This project tackled the problem of identifying power grid lines by applying GIS and machine learning. The team worked on data collection, preprocessing, modelling, deployment, and maintenance.

The project partner, SurplusMap, is a geo-intelligence SaaS platform that empowers decision-makers in the green transition to make better and faster decisions on where to develop new green industries.

The problem

The problem that SurplusMap is trying to solve is to identify and map the power grid from satellite images using machine learning. As seen in the images below, our objective has been to use satellite imagery and other geospatial data in conjunction with machine learning approaches to automatically identify power grid infrastructures such as pylons, substations, and power stations.

Powerstation images at different zoom levels and angles, Source: Omdena; SurplusMap

The use of satellite imagery is important because it can provide an accurate picture of the location of these infrastructures. This allows us to better understand where they are and what proportions of them there are, as well as their size. It also helps us determine whether there are any new power lines being built or if they have been damaged by natural disasters like hurricanes or earthquakes.

The project outcomes

Different object detection pre-trained models were applied. The team also custom-trained and tested various models with new test cases for improvement. The winning models were custom trained with the final data to detect pylons and stations. The model is successfully detecting the objects and will be improved by adding more data. An example visualization of the model can be seen in the image below.

Example visualization using object detection for power infrastructure, Source: Omdena; SurplusMap

India has a variety of geographical features and different climatic conditions in different regions. The project can be scaled up to be applied to other countries for green transition which has geographical features similar to India.