems (OMS) as well as the need for the data to be spatially accurate and mobile, working in both connected and disconnected states. Combine these capabilities with data hosting, storage and delivery concerns (e.g. cloud vs on premise) and we have and entirely new set of challenges that must be included within the decision matrix.

Paradoxically, this complexity and multitude of technologies, platforms and approaches also present tremendous risk of sub-optimal or even wasteful spending due to lack of full understanding of the “art-of-the-possible” in leveraging the synergistic effects of a well architected cross-functional remote sensing and software integration program. This issue is exacerbated by well intended, but often uninformed, RFP’s that are written in a point solution fashion that tend to minimize the cross-functional repurposing of the utilities investment.

Our Approach

To address this challenge, we believe that a fresh perspective is needed in what can best be described as a “system-of-systems” (SOS) approach to remote sensing, analytics and software system integration. SOS begins by elevating the analysis to look within and across as many internal verticals as practicable including Asset Inspection, Vegetation Management, Right-of-Way protection, Facility Maintenance, Engineering, Compliance, Environmental, Legal etc.

We perform this analysis in conjunction with understanding the business drivers within each group, including internal and external constraints, while creating use cases for each specific vertical’s needs. From this set of use cases, a least-common-denominator (LCD) approach can be applied to evaluate the minimum level of frequency, timing and accuracies required for each business use case.

We believe that combining the SOS approach with the team of subject matter experts assembled by ECI will result in significant gains in operational efficiencies (either better optimization of spend or direct O&M savings) while maintaining or improving safety performance, reliability and compliance.

One of the overriding themes of ECI’s SOS methodology is to utilize a “collect once and leverage many times” approach wherever possible. This mental model makes sense on many levels, but in many cases requires cross-functional collaboration and even a new way of thinking and working within the organization. The significant difference is that our approach focuses on meeting least common denominator (LCD) outcomes across as many business use cases as practicable. This is a departure from traditional “silo” product specifications, which in many cases preclude cross-functional participation e.g. (a vegetation specification that does not require sufficient accuracy to be of value to the engineering group, or an imagery collection that is not high enough resolution to benefit the asset inspection group). We use the term “remote sensing” to encompass data capture, analytics, storage, hosting, software and delivery of products to field users.