Introduction

Vegetation is a significant source of outages for many utilities. In some regions from one quarter to one half of all outages can be ascribed to vegetation. Vegetation encroachment initiated the massive North American Northeastern outage of 2003. With the growing incidence of extreme events managing vegetation near power lines is becoming increasingly crucial in tackling potential fire hazards and securing infrastructure increasing resilience. The traditional approach to identifying areas of high-risk vegetation encroachment involves helicopters and ground crews and is time consuming and costly. Drones have been used to reduce the cost of data collection, but it is still a costly process. 

Now an innovative startup called Spacept is using satellite imagery together with deep learning to identify high risk areas of vegetation encroachment. By applying machine learning algorithms to high resolution satellite imagery, it is possible to distinguish vegetation from other objects, differentiate trees from grass and bushes and identify trees at risk of encroachment. Comparing a drone-based approach with the satellite imagery methodology reveals that the latter approach is highly accurate in identifying high risk areas and significantly less costly than a drone-based process.

Background

Power line inspections for vegetation management and other purposes are essential in ensuring grid reliability and resilience. They are generally performed by manned helicopters or by a ground crew. Data is collected with cameras and analyzed to detect diseased trees that could fall and hit a power line (fall ins) or trees that are encroaching on power lines (grow ins). These inspections are mandated by NERC in North America and are not optional. Beyond visual line of sight (BVLOS) drones have reduced the cost of collecting line inspection data. Now a new company is reducing the cost of identifying trees at risk by analyzing high resolution satellite imagery to identify high-risk areas, thus reducing the total length of power line that requires more detailed inspection with drones, helicopters, or ground crews.

Evolving machine learning models at Spacept

A Swedish company Spacept has developed a vegetation management solution that applies machine learning models to multi-band satellite imagery to automatically identify areas of high risk for vegetation encroachment on above-ground transmission and distribution lines anywhere on the planet. Spacept develops its own in-house training data for machine learning models which enables follow-up ground inspections of areas of high risk to develop pruning and other response programs. The training data is constantly enhanced with feedback from new projects for continuous improvement of the models.

Satellite-based vegetation encroachment risk assessment

The key business benefit of Spacept’s satellite imagery-based solution is automating the process for identifying the high-risk areas that require ground-based inspection, whether helicopter, drone-based, or ground crew, thus significantly lowering the cost of vegetation encroachment inspections. Spacept’s satellite-based vegetation encroachment technology makes it possible for power grid operators to monitor vegetation encroachment over their entire infrastructure in a cost-effective manner. Spacept uses the most recent, highest resolution (0.50 – 0.30 cm) satellite imagery commercially available which satisfies the requirements for vegetation encroachment detection. Spacept’s approach makes it possible to monitor hundreds of thousands of kilometers of power lines every year anywhere in the world.

Spacept’s vegetation management platform

Workflow

The workflow of identifying and downloading satellite imagery, applying machine learning for feature identification, and the assignment of risk categories to individual trees is currently a semi-automated process. The longer-term objective is to reduce manual intervention and achieve an end-to-end automated workflow. The current workflow proceeds from a geographical layout of the power line to be inspected in the form of a standard GIS file.

The most recent available high resolution satellite images satisfying certain criteria are identified and downloaded from data providers. The criteria typically include panchromatic and multi-spectral bands, resolution of at least 50 cm, vegetation capture with leaf on, and minimal cloud coverage. The downloaded images are fed to the current vegetation model which automatically extracts features and distinguishes trees from other man-made and natural objects. One of the important capabilities of the model is that it is very efficient in differentiating trees from bushes.

After the automated processing, the imagery and labelling are reviewed by experienced assessors who review each image and make corrections to the labelling for problem areas, for example, for cases of visually distinguishable bushes or edges of trees. After completion of the labelling step, the distance between each tree and the power line is calculated and a risk category is assigned using risk categories provided by the client. The final deliverable is a GIS file containing the location and the corresponding risk categories assigned to the trees.

In-field case study

To assess the potential business benefits of satellite-based analysis Spacept partnered with infrastructure health management platform Sterblue to apply their satellite-based power line vegetation management solution for thousands of kilometres of a French power grid operator grid. Spacept applied their algorithms to selected satellite imagery to generate a risk file for the targeted segment of the grid. Sterblue assigned emergency (risk) levels based on the distance from the power line using a classification of emergency levels provided by the grid operator. Sterblue then carried out in detail inspections of at-risk areas with its drone inspection fleet. A ground inspection team was sent to verify the locations of the trees physically and collect ground truth. A total of 283 locations were identified. The vegetation at each location was manually validated and was compared with the satellite predictions.

The most important conclusion on the business benefit of the satellite imagery-based analysis is that it was able to identify all the trees that the subsequent ground inspection identified as at risk. In a real-world scenario, applying the satellite imagery analysis as a first step in a power line inspection would dramatically reduce the area and trees that would require ground-based inspection using helicopter or drone-based inspection. As a result of this process, Sterblue reduced its operating costs by 80%.

A more detailed analysis of the satellite data attempted to classify trees into detailed emergency levels U0 to U3. Comparing this analysis with Sterblue’s classification was used to create a “confusion matrix” showing instances of agreement and disagreement between the two analyses. It was found that there was good agreement between the satellite and ground-based inspections and analyses.

Conclusion

Several companies have recognized that earth observation imagery is of limited value without the application of analytics to interpret the data. For example, Spacept is applying analytics to the huge amount of earth observation data to provide a valuable high risk vegetation encroachment detection service that is safer, faster, and less costly than ground-based approaches to transmission and distribution line vegetation monitoring.